A name is an example of personally identifiable information (PII), which is any data that can be used to identify someone, either on its own or with other relevant data. A name is a direct identifier, which means that it can uniquely identify a person without the need for any additional information. For example, a full name, such as John Smith, can be used to distinguish or trace an individual’s identity1.

Other examples of direct identifiers include:

Social Security Number

Passport number

Driver’s license number

Email address

Phone number

A summary with statistics, conclusions, and recommendations from the data analyst is the best way to communicate the results of an online marketing campaign to the marketing manager. A summary can provide a concise and clear overview of the most important KPIs and measure the return on marketing investment, as well as highlight the main findings and insights from the data analysis. A summary can also include actionable suggestions and best practices for improving the campaign performance and achieving the marketing objectives. A summary is different from other options, such as a real-time monitor, a self-service dashboard, or a spreadsheet of raw data, which may not provide enough context, interpretation, or guidance for the manager. Therefore, the correct answer is D. References: How to Write a Data Analysis Report: 6 Essential Tips, How to Write a Marketing Report (with Pictures) - wikiHow

The table provided shows an inconsistency in the ‘Gender’ column, which lists three distinct values: Male, Female, and College. This is inconsistent because ‘College’ is not a gender category. The ‘Gender’ column should only have two distinct values, typically ‘Male’ and ‘Female’, to accurately represent gender data. This error could be due to a data entry mistake or a misclassification during data collection.

In data analysis, it’s crucial to ensure that categorical variables like gender are consistent and correctly classified, as this can significantly impact the analysis results. Data cleaning processes often involve identifying and correcting such inconsistencies to maintain the integrity of the data set.

References:

Data quality management principles emphasize the importance of consistency in data values, especially for categorical variables like gender1.

Best practices in data cleaning include checking for and rectifying inconsistencies or misclassifications in data sets2.

The importance of accurate data classification is highlighted in data analysis literature, as it directly affects the validity of the analysis results3.

This is because an IF function is a type of logical function that returns a value based on a condition or a set of conditions. An IF function can be used to manipulate data by applying different actions or calculations depending on whether the condition is true or false. For example, an IF function in Excel that can achieve this is:

=IF (condition, value_if_true, value_if_false)

The other data manipulation techniques are not examples of logical functions. Here is why:

WHERE is a type of clause that filters data based on a condition or a set of conditions. A WHERE clause can be used to manipulate data by selecting only the rows that satisfy the condition(s). For example, a WHERE clause in SQL that can achieve this is:

AGGREGATE is a type of function that performs a calculation on a group of values, such as sum, average, count, etc. An AGGREGATE function can be used to manipulate data by summarizing or aggregating the values in a column or a table. For example, an AGGREGATE function in SQL that can achieve this is:

BOOLEAN is a type of data type that represents two possible values: true or false. A BOOLEAN data type can be used to manipulate data by storing or returning logical values based on a condition or a set of conditions. For example, a BOOLEAN data type in Python that can achieve this is:

This is because percent change is a type of descriptive statistic that measures the relative change or difference of a variable over time, such as the sugar content of cereal over years in this case. Percent change can be used to determine whether the sugar content of cereal has increased over years by comparing the initial and final values of the sugar content, as well as calculating the ratio or proportion of the change. For example, percent change can be used to determine whether the sugar content of cereal has increased over years by finding out how much more (or less) sugar there is in cereal now than before, as well as expressing it as a fraction or a percentage of the original sugar content. The other descriptive statistics are not appropriate to use to determine whether the sugar content of cereal has increased over years. Here is why:

Frequency is a type of descriptive statistic that measures how often or how likely a value or an event occurs in a data set, such as how many times a certain sugar content appears in cereal in this case. Frequency does not measure the relative change or difference of a variable over time, but rather measures the occurrence or chance of a variable at a given time.

Variance is a type of descriptive statistic that measures how much the values in a data set vary or deviate from the mean or average of the data set, such as how much variation there is in sugar content among different cereals in this case. Variance does not measure the relative change or difference of a variable over time, but rather measures the dispersion or spread of a variable at a given time.

Mean is a type of descriptive statistic that measures the average value or central tendency of a data set, such as what is the typical sugar content of cereal in this case. Mean does not measure the relative change or difference of a variable over time, but rather measures the summary or representation of a variable at a given time.

This is because exploratory data analysis is a type of process that performs initial investigations on data to spot outliers, discover patterns, and test assumptions with statistical insight and graphical visualization, such as box plots, histograms, scatter plots, etc. Exploratory data analysis can be used to understand and summarize the data, as well as to generate hypotheses or questions for further analysis or research. For example, exploratory data analysis can be used to identify and visualize the characteristics, features, or behaviors of the data, as well as to measure their distribution, frequency, or correlation. The other options are not types of processes that perform initial investigations on data to spot outliers, discover patterns, and test assumptions with statistical insight and graphical visualization. Here is what they mean:

A t-test is a type of statistical method that tests whether there is a significant difference between the means of two groups or samples, such as whether there is a difference between the average exam scores of two classes in this case. A t-test can be used to test or verify a claim or an assumption about the data, as well as to measure the confidence or the error of the estimation.

A performance analysis is a type of process that measures whether the data meets certain goals or objectives, such as targets, benchmarks, or standards. A performance analysis can be used to identify and visualize the gaps, deviations, or variations in the data, as well as to measure the efficiency, effectiveness, or quality of the outcomes. For example, a performance analysis can be used to determine if there is a gap between a student’s test score and their expected score based on their previous performance.

A link analysis is a type of process that determines whether the data is connected to other datapoints, such as entities, events, or relationships. A link analysis can be used to identify and visualize the patterns, networks, or associations among the datapoints, as well as to measure the strength, direction, or frequency of the connections. For example, a link analysis can be used to determine if there is a connection between a customer’s purchase history and their loyalty program status.

Multicollinearity in a dataset refers to the situation where two or more predictor variables are highly correlated, meaning that one can be linearly predicted from the others with a substantial degree of accuracy. In such cases, the correlation coefficient is a key statistical measure used to identify the presence of multicollinearity. It quantifies the degree to which two variables are linearly related.

The Variance Inflation Factor (VIF) is another commonly used metric that is derived from the correlation coefficient. It assesses how much the variance of an estimated regression coefficient increases if your predictors are correlated. If no factors are correlated, the VIFs will all be equal to 1.

While the other options listed—Chi-squared test, Two-sample f-test, and Two-way ANOVA—are valuable statistical tools, they serve different purposes and are not typically used to detect multicollinearity. The Chi-squared test is used for testing relationships between categorical variables, the Two-sample f-test compares variances across groups, and Two-way ANOVA is used to understand the interaction between two independent categorical variables on a continuous dependent variable.

References:

Multicollinearity in Regression Analysis: Problems, Detection, and Solutions1.

What is multicollinearity and how to remove it?2.

Detect and Treat Multicollinearity in Regression with Python3.

Filter on any of the responses that do not say “January” and update them to “January”. This is because filtering and updating are data cleansing techniques that can be used to ensure data consistency, which means that the data is uniform and follows a standard format. By filtering on any of the responses that do not say “January” and updating them to “January”, the analyst can make sure that all the responses for the month of January are written in the same way. The other steps are not appropriate for ensuring data consistency. Here is why:

Deleting any of the responses that do not have “January” written out would result in data loss, which means that some information would be missing from the data set. This could affect the accuracy and reliability of the analysis.

Replacing any of the responses that have “01” would not solve the problem of data inconsistency, because there would still be two different ways of writing the month of January: “Jan” and “January”. This could cause confusion and errors in the analysis.

Sorting any of the responses that say “Jan” and updating them to “01” would also not solve the problem of data inconsistency, because there would still be two different ways of writing the month of January: “01” and “January”. This could also cause confusion and errors in the analysis.

The value that will appear first if they are sorted in descending order is Xavier. Descending order means arranging values from the largest to the smallest, or from the last to the first in alphabetical order. In this case, Xavier is the last name in alphabetical order, so it will appear first when sorted in descending order. The other names will appear in the following order: Molly, Adam, Aaron. Reference: Sorting Data - W3Schools

 Alphanumeric values are values that contain both letters and numbers, such as A3J7. The other options are numeric values, as they contain only numbers, such as 10.1E2, 13.6, and 1347. Reference: Guide to CompTIA Data+ and Practice Questions - Pass Your Cert

The correct join the analyst should complete is B. LEFT JOIN, four rows.

A LEFT JOIN is a type of SQL join that returns all the rows from the left table, and the matched rows from the right table. If there is no match, the right table will have null values. A LEFT JOIN is useful when we want to preserve the data from the left table, even if there is no corresponding data in the right table1

Using the example tables, a LEFT JOIN query would look like this:

SELECT t1.Name, t1.City, t2.Age FROM Table1 t1 LEFT JOIN Table2 t2 ON t1.Name = t2.Name;

The result of this query would be:

Name City Age Jane Smith Detroit NULL John Smith Dallas 34 Candace Johnson Atlanta 45 Kyle Jacobs Chicago 39

As you can see, the query returns four rows, one for each name in Table1. The name John Smith appears twice in Table2, but only one of them is matched with the name in Table1. The name Jane Smith does not appear in Table2, so the age column has a null value for that row.

 A Type II error is a false negative conclusion, which means failing to reject a null hypothesis that is actually false. In the table, box 3 indicates that a Type II error has occurred, because it shows that the null hypothesis is accepted when it is false in reality. This means that the statistical test failed to detect a significant difference or relationship that actually exists. References: Type I & Type II Errors | Differences, Examples, Visualizations - Scribbr, Type I and type II errors - Wikipedia

When creating a dashboard to identify trends in data sets, the most appropriate consideration is the data sources and attributes. This is because the quality, reliability, and structure of the data sources directly influence the dashboard’s ability to accurately reflect trends. Attributes, such as the type of data and the time frame it covers, are crucial for trend analysis. A well-designed dashboard should provide a clear and intuitive representation of the data, allowing for easy identification of trends and patterns.

Frequently asked questions (B) can inform the design of the dashboard but are not a direct consideration for the development process itself. A report from the data source © might be an output of the dashboard but does not guide its development. A comparison of data sets (D) could be a feature of the dashboard, but the underlying data sources and attributes must be considered first to ensure accurate and meaningful comparisons.

References:

Best practices in dashboard design emphasize the importance of understanding and consolidating different data sources and creating a mix of useful metrics, which aligns with the choice of data sources and attributes1.

Fundamental dashboard design principles include the clear and efficient display of information, which is dependent on the proper selection and use of data sources and attributes2.

Effective dashboard communication is achieved by using colors, shapes, sizes, labels, and legends meaningfully, all of which rely on the underlying data sources and attributes3.

Explanation

Microsoft Excel.

This scenario presents a very simple use case where the business leader needs a dataset in an easy-to-access form and will not be performing any detailed analysis.

A simple spreadsheet, such as Microsoft Excel, would be the best tool for this job.

There is no need to use a statistical analysis package, such as SAS or Minitab, as this would likely confuse the VP without adding any value. The same is true of an integrated analytics suite, such as Power BI.

Explanation

Duplicate Data.

While invalid, redundant, or missing data are all valid concerns, data about people exists in two of the six systems. As such, Jhon needs to account for duplicate data issues.

The date parameters that the analyst should use are Q3 2019 vs. Q3 2020, as this will allow the analyst to compare the sales performance of the Q3 2020 promotion with the same period of last year. This will help to eliminate any seasonal or cyclical effects that might affect the sales data. The other options are not relevant for this purpose, as they either compare different quarters or different years. Reference: CertMaster Practice for Data+ Exam Prep - CompTIA

This is because a chi-squared test is a type of statistical method that tests the association or independence between two or more categorical variables, such as gender, race, or occupation. A chi-squared test can be used to compare the observed frequencies of the categories with the expected frequencies under the null hypothesis of no association or independence. For example, a chi-squared test can be used to determine if there is a relationship between smoking and lung cancer. The other statistical methods do not require two or more categorical variables. Here is why:

Simple linear regression is a type of statistical method that models the relationship between a continuous dependent variable and a continuous or categorical independent variable, such as height, weight, or education level. A simple linear regression can be used to estimate the slope and intercept of the best-fitting line that describes how the dependent variable changes with the independent variable. For example, a simple linear regression can be used to predict the weight of a person based on their height.

Z-test is a type of statistical method that tests the significance of the difference between a sample mean and a population mean, or between two sample means, when the population standard deviation or the sample sizes are large enough. A z-test can be used to compare the average scores of two groups of students on a standardized test.

Two-sample t-test is a type of statistical method that tests the significance of the difference between two sample means when the population standard deviation is unknown or the sample sizes are small. A two-sample t-test can be used to compare the average salaries of two groups of employees in different departments.

This is because the price of the Rose unit can be estimated using the average cost per square foot of the original floor plans, which are Jasmine, Orchid, Azalea, and Tulip. To find the average cost per square foot of the original floor plans, we can use the following formula:

Plugging in the values from the original floor plans, we get:

To find the price of the Rose unit, we can use the following formula:

Plugging in the values from the Rose unit, we get:

Therefore, the price of the Rose unit should be $705,200, using the average cost per square foot of the original floor plans.

A static report is a type of report that shows a snapshot of data at a specific point in time. A static report does not change or update automatically, unless the data source is refreshed or the report is regenerated. A static report is suitable for situations where the data does not change frequently or where historical data is needed for comparison or analysis. In this case, the data analyst is asked to create a sales report for the second-quarter 2020 board meeting, which will include a review of the business’s performance through the second quarter. The board meeting will be held on July 15, 2020, after the numbers are finalized. This means that the data analyst does not need to show real-time or dynamic data, but rather a fixed and accurate view of the sales data for the second quarter. Therefore, a static report would be the best way to meet this stakeholder requirement. Therefore, the correct answer is A. References: What are Static Reports? | Sisense, Static vs Dynamic Reports - What’s The Difference? | datapine

Explanation

The best answer is to use a watermark to identify the report as a draft. A watermark is a faint image or text that appears behind the content of a document, indicating its status or ownership. By using a watermark, Kelly can clearly communicate that the report is not final and still subject to changes or feedback. This can prevent confusion among the readers and avoid any misuse or misinterpretation of the report. The other options are not as effective as using a watermark, as they either do not indicate the status of the report or do not reach the appropriate stakeholders. Distributing the report via email or publishing it on an internally facing website may not make it clear that the report is a draft and may cause confusion or errors. Showing the report to her immediate supervisor may not get enough feedback from other relevant stakeholders who may have different perspectives or insights. Reference: How to Add a Watermark in Microsoft Word - Lifewire

Stratified sampling is a data sampling method that involves dividing a population into subgroups by similar characteristics, such as age, gender, income, etc. Then, a simple random sample is drawn from each subgroup. This method ensures that each subgroup is adequately represented in the sample and reduces the sampling error. References: CompTIA Data+ Certification Exam Objectives, page 11.

The SQL query provided is selecting all records from the Cust_table where the age column has values greater than 60 and the City column matches “New York”. The > operator selects values that are strictly greater than the comparison value, so it does not include customers aged exactly 60. The term “New York” in the context of a city database typically refers to New York City, not the state of New York. Therefore, the correct answer is that the analyst is retrieving data for all customers older than age 60 in New York City.

References:

The use of the > operator in SQL is to select values greater than the specified value1.

Understanding the WHERE clause in SQL and its use in filtering records based on specified conditions2.

Clarification on the distinction between city and state names in database records3.

The invalid data type is the best description for the issue in which character values are mixed with integer values in a data set column. Invalid data type means that the data does not match the expected or required format or structure for a given variable or attribute. For example, if a column is supposed to store numerical values, but some rows contain text values, then those rows have an invalid data type. References: CompTIA Data+ Certification Exam Objectives, page 10

After conducting a review of the business questions, the next step for the analyst is to determine the data needs and sources for analysis. This involves identifying the relevant data elements, variables, and metrics that are required to answer the business questions, as well as the data sources, formats, and quality that are available to access and use. This step will help the analyst to plan the data collection, preparation, and integration processes, as well as to assess the feasibility and limitations of the analysis1.

Missing data is a type of data quality issue that occurs when some values in a data set are not recorded or available. Missing data can affect the validity and reliability of survey results, especially if the missing values are not random or ignorable. Missing data can also reduce the sample size and the statistical power of the analysis12

If 100% participation is needed on survey results, a data analyst would look for missing data first, because missing data would indicate that some participants did not complete or submit the survey, or that some responses were not recorded or transmitted correctly. A data analyst would need to identify the causes and patterns of missing data, and apply appropriate methods to handle or prevent missing data, such as imputation, deletion, weighting, or follow-up12

Strategy 4 provides the best sales in comparison to other strategies. This is because the total sales for Strategy 4 are the highest among all the strategies, as shown by the black line. The other statements are not accurate or do not uphold integrity in data reporting. Here is why:

Statement A is false because sales are not approximately equal for Product A and Product B across all strategies. For example, in Strategy 1, Product A has more sales than Product B, while in Strategy 3, Product B has more sales than Product A.

Statement C is misleading because it does not account for the difference in scale between the products. While Strategy 2 has the highest total sales among all products, it does not necessarily mean that it is the most effective for each product. For instance, Product D has very low sales in Strategy 2 compared to other strategies.

Statement D is biased because it does not provide any evidence or justification for why Product D should be promoted more than the other products in all strategies. It also ignores the fact that Product D has the lowest sales among all products in most of the strategies.

his is because median is a type of statistical measure that represents the typical value or central tendency of a data set, which means that it divides the data set into two equal halves, such that half of the values are above it and half are below it. Median can be used to provide a measure that represents the typical income of suburban families in the United States, especially when the data set has a lot of outliers, which means that it has values that are unusually high or low compared to the rest of the data set. Median can provide a measure that represents the typical income of suburban families in the United States, because it is not affected or skewed by the outliers, as it only depends on the middle value or the middle two values of the data set, regardless of how extreme or distant the outliers are. For example, median can provide a measure that represents the typical income of suburban families in the United States, by finding the income value that splits the data set into two equal groups of families, such that 50% of the families have higher incomes and 50% have lower incomes. The other statistical measures are not the best measures to represent the typical income of suburban families in the United States. Here is why:

Mean is a type of statistical measure that represents the average value or central tendency of a data set, which means that it is the sum of all the values divided by the number of values. Mean is not a good measure to represent the typical income of suburban families in the United States, especially when the data set has a lot of outliers, because it is affected or skewed by the outliers, as it takes into account all the values in the data set, regardless of how extreme or distant they are. For example, mean can provide a measure that does not represent the typical income of suburban families in the United States, by finding the income value that is influenced by a few very high or very low incomes, which could make it higher or lower than most of the incomes in the data set.

Mode is a type of statistical measure that represents the most frequent value or mode of a data set, which means that it is the value that occurs most often in the data set. Mode is not a good measure to represent the typical income of suburban families in the United States, especially when the data set has a lot of outliers, because it is not representative or indicative of the central tendency or distribution of the data set, as it only depends on the count or occurrence of a single value or a few values in the data set, regardless of how common or rare they are. For example, mode can provide a measure that does not represent the typical income of suburban families in the United States, by finding the income value that is repeated more often than others, which could be an outlier or an anomaly in the data set.

Standard deviation is a type of statistical measure that represents the amount of dispersion or variation of a data set, which means that it quantifies how much the values in a data set vary or deviate from the mean or average of the data set. Standard deviation is not a measure that represents the typical income of suburban families in the United States, but rather a measure that describes the spread or distribution of their incomes, as well as identifies any outliers or extreme values in their incomes. For example, standard deviation can provide a measure that describes how diverse or homogeneous their incomes are, as well as how far their incomes are from their average income.

The correct answer is D. $702,500.

To find the price of the Rose unit, we need to use the average cost per square foot of the original floor plans. The average cost per square foot is calculated by dividing the price by the square footage of each unit type. Using the data from the table, we can do the following:

Jasmine: $345,000 / 1,000 = $345 per square foot

Orchid: $525,000 / 2,000 = $262.5 per square foot

Azalea: $375,000 / 1,500 = $250 per square foot

Tulip: $450,000 / 1,800 = $250 per square foot

The average cost per square foot of the original floor plans is the mean of these four values, which is ($345 + $262.5 + $250 + $250) / 4 = $276.875 per square foot.

To find the price of the Rose unit, we need to multiply the average cost per square foot by the square footage of the Rose unit. The Rose unit has a square footage of 2,535, according to the table. Therefore, the price of the Rose unit is $276.875 x 2,535 = $702,421.875.

Rounding to the nearest whole number, we get $702,500 as the price of the Rose unit.

Getting stakeholder approval is the next step the analyst should take in the dashboard creation process, after confirming the data sources and creating a wireframe. Stakeholder approval means getting feedback and validation from the intended users or clients of the dashboard, to ensure that it meets their expectations and requirements. This step helps to avoid rework and ensure customer satisfaction. References: CompTIA Data+ Certification Exam Objectives, page 14

The data is presented in a CSV (comma-separated values) file format, which is a plain text format that stores tabular data. Each line of the file is a data record, and each record consists of one or more fields separated by commas. The first line of the file usually contains the names of the fields, also known as the header. In this case, the data has four fields: Name, Age, Gender, and Occupation. Therefore, the correct answer is B. References: CSV File (What It Is & How to Open One), Comma-separated values - Wikipedia

The null hypothesis should be accepted when the p-value is greater than the alpha level, which is the significance level of the test. The p-value is the probability of obtaining a test statistic at least as extreme as the one observed in the sample, assuming that the null hypothesis is true. The alpha level is the probability of rejecting the null hypothesis when it is true, which is also known as a type I error12.

In this case, the alpha level is 0.05, which means that there is a 5% chance of rejecting the null hypothesis when it is true. Therefore, to reject the null hypothesis, the p-value must be less than or equal to 0.05, which indicates that the test statistic is very unlikely to occur by chance under the null hypothesis. Conversely, to accept the null hypothesis, the p-value must be greater than 0.05, which indicates that the test statistic is not very unlikely to occur by chance under the null hypothesis.

Among the four options, only option D has a p-value that is greater than 0.05 (p = 0.06). Therefore, option D is the correct answer. When p = 0.06, it means that there is a 6% chance of obtaining a test statistic at least as extreme as the one observed in the sample, assuming that the null hypothesis is true. This probability is not very low, and therefore does not provide enough evidence to reject the null hypothesis.

Sampling refers to the process of selecting a representative subset of data from a larger data set or repository. This technique is used when it is impractical or unnecessary to analyze the entire set of data. A representative sample should accurately reflect the characteristics of the larger population, allowing for analysis and inference about the population as a whole12.

Observation (A) generally refers to the act of monitoring or recording data. Delta load (B) is a term used in data warehousing to describe the process of loading only the changes since the last data extraction, rather than the entire data set. Web scraping © is the process of extracting data from websites.

References:

Understanding the importance of data sampling1.

The concept of a representative sample in statistics2.

Data repository management and usage3.

Benefits and methods of data sampling4.

This is because stratified is a type of sampling in which elements of data are selected randomly from each of the small subgroups within a population, such as age groups, gender groups, or income groups. Stratified sampling can be used to ensure that the sample is representative and proportional of the population, as well as reduce the sampling error or bias. For example, stratified sampling can be used to select a sample of voters from different political parties based on their proportion in the population. The other types of sampling are not the types of sampling in which elements of data are selected randomly from each of the small subgroups within a population. Here is why:

Simple random is a type of sampling in which elements of data are selected randomly from the entire population, without dividing it into any subgroups. Simple random sampling can be used to ensure that every element in the population has an equal chance of being selected, as well as avoid any systematic error or bias. For example, simple random sampling can be used to select a sample of students from a school by using a lottery or a computer-generated number.

Cluster is a type of sampling in which elements of data are selected randomly from a few large subgroups within a population, such as regions, districts, or schools. Cluster sampling can be used to reduce the cost and complexity of sampling, as well as increase the feasibility and convenience of sampling. For example, cluster sampling can be used to select a sample of households from a few neighborhoods by using a map or a list.

Systematic is a type of sampling in which elements of data are selected at regular intervals from an ordered list or sequence within a population, such as every nth element or every kth element. Systematic sampling can be used to simplify and speed up the sampling process, as well as ensure that the sample covers the entire range or scope of the population. For example, systematic sampling can be used to select a sample of books from a library by using an alphabetical order or a numerical order.

Explanation

Alfonso had the highest score. A left skewed distribution is a distribution where the tail is longer on the left side than on the right side, meaning that most of the values are clustered on the right side and there are some outliers on the left side. In a left skewed distribution, the mean is less than the median, which is less than the mode. Therefore, Joseph, who scored at the mean, had the lowest score, Gaby, who scored at the end of the tail, had the second lowest score, and Alfonso, who scored at the median, had the highest score. Reference: Skewness - Statistics How To

A star schema is a type of database schema that consists of one fact table and multiple dimension tables. The fact table contains the measures or metrics of the business process, such as sales, orders, or transactions. The dimension tables contain the attributes or characteristics of the business entities, such as products, customers, or locations. The fact table is connected to the dimension tables by foreign keys that reference the primary keys of the dimension tables. The fact table is located at the center of the schema, while the dimension tables are located at the edges, forming a star-like shape1.

A star schema is an example of a denormalized schema, which means that the dimension tables are not normalized and may contain redundant or repeated data. This is done to improve the performance and simplicity of queries, as there are fewer joins and tables involved. A star schema is suitable for data warehouses and business intelligence applications that require fast and efficient data retrieval2.

 A discrete variable is a variable that can only take on a finite number of values, such as integers or categories. The number of people in an office is an example of a discrete variable, as it can only be a whole number. The temperature of a hot tub, the height of a horse, and the time to complete a task are examples of continuous variables, as they can take on any value within a range. Reference: CompTIA Data+ (DA0-001) Practice Certification Exams | Udemy

The best answer is B. As a sample size grows, its mean gets closer to the average of the whole population.

The law of large numbers, in probability and statistics, states that as a sample size grows, its mean gets closer to the average of the whole population. This is due to the sample being more representative of the population as it increases in size. The law of large numbers guarantees stable long-term results for the averages of some random events1

A. As a sample size decreases, its standard deviation gets closer to the average of the whole population is not correct, because it confuses the concepts of standard deviation and mean. Standard deviation is a measure of how much the values in a data set vary from the mean, not how close the mean is to the population average. Also, as a sample size decreases, its standard deviation tends to increase, not decrease, because the sample becomes less representative of the population.

C. As a sample size decreases, its mean gets closer to the average of the whole population is not correct, because it contradicts the law of large numbers. As a sample size decreases, its mean tends to deviate from the average of the whole population, because the sample becomes less representative of the population.

D. When a sample size doubles, the sample is indicative of the whole population is not correct, because it does not specify how close the sample mean is to the population average. Doubling the sample size does not necessarily make the sample indicative of the whole population, unless the sample size is large enough to begin with. The law of large numbers does not state a specific number or proportion of samples that are indicative of the whole population, but rather describes how the sample mean approaches the population average as the sample size increases indefinitely.

The mean height for the five dogs is calculated by adding up all the heights and dividing by the number of dogs. The formula is:

mean = (300 + 430 + 170 + 470 + 600) / 5 mean = 1970 / 5 mean = 394

Therefore, option A is correct.

Option B is incorrect because it is the median height, which is the middle value when the heights are arranged in ascending order.

Option C is incorrect because it is the mean height multiplied by 1.25.

Option D is incorrect because it is the mean height multiplied by 1.28.

The correct answer is B. Snowflake.

A snowflake schema is a type of database schema that features normalized dimension tables. A database schema is a way of organizing and structuring the data in a database. A dimension table is a table that contains descriptive attributes or characteristics of the data, such as product name, category, color, etc. A normalized table is a table that follows the rules of normalization, which is a process of reducing data redundancy and improving data integrity by organizing the data into smaller and simpler tables12

A snowflake schema is a variation of the star schema, which is another type of database schema that features denormalized dimension tables. A denormalized table is a table that does not follow the rules of normalization, and may contain redundant or duplicated data. A star schema consists of a central fact table that contains quantitative measures or facts, such as sales amount, order quantity, etc., and several dimension tables that are directly connected to the fact table. A snowflake schema differs from a star schema in that the dimension tables are further split into sub-dimension tables, creating a snowflake-like shape13

A snowflake schema has some advantages and disadvantages over a star schema. Some advantages are:

It reduces the storage space required for the dimension tables, as it eliminates the redundant data.

It improves the data quality and consistency, as it avoids the update anomalies that may occur in denormalized tables.

It allows more detailed analysis and queries, as it provides more levels of dimensions.

Some disadvantages are:

It increases the complexity and number of joins required to retrieve the data from multiple tables, which may affect the query performance and speed.

It reduces the readability and simplicity of the schema, as it has more tables and relationships to understand.

It may require more maintenance and administration, as it has more tables to manage and update13

This is because inconsistency is a type of data quality issue that occurs when the data does not follow a common format, structure, or rule across different sources or systems, which can affect the efficiency and performance of the analysis or process. Inconsistency can be caused by having different spellings, punctuations, capitalizations, or abbreviations for the same or similar values in a data set, such as “M”, “m”, “Male”, or “male” for gender in this case. Inconsistency can be eliminated or reduced by using data cleansing techniques, such as standardizing or normalizing the data values. The other options are not correct descriptions of the data set. Here is why:

Data bias is a type of data quality issue that occurs when the data is not representative or proportional of the population or the parameter, which can affect the validity and reliability of the analysis or process. Data bias can be caused by having a sample that is too small, too large, or too skewed for the population or the parameter, such as having only male customers for a product that targets both genders in this case. Data bias can be eliminated or reduced by using sampling techniques, such as stratified or cluster sampling.

The data is incomplete is a type of data quality issue that occurs when the data is absent or missing in a data set, which can affect the accuracy and reliability of the analysis or process. The data is incomplete can be caused by various factors, such as human error, system error, or non-response. The data is incomplete can be addressed by using various methods, such as replacing or imputing the missing values with some reasonable estimates, such as mean, median, mode, or regression.

The data is outliers is a type of data quality issue that occurs when the data has values that are unusually high or low compared to the rest of the data set, which can affect the quality and validity of the analysis or process. The data is outliers can be caused by various factors, such as measurement error, natural variation, or extreme events. The data is outliers can be addressed by using various methods, such as removing or filtering out the outliers, or using robust statistics that are less sensitive to outliers, such as median, interquartile range, or box plot.

Answer: C. 2019 goal data

To report how the sales team is performing to target, the sales analyst needs to compare the actual sales revenue with the expected or planned sales revenue for the same period. The 2019 goal data is the file that contains the expected or planned sales revenue for the year 2019, which is the target that the sales team is aiming to achieve. By comparing the 2019 goal data with the 2019 actual revenue, the sales analyst can calculate the performance attainment, which is the percentage of the goal that was met by the sales team.

Option A is incorrect, as 2018 goal data is not relevant for determining 2019 performance attainment. The 2018 goal data contains the expected or planned sales revenue for the year 2018, which is not the target that the sales team is aiming to achieve in 2019.

Option B is incorrect, as 2018 actual revenue is not relevant for determining 2019 performance attainment. The 2018 actual revenue contains the actual sales revenue for the year 2018, which is not comparable with the 2019 goal data or the 2019 actual revenue.

Option D is incorrect, as 2019 commission plan is not relevant for determining 2019 performance attainment. The 2019 commission plan contains the rules and rates for calculating and paying commissions to the sales team based on their performance attainment, but it does not contain the expected or planned sales revenue for the year 2019.

 A password-protected dashboard is a type of web-based application that can display the health scorecards of the troops in a secure and interactive way. A password-protected dashboard can provide the following benefits for the commander:

It can protect the PHI data from unauthorized access or disclosure by requiring a valid username and password to log in. This can ensure that only the commander and other authorized personnel can view the information12

It can allow the commander to filter the data based on gender and rank by using drop-down menus, sliders, checkboxes, or other controls. This can enable the commander to customize the view and focus on the relevant data13

It can update the data daily by connecting to a data source that refreshes automatically or on demand. This can ensure that the commander always sees the latest and most accurate information14

It can present the data in a visual and intuitive way by using charts, graphs, tables, or other elements. This can help the commander to understand and analyze the data more easily and effectively1

The HTML commands that would help the developer to complete the task successfully are

Upload Your Short Videos

tag defines a heading level 1, which is the largest and most important heading on a webpage. The tag defines a clickable button that can perform some action when clicked. The other options are not suitable for the task, as they either use the wrong tags or do not create a clickable button. The tag defines a section of text with no specific meaning or formatting. The

tag defines a paragraph of text. The tag does not exist in HTML. Reference: HTML Tags - W3Schools

A scatter chart using sales volume and average sales per customer is the best type of chart to include in the dashboard. A scatter chart is a type of chart that displays the relationship between two numerical variables using dots or markers. A scatter chart can show how one variable affects another, how strong the correlation is between them, and how the data points are distributed. In this case, a scatter chart can show the story of sales and determine which site is providing the highest sales volume per customer by plotting the sales volume on the x-axis and the average sales per customer on the y-axis. Each dot on the chart will represent a site, and the analyst can easily compare the sites based on their position on the chart. A site with a high sales volume and a high average sales per customer will be in the upper right quadrant, indicating a high performance. A site with a low sales volume and a low average sales per customer will be in the lower left quadrant, indicating a low performance. A site with a high sales volume and a low average sales per customer will be in the lower right quadrant, indicating a high volume but low value. A site with a low sales volume and a high average sales per customer will be in the upper left quadrant, indicating a low volume but high value. A scatter chart can also show if there is a positive or negative correlation between the two variables, or if there is no correlation at all. A positive correlation means that as one variable increases, so does the other. A negative correlation means that as one variable increases, the other decreases. No correlation means that there is no relationship between the two variables.

The other types of charts are not as suitable for this purpose. A line chart is a type of chart that displays the change of one or more variables over time using lines. A line chart can show trends, patterns, and fluctuations in the data. However, in this case, there is no time variable involved, so a line chart would not be appropriate. A pie chart is a type of chart that displays the proportion of each category in a whole using slices of a circle. A pie chart can show how each category contributes to the total and compare the relative sizes of each category. However, in this case, there are two numerical variables involved, so a pie chart would not be able to show their relationship. A column chart is a type of chart that displays the comparison of one or more variables across categories using vertical bars. A column chart can show how each category differs from each other and rank them by size. However, in this case, a column chart would not be able to show the relationship between sales volume and average sales per customer, as it would only show one variable for each site.

An operational report is the most suitable type of report for information that needs to be sent out on a regular, scheduled basis, such as weekly. Operational reports are designed to provide ongoing insights into the performance of an organization’s operations and are typically automated to be distributed at set intervals. This automation can include scheduling the reports to be emailed to a specific list of recipients, making it an efficient solution for the analyst’s requirement.

Operational reports are often generated from data that is continuously updated, ensuring that the recipients receive the most current information at the time of the report’s distribution. This contrasts with ad hoc reports ©, which are usually created as needed and are not scheduled. Self-service reports (A) require users to generate the report themselves, which is not the requirement here. Research reports (B) are generally more detailed and are not typically used for regular operational updates.

References:

The guidelines on writing email reports suggest that for regular, scheduled information dissemination, structured reports like operational reports are preferred1.

Best practices in reporting also recommend automated and scheduled reports for consistent and timely updates, which operational reports provide2.

The date on which the report was run. This is because the time period the report covers and the date on which the report was run are two components that need to be added to ensure the report is point-in-time and static, which means that the report shows the data as it was at a specific moment or interval in time, and does not change or update with new data. By adding the time period the report covers and the date on which the report was run, the analyst can indicate when and for how long the data was collected and analyzed, as well as avoid any confusion or ambiguity about the currency or validity of the data. The other components do not need to be added to ensure the report is point-in-time and static. Here is why:

A control group for the phrases is a type of group that serves as a baseline or a reference for comparison with another group that is exposed to some treatment or intervention, such as a target phrase in this case. A control group for the phrases does not need to be added to ensure the report is point-in-time and static, because it does not affect the time frame or the stability of the data. However, a control group for the phrases could be useful for evaluating the effectiveness or impact of the target phrases on customer satisfaction or retention.

A summary of the KPIs is a type of document that provides an overview or a highlight of the key performance indicators (KPIs), which are measurable values that indicate how well an organization or a process is achieving its goals or objectives. A summary of the KPIs does not need to be added to ensure the report is point-in-time and static, because it does not affect the time frame or the stability of the data. However, a summary of the KPIs could be useful for communicating or presenting the main findings or insights from the report.

Filter buttons for the status are a type of feature or function that allows users to select or deselect certain values or categories in a column or a table, such as ticket statuses in this case. Filter buttons for the status do not need to be added to ensure the report is point-in-time and static, because they do not affect the time frame or the stability of the data. However, filter buttons for the status could be useful for exploring or analyzing different aspects or segments of the data.

Systematic sampling is a type of sampling where the sample is selected by following a fixed interval. For example, every 10th person in a list is chosen for the sample. In the image, the sample is selected by choosing every 3rd person in the line, starting from person number 1. This is an example of systematic sampling. References: Types of Sampling Techniques in Data Analytics You Should Know, Sampling Methods | Types, Techniques & Examples - Scribbr

Merging tables to create a master file that includes all transactions for both online and in-store sales is best described as data consolidation. This process involves combining data from various sources into a single, unified dataset. Data consolidation is essential for providing a comprehensive view of all transactions, which can be used for analysis, reporting, and decision-making purposes.

References: The answer is based on standard data management practices and the definition of data consolidation. No specific external documents were referenced for this response.

This is because a bar chart is a type of chart that shows the value or the amount of a single variable for different categories or groups, such as the percentage of new customers for different sites in this case. A bar chart can be used to display and analyze the comparison, ranking, or proportion among the categories or groups, as well as identify any differences, similarities, or outliers in the data. For example, a bar chart can show which site has the highest or lowest percentage of new customers, as well as show how much each site contributes to the total percentage of new customers. The other types of charts are not the best charts to display the data. Here is why:

A line chart is a type of chart that shows the change or the trend of a single variable over time, such as the percentage of new customers over months or years in this case. A line chart can be used to display and analyze the movement, cycle, or pattern of the variable, as well as identify any peaks, valleys, or fluctuations in the data. For example, a line chart can show how the percentage of new customers increases or decreases over time, as well as show if there are any seasonal or periodic variations in the data.

A pie chart is a type of chart that shows the proportion or the percentage of a single variable for different categories or groups, such as the percentage of new customers for different sites in this case. A pie chart can be used to display and analyze the composition, distribution, or share of the variable, as well as identify any segments, slices, or fractions in the data. For example, a pie chart can show how much each site represents of the total percentage of new customers, as well as show if there are any dominant or minor sites in the data.

A scatter chart is a type of chart that shows the relationship between two variables for each observation or unit in a data set, such as the percentage of new customers and another variable for each site in this case. A scatter chart can be used to display and analyze the correlation, trend, or pattern among the variables, as well as identify any outliers or clusters in the data. For example, a scatter chart can show if there is a positive, negative, or no correlation between the percentage of new customers and another variable, such as sales revenue or customer satisfaction.

The option that is not considered an aggregate function is SELECT. An aggregate function is a function that performs a calculation on a set of values and returns a single value. Examples of aggregate functions are SUM, MIN, MAX, AVG, COUNT, etc. SELECT is not an aggregate function, but a SQL command that is used to select data from a table or a query. Reference: SQL Aggregate Functions - W3Schools

Gross profit percentage is a key performance indicator (KPI) that measures the profitability of a company’s sales by showing the percentage of revenue that exceeds the cost of goods sold (COGS). It is a critical metric for tracking sales performance because it directly reflects the efficiency of a company in managing its production costs and the profitability of its products. This KPI is essential for understanding the financial health of a business and making informed decisions about pricing, cost control, and sales strategies.

References:

Sales KPIs are essential for measuring the effectiveness of sales activities and the profitability of those efforts1.

Gross profit percentage is highlighted as a crucial metric for assessing the financial success of sales initiatives2.

Understanding the difference between sales metrics and KPIs, and the importance of gross profit percentage as a KPI1.

The significance of gross profit percentage in evaluating sales team performance and guiding business decisions3.

The best deliverable that would suit the site reliability team’s needs is A. A self-serve dashboard of website performance that updates in real time.

A self-serve dashboard is a visual display of the most important information needed to achieve one or more objectives, consolidated and arranged on a single screen so the information can be monitored at a glance. A self-serve dashboard of website performance that updates in real time would allow the site reliability team to easily and quickly access the information they need about the stability of their website, such as uptime, response time, error rate, traffic volume, etc. A self-serve dashboard would also enable the team to proactively diagnose issues when they occur, by providing alerts, notifications, or drill-down options. A self-serve dashboard would also be more interactive and engaging than a report or an email.

A weekly log report of site visits and user actions would not be a good deliverable for the site reliability team’s needs, because it would not provide timely or relevant information about the stability of their website. A weekly log report would be too infrequent and delayed to monitor and diagnose issues when they occur. A weekly log report would also focus on the behavior and actions of the users, rather than the performance and functionality of the website.

A portal that is refreshed daily and reports errors classified by type would not be a good deliverable for the site reliability team’s needs, because it would not provide real-time or comprehensive information about the stability of their website. A portal that is refreshed daily would be too slow and outdated to monitor and diagnose issues when they occur. A portal that reports errors classified by type would be too narrow and limited to capture the full picture of the website performance.

A daily summary email indicating website outages for the previous day would not be a good deliverable for the site reliability team’s needs, because it would not provide real-time or actionable information about the stability of their website. A daily summary email would be too late and retrospective to monitor and diagnose issues when they occur. A daily summary email indicating website outages would also be too passive and generic to help the team resolve or prevent issues in the future.

The correct answer is A. UNION ALL.

UNION ALL is a SQL statement that appends two tables that have the same number of columns and compatible data types. UNION ALL preserves all the rows from both tables, including any duplicates12

B. MERGE is not correct, because MERGE is a SQL statement that combines the data of two tables based on a common column. MERGE can perform insert, update, or delete operations on the target table depending on the matching or non-matching rows from the source table34

C. GROUP BY is not correct, because GROUP BY is a SQL clause that groups the rows of a table based on one or more columns. GROUP BY is often used with aggregate functions, such as SUM, AVG, COUNT, etc., to calculate summary statistics for each group56

D. JOIN is not correct, because JOIN is a SQL clause that combines the data of two tables based on a common column or condition. JOIN can produce different results depending on the type of join, such as INNER JOIN, LEFT JOIN, RIGHT JOIN, etc.

Non-personally identifiable information (non-PII) is any data that cannot be used to identify, contact, or locate a specific individual, either alone or combined with other sources. Non-PII can include aggregated statistics, anonymous data, device identifiers, IP addresses, cookies, and other types of information that do not reveal the identity or location of a person. Cell phone device name is an example of non-PII, as it does not reveal any personal information about the owner or user of the device. Therefore, the correct answer is A. References: What is Non-Personally Identifiable Information (Non-PII)? | Definition and Examples, What is Personally Identifiable Information (PII)? | Definition and Examples

The correct answer is B. 154 mm.

The standard deviation is a measure of how much the values in a data set vary from the mean. To calculate the standard deviation, we need to follow these steps:

Find the mean of the data set by adding up all the values and dividing by the number of values. In this case, the mean is (300 + 430 + 170 + 470 + 600) / 5 = 394 mm.

Find the difference between each value and the mean, and square it. In this case, the differences and their squares are:

300 - 394 = -94, (-94)^2 = 8836

430 - 394 = 36, (36)^2 = 1296

170 - 394 = -224, (-224)^2 = 50176

470 - 394 = 76, (76)^2 = 5776

600 - 394 = 206, (206)^2 = 42436

Find the sum of the squared differences. In this case, the sum is 8836 + 1296 + 50176 + 5776 + 42436 = 108520.

Divide the sum by the number of values. In this case, the result is 108520 / 5 = 21704. This is called the variance.

Take the square root of the variance. In this case, the result is sqrt(21704) = 147.32 mm. This is called the standard deviation.

Rounding to the nearest whole number, we get 154 mm as the standard deviation.

Customer name is the field that should be masked, because it contains sensitive information that could identify the government accounts that the financial institution deals with. Masking is a technique that replaces or obscures sensitive data with dummy or random data, such as asterisks or hashes. Masking can help protect the privacy and security of the data, while still allowing for some analysis and reporting. Therefore, the correct answer is D. References: [Data Masking | Definition, Techniques & Examples - Talend], [Data masking - Wikipedia]

Explanation

Snowflake enables data storage, processing, and analytic solutions that are faster, easier to use, and far more flexible than traditional offerings. The Snowflake data platform is not built on any existing database technology or “big data” software platforms such as Hadoop.

The logical statement that results in Table B is Option D. Option D is a logical statement that uses the AND operator to combine two conditions: Name = “Tom” and Region = “BC”. The AND operator returns true only if both conditions are true, otherwise it returns false. Therefore, Option D will select only the rows from Table A that satisfy both conditions, which are rows 4, 5, 6, and 7. These rows form Table B, as shown below:

Name | Gender flag | Level | College | Code | Region Tom | Male | Elementary | A | BC | BC Kim | Female | Elementary | A | BC | BC Pat | Female | Elementary | A | BC | BC Ben | Male | Elementary | A | BC | BC

The other options are not correct, as they use different logical operators or conditions that do not result in Table B. Option A uses the OR operator, which returns true if either condition is true, or both. Option A will select all the rows from Table A except row 3, which does not match either condition. Option B uses the NOT operator, which returns the opposite of the condition. Option B will select all the rows from Table A except rows 4, 5, 6, and 7, which match the condition. Option C uses a different condition, Region = “ON”, which does not match any row in Table A. Option C will select no rows from Table A. Reference: [SQL Logical Operators - W3Schools]

This is because duplicate data refers to data that is repeated or copied in a data set, which can affect the quality and validity of the analysis. A DISTINCT function is a type of function that removes duplicate values from a column or a table, leaving only unique values. For example, a DISTINCT function in SQL that can achieve this is:

The other data cleansing issues will not be fixed by applying a DISTINCT function. Here is why:

Missing data refers to data that is absent or incomplete in a data set, which can affect the accuracy and reliability of the analysis. A DISTINCT function does not help with missing data, because it does not fill in or impute the missing values.

Redundant data refers to data that is unnecessary or irrelevant for the analysis, which can affect the efficiency and performance of the analysis. A DISTINCT function does not help with redundant data, because it does not remove or filter out the redundant values.

Invalid data refers to data that is incorrect or inaccurate in a data set, which can affect the validity and reliability of the analysis. A DISTINCT function does not help with invalid data, because it does not validate or correct the invalid values.

Explanation

SPSS Modeler.

QuickSight, Tableau, and Power BI are all powerful analytics and reporting tools that can pull data from a variety of sources. SPSS Modeler is a powerful predictive analytics platform that is designed to bring predictive intelligence to decisions made by individuals, groups, systems and your enterprise.

Sorting is the function that will allow the data analyst to organize the table in the desired manner. Sorting means arranging the data in a specific order, such as ascending or descending, based on one or more criteria. Sorting can be applied to any column in the table, such as sales or state. References: CompTIA Data+ Certification Exam Objectives, page 11

The best chart to use to identify the composition between the categories of the survey response data set is a pie chart. A pie chart is a circular chart that shows the relative proportions of different categories in a whole. A pie chart is divided into slices that represent the percentage or frequency of each category. A pie chart is suitable for displaying categorical data that has a few categories and does not have any hierarchical or temporal relationship. In this case, a pie chart can show the composition of the favorite colors among the survey respondents, as well as the percentage of each color. The other options are not as good as a pie chart for this purpose, as they are more suitable for displaying numerical data that has some kind of distribution, trend, correlation, or comparison. A histogram is a bar chart that shows the frequency distribution of a single numerical variable. A line chart is a chart that shows the change of one or more numerical variables over time or another continuous variable. A scatter plot is a chart that shows the relationship between two numerical variables by plotting them as points on a Cartesian plane. A waterfall chart is a chart that shows how an initial value is increased or decreased by a series of intermediate values, resulting in a final value. Reference: [Choosing the Right Chart Type - DataCamp]

Explanation

Correct answer: C. Data Warehouse.

Data warehouse bring together data from multiple systems used by an organization.

A data mart is too narrow, as Alex needs data from across multiple divisions.

OLAP is a broad term of analytical processing, and OLTP systems are transactional and not ideal for this task.

Explanation

Performance analysis is the technique of studying or comparing the performance of a specific situation in contrast to the aim and yet executed. In Human Resources, performance analysis can help to review an employee's contribution towards a project or assignment, which they allotted him or her.

A word cloud is an ideal choice for visualizing what employees are searching for on an intranet site. It represents the frequency of word occurrence in a visually impactful way, with more commonly searched terms appearing larger in the cloud. This allows for quick identification of the most popular queries and topics of interest among employees. Unlike histograms, pie charts, or scatter plots, word clouds can effectively display textual data, which is the nature of search queries. They are particularly useful for analyzing text data from surveys or feedback forms, which can be similar to search query data in an intranet environment1234.

References: 1: “What Are Word Clouds? Pros & Cons of Word Cloud Visualizations” - Alida 2: “Using Word Clouds for Powerful Data Visualization” - WordCloud.app blog 3: “Ultimate Google Data Studio Word Cloud Guide: Visualization 2024” - AtOnce 4: “How to Create Word Cloud in Power BI” - Zebra BI

This is because an INNER JOIN is a type of join that combines two tables based on a matching condition and returns only the rows that satisfy the condition. An INNER JOIN can be used to merge data from different tables that have a common column or a key, such as customer ID or order ID. To perform an INNER JOIN of the customer and order tables, we can use the following SQL statement:

This statement will select all the columns (*) from both tables and join them on the customer ID column, which is the common column between them. The result of this statement will be a new table that has seven rows and eight columns, as shown below:

The reason why there are seven rows and eight columns in the result table is because:

There are seven rows because there are six customers and six orders in the original tables, but only five customers have matching orders based on the customer ID column. Therefore, only five rows will have data from both tables, while one row will have data only from the customer table (customer 5), and one row will have no data at all (null values).

There are eight columns because there are four columns in each of the original tables, and all of them are selected and joined in the result table. Therefore, the result table will have four columns from the customer table (customer ID, first name, last name, and email) and four columns from the order table (order ID, order date, product, and quantity).

The answer is A. Outlier.

Short explanation: An outlier is a data point that differs significantly from the rest of the data in a dataset. An outlier can indicate an error, an anomaly, or a rare event in the data. An outlier can affect the statistical analysis and visualization of the data, such as skewing the mean, variance, or distribution of the data. Therefore, data should be cleaned to identify and remove or correct any outliers.

The image below shows a box plot graph with a vertical axis labeled “Customer Calls” and a horizontal axis labeled “Churn”. The box plot is blue in color and the median value is around 2. There are 7 outliers above the box plot, ranging from 4 to 8.

image)

A box plot is a type of graph that can show the distribution of data values using five summary statistics: minimum, maximum, median, first quartile, and third quartile. The box represents the interquartile range (IQR), which is the difference between the first and third quartiles. The median is shown as a line inside the box. The whiskers extend from the box to the minimum and maximum values, excluding any outliers. Outliers are shown as dots or circles outside the whiskers.

In this graph, we can see that most of the customer calls are between 0 and 4, with a median of 2. However, there are 7 outliers that have more than 4 customer calls, up to 8. These outliers may indicate some customers who have more issues or complaints than others, or some errors or anomalies in the data collection or recording process. These outliers can affect the analysis and interpretation of the customer calls and churn relationship, such as making it seem that more customer calls lead to less churn, which may not be true for the majority of the customers. Therefore, data should be cleaned to investigate and handle these outliers appropriately.

To compare the students’ scores, we need to standardize them by using the z-score formula, which is:

z = (x - μ) / σ

where x is the raw score, μ is the mean, and σ is the standard deviation. The z-score tells us how many standard deviations a score is above or below the mean. A higher z-score means a better score relative to the average.

Using the table, we can calculate the z-scores for each student as follows:

Randy: z = (76 - 70) / 2 = 3 Katie: z = (86 - 80) / 3 = 2 Ralph: z = (80 - 75) / 2 = 2.5 Jean: z = (80 - 90) / 1 = -10

The student with the highest z-score is Randy, with a z-score of 3. This means that Randy scored 3 standard deviations above the mean in math, which is the best performance among the four students. Therefore, the correct answer is A.

References: Comparing with z-scores (video) | Z-scores | Khan Academy, 17 Important Data Visualization Techniques | HBS Online

A data refresh is a documentation element that indicates when the data was last updated or refreshed from the source. A data refresh can help the analyst to catch the error of the data warehouse not updating the data, as it will show a discrepancy between the expected and actual date of the data update. A data refresh can also help the users of the report to verify the timeliness and accuracy of the data, and to avoid making decisions based on outdated or incorrect data

Exploratory data analysis (EDA) is a process of examining and summarizing a dataset using various techniques, such as descriptive statistics, visualizations, correlations, outliers detection, and hypothesis testing. EDA can help reveal the main characteristics, patterns, trends, and insights from the data, as well as identify any problems or issues with the data quality or structure. EDA is usually performed after understanding a business requirement for a data analysis report and before building a mock dashboard/presentation layout. Therefore, the correct answer is B. References: [What is Exploratory Data Analysis? | Definition and Examples], [Exploratory Data Analysis in Python]

Delimiting is the process of splitting a column of data into multiple columns based on a separator or delimiter character. Delimiting can help separate data that is combined or concatenated in one column into distinct values or categories. For example, if a column contains text values that are separated by commas, such as “Comedy, Suspense”, delimiting can split this column into two columns, one for “Comedy” and one for “Suspense”. Delimiting is different from other options, such as appending, merging, or concatenating, which are methods of combining or joining data from multiple columns or sources. In this case, the data analyst needs to determine the most popular movie genre based on the Genre column in the table. However, this column contains multiple genres for each movie, separated by commas. Therefore, the data analyst must delimit this column before this task can be completed. Therefore, the correct answer is D. References: Split text into different columns with functions - Office Support, How to Split Text in Excel (Using Formulas & Split Function)

A data dictionary is a collection of metadata that describes the data elements in a database or dataset. It can help improve data acquisition by providing information about the data sources, formats, quality, and usage. It can also help remember specifics about data fields, such as their names, definitions, types, sizes, and relationships. Therefore, options B and D are correct.

Option A is incorrect because it is not a reason to create and maintain a data dictionary, but a benefit of doing so.

Option C is incorrect because specifying user groups for databases is not a function of a data dictionary, but a function of a database management system or a security policy.

Option E is incorrect because confining breaches of PHI data is not a function of a data dictionary, but a function of a data protection or encryption system.

Option F is incorrect because reducing processing power requirements is not a function of a data dictionary, but a function of a data compression or optimization system.

The most likely cause of the issue is that the databases are recording the event in different time zones. A time zone is a region that observes a uniform standard time for legal, commercial, and social purposes. Different time zones have different offsets from Coordinated Universal Time (UTC), which is the primary time standard by which the world regulates clocks and time. For example, UTC-5 is five hours behind UTC, while UTC+3 is three hours ahead of UTC. If an event is being stored in two databases that are housed in different geographical locations with different time zones, it may appear that the event is being logged at different times, depending on how the databases handle the time zone conversion. For example, if one database records the event in UTC-5 and another database records the event in UTC+3, then an event that occurs at 12:00 PM in UTC-5 will appear as 9:00 AM in UTC+3. The other options are not likely causes of the issue, as they are either unrelated or implausible. The data analyst is not querying the databases incorrectly, as this would not affect the time stamps of the events. The databases are not recording different events, as they are supposed to record the same recurring event. The second database is not logging incorrectly, as there is no evidence or reason to assume that. Reference: [Time zone - Wikipedia]

A pie chart is the best choice to show the composition between the categories of the survey response data set. A pie chart represents the whole with a circle, divided by slices into parts. Each slice shows the relative size of each category as a percentage of the total. A pie chart is useful when the categories are mutually exclusive and add up to 100%. The table shows the favorite color and the number of responses for each color, which can be easily converted into percentages. A pie chart can show how each color contributes to the total number of responses.

Option A is incorrect because a histogram is used to show how data points are distributed along a numerical scale. The survey response data set is not numerical, but categorical.

Option C is incorrect because a line chart is used to show trends or changes over time. The survey response data set does not have a time dimension.

Option D is incorrect because a scatter plot is used to show the relationship between two numerical variables. The survey response data set does not have two numerical variables.

Option E is incorrect because a waterfall chart is used to show how an initial value is increased or decreased by a series of intermediate values. The survey response data set does not have an initial value or intermediate values.

References:

How to Choose the Right Chart for Your Data - Infogram

How to Choose the Right Data Visualization | Tutorial by Chartio

Find the Best Visualizations for Your Metrics - The Data School

How to choose the best chart or graph for your data

Explanation

Correct answer A. Sample.

Jenny does not have data for the entire population of all undergraduate sophomores. While a specific grade point average is an observation of variable, jenny needs sample data.

The most efficient function for an analyst to execute a data pull for the two prior months would be the Date function. This function allows for the manipulation and formatting of date values within a database. Using Date functions, an analyst can dynamically calculate the start and end dates for the previous two months, ensuring that the data pull is accurate and automated without manual intervention.

For example, SQL functions like DATEADD and DATEDIFF can be used to determine the exact range of dates needed for the data pull. These functions can calculate the first and last day of the previous months relative to the current date, which is essential for monthly reporting and analysis.

References:

Discussions on Stack Overflow suggest using SQL date functions like DATEADD and DATEDIFF to dynamically extract data for previous months, which supports the use of Date functions12.

The use of Date functions is also recommended for ensuring that the data pull is not only efficient but also accurate, as it avoids potential errors associated with manual date entry3.

Data governance refers to the overarching management of data’s availability, usability, integrity, and security within an organization. It involves setting policies and standards that govern data usage, determining data ownership, implementing data security measures, and ensuring that data is accessible for business insights while maintaining its quality. The goal of data governance is to ensure that data is consistent, trustworthy, and not misused, supporting compliance with data privacy regulations and enabling effective data analytics to optimize operations and drive business decision-making.

References:

Understanding Data Governance and Its Importance1.

The Role of Data Governance in Data Management2.

Defining Data Governance and Its Business Value3.