DA0-001 Reviews Questions

Parameterization in SQL queries is a technique used to prevent SQL injection, which is a common security vulnerability that allows an attacker to interfere with the queries that an application makes to its database. By using parameterized queries, the database can distinguish between code and data, regardless of the input received. This method ensures that an attacker cannot change the intent of a query, even if SQL commands are inserted by the attacker. While parameterization can also affect performance by enabling consistent query execution plans, its primary purpose is to enhance security.

References:

Medium article on SQL Query Optimization1.

MSSQLTips on SQL Query Performance2.

Blog post on SQL Performance Optimization3.

SQL Easy guide on improving SQL Query Performance4.

LearnSQL.com on SQL for Data Analysis5.

The measure of dispersion is used to describe the spread of data around a central value. In the context of a data set containing actual and projected sales, the measure of dispersion will help to understand the variability or consistency of sales figures. The variance is themost appropriate statistical method for finding the measure of dispersion because it calculates the average of the squared differences from the Mean, providing a clear picture of data spread. It is especially useful in comparing the spread between different data sets and understanding the distribution of data points.

Mean is a measure of central tendency, not dispersion.

Correlation measures the relationship between two variables, not the spread of a single variable.

Confidence intervals are used to estimate the range within which a population parameter will fall, but they do not measure dispersion within the data set itself.

References:

Measures of Dispersion in Statistics1

Measures of Dispersion - Definition, Formulas, Examples2

Statistical dispersion - Wikipedia3

Discrete data is a type of data that can only take certain values, usually whole numbers or integers. Discrete data can be counted, but not measured. For example, the number of students in a class, the number of books in a library, or the number of phone calls that a call center receives in a day are all examples of discrete data. Discrete data is different from continuous data, which can take any value within a range, and can be measured with precision. For example, the height of a person, the weight of a fruit, or the temperature of a room are all examples of continuous data. Therefore, the correct answer is D. References: [Discrete vs Continuous Data: Definition and Examples - Statistics How To], [Discrete Data - Definition and Examples | Math Goodies]