DA0-001 Exam Results

SCD stands for Slowly Changing Dimension, a common concept in data warehousing. It describes:

How to manage changes to dimension data (e.g., customer address, product category) over time.

Ensuring that historical facts are correctly associated with the appropriate version of the dimension at the time of the transaction.

Common SCD types (Type 1, Type 2, etc.) define:

Whether old values are overwritten (Type 1),

Or historical records are preserved with new rows and effective dates (Type 2),

Or a combination of methods.

This aligns with “a technique used to manage historical data changes” in dimensional data, especially in star schemas.

Why other options are incorrect:

Data profiling (A): SCD is not used for profiling; profiling examines structure/quality of existing data.

Sorting large data sets (B): Sorting is unrelated to SCD logic.

Archiving data (C): Archiving moves old data to long-term storage; SCD manages changes within dimensions, not general archival.

Therefore, SCD is best defined as “a technique used to manage historical data changes” (D).

CompTIA Data+ Reference (concept alignment):

DA0-001 Exam Objectives – Data concepts and environments: dimensional modeling and Slowly Changing Dimensions (SCD).

Standard warehouse design content on SCD Types 1/2/3 as methods to handle historical changes in dimension tables.

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

This is because date is a type of variable that represents a specific point or period in time, such as a day, a month, or a year. Date variables can be used to store, manipulate, or analyze temporal data, such as transaction dates, birth dates, or expiration dates. For example, date variables can be used to calculate the duration or the difference between two dates, or to filter or sort the data by date. The other variable types are not correct descriptions of the “Year” column. Here is why:

Numeric is a type of variable that represents a numerical value, such as an integer, a decimal, or a fraction. Numeric variables can be used to store, manipulate, or analyze quantitative data, such as amounts, prices, or scores. For example, numeric variables can be used to perform arithmetic operations or calculations on the data, or to measure the central tendency or the dispersion of the data.

Alphanumeric is a type of variable that represents a combination of alphabetic and numeric characters, such as letters, numbers, symbols, or spaces. Alphanumeric variables can be used to store, manipulate, or analyze textual data, such as names, addresses, or codes. For example, alphanumeric variables can be used to concatenate or split the data, or to search or match the data using patterns or expressions.

Text is a type of variable that represents a sequence of alphabetic characters, such as letters or words. Text variables can be used to store, manipulate, or analyze textual data, such as names, categories, or labels. For example, text variables can be used to change the case or the length of the data, or to compare or classify the data using criteria or rules.