CompTIA DA0-001 - CompTIA Data+ Certification Exam

To compare two quantitative variables, the classic best choice (when available) is a scatter plot. In this version of the question, however, scatter plot is not one of the options. Among the given choices:

Bar graph – typically compares a categorical variable (categories on the x-axis) against a quantitative measure (bar height). It is not ideal for two purely quantitative variables.

Histogram – is designed to show the distribution of a single quantitative variable, not the relationship between two quantitative variables.

Pie chart – is used to show proportions of a whole and is not appropriate for comparing two quantitative variables directly.

Heat map (B) – can be used to display the relationship between two dimensions (which can be numeric) by using color intensity to encode the magnitude of a third variable or frequency of combinations. It is the best fit among the given choices for visualizing relationships across pairs of numeric values when a more suitable plot (like scatter) is not provided.

So, from the options listed here, Heat map (B) is the most appropriate to represent a relationship between two quantitative variables.

Note: In many CompTIA Data+ practice items that do include a scatter plot option, that is normally the correct choice for “two quantitative variables.” Here, with the restricted options, heat map is the best available match.

CompTIA Data+ Reference (concept alignment):

CompTIA Data+ Official Exam Objectives – Domain on Visualization (choosing appropriate visualizations for different data types).

CompTIA Data+ Official Study Guide – section distinguishing bar charts, histograms, pie charts, and heat maps, and their use cases.

 A flat text file is a type of data file that contains only plain text without any formatting or markup. Data in a flat text file is usually separated by a delimiter, which is a character that marks the boundary between different fields or values. For example, a comma-separated values (CSV) file is a flat text file that uses commas as delimiters. Other common delimiters are tabs, spaces, semicolons, and pipes. Therefore, the correct answer is A. References: Plain text - Wikipedia, Comparison of document markup languages - Wikipedia

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.

A data-mining ETL tool is a software application that performs extract, transform, and load(ETL) operations on data for data mining purposes. Data mining is the process of discovering patterns, trends, and insights from large and complex data sets. ETL tools help to prepare the data for analysis by extracting data from various sources, transforming data into a consistent and suitable format, and loading data into a data warehouse or other destination. SSIS (SQL Server Integration Services) is an example of a data-mining ETL tool that is part of Microsoft SQL Server. SSIS provides graphical tools and wizards for building and debugging ETL packages that can work with various data sources and destinations. Therefore, the correct answer is A. References: [Data Mining - SQL Server Integration Services (SSIS) | Microsoft Docs], [What Is Data Mining? | Oracle]

Count data represents values that come from counting distinct items (e.g., number of tickets sold, number of customers, number of defects). These values are:

Whole numbers (0, 1, 2, 3, …)

Not fractional (you can’t have 3.7 customers)

Typically represented as discrete quantitative data

In statistics and in the CompTIA Data+ context, discrete data is the correct type for counts, whereas:

Continuous data can take any value within a range (including decimals), such as height, temperature, or time.

Referential and Sequential are not standard data type classifications for numeric/statistical data in the exam context.

So, when the question asks about “count data,” the best and most accurate data type is Discrete (A).

CompTIA Data+ Reference (concept alignment):

CompTIA Data+ (DA0-001) Official Exam Objectives – Domain on Data Concepts and Environments (data types: discrete vs continuous).

CompTIA Data+ Official Study Guide – section describing quantitative data types and specifically discrete data used for counts.

star schema is a type of database schema that consists of one fact table that is composed of multiple dimensions. A fact table contains quantitative measures or facts that are related to a specific event or transaction. A dimension table contains descriptive attributes or dimensions that provide context for the facts. A star schema is called so because it resembles a star, with the fact table at the center and the dimension tables radiating from it. A star schema is a type of dimensional schema, which is designed for data warehousing and analytical purposes. Other types of dimensional schemas include snowflake schema and galaxy schema. A snowflake schema is similar to a star schema, except that some or all of the dimension tables are normalized into multiple tables. A galaxy schema consists of multiple fact tables that share some common dimension tables. A transactional schema is a type of database schema that is designed for operational purposes, such as recording day-to-day transactions and activities. A transactional schema is usually normalized to reduce data redundancy and improve data integrity. A non-relational schema is a type of database schema that does not follow the relational model, which organizes data into tables with rows and columns. A non-relational schema can store data in various formats, such as documents, graphs, key-value pairs, etc.

Data aggregation is the process of compiling data from multiple sources and summarizing it into a single dataset. Data aggregation can be used to calculate statistics, such as averages, sums, counts, or percentages. In this case, the analyst must obtain the average daily sales for the following week, which is a statistic that can be calculated by aggregating the sales data from each day and dividing by the number of days. Data aggregation can be done using various tools and methods, such as spreadsheets, databases, or programming languages.

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.