CompTIA DA0-002 - CompTIA Data+ Exam (2025)

This question is part of the Visualization and Reporting domain, focusing on report delivery methods. The requirement for a report that can be generated and accessed at any time suggests user-driven access.

Ad hoc (Option A): Ad hoc reports are one-time, on-demand reports, not designed for anytime access by the user.

Dynamic (Option B): Dynamic reports allow interactivity but don’t necessarily imply user-generated access at any time.

Self-service (Option C): Self-service reporting allows users (e.g., the senior manager) to generate and access reports on-demand at any time, fitting the requirement.

Static (Option D): Static reports are fixed and don’t allow on-demand generation by the user.

The DA0-002 Visualization and Reporting domain includes "the appropriate visualization in the form of a report" with delivery methods, and self-service is ideal for anytime access.

This question falls under the Data Governance domain, focusing on tools that support data management and quality control features. The task is to identify a tool with capabilities like versioning, corruption checks, KPIs, and authentication.

Notebook (Option A): Notebooks (e.g., Jupyter) are for data analysis and coding but don’t inherently support versioning, corruption checks, or authentication.

REST API (Option B): REST APIs enable data access but don’t provide versioning or corruption checks as a primary function.

Pipeline (Option C): Data pipelines automate data workflows but don’t typically include versioning or authentication.

Source control (Option D): Source control systems (e.g., Git) support automatic versioning (tracking changes), corruption checks (integrity verification), KPIs (e.g., commit frequency), and user authentication (access control), making this the best fit.

The DA0-002 Data Governance domain includes "data quality control concepts," and source control systems provide the listed capabilities to ensure data integrity and security.

This question pertains to the Visualization and Reporting domain, focusing on selecting the appropriate visualization for a specific requirement. The task is to show the composition of total sales across three channels, which involves showing proportions.

Pivot table (Option A): A pivot table summarizes data but isn’t a visualization; it’s more for data exploration.

Pie chart (Option B): A pie chart shows the proportion of total sales for each channel (Digital, Store, Online), which is ideal for displaying composition.

KPI card (Option C): A KPI card displays a single metric, not suitable for showing composition across multiple channels.

Box plot (Option D): A box plot shows data distribution (e.g., quartiles), not proportions.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and a pie chart is best for showing the composition of totals.

This question pertains to the Data Concepts and Environments domain, focusing on file formats for data sharing. The task is to identify the best reason to choose CSV over Excel for sharing data.

CSV files can store different types of encoding (Option A): While CSV files can use different encodings, this isn’t the primary reason to choose them over Excel.

CSV files are not vendor-specific (Option B): CSV is a plain-text format that can be opened by any software, unlike Excel files, which are tied to Microsoft Excel, making CSV more interoperable and the best reason for sharing.

CSV files are smaller in size (Option C): CSV files are often smaller due to their simplicity, but this isn’t always the primary reason for sharing.

CSV files are easier to change in text editors (Option D): While true, this isn’t the most compelling reason for sharing data across systems.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and CSV’s vendor-neutral nature makes it ideal for sharing data.

This question falls under the Data Analysis domain, focusing on SQL query correction. The query uses an aggregate function (SUM) but has two issues: it uses "SORT BY" (incorrect syntax) and lacks a GROUP BY clause for non-aggregated columns.

The query selects firstName, lastName, and SUM(income), but firstName and lastName are not aggregated, requiring a GROUP BY clause.

"SORT BY" is incorrect; the correct syntax is "ORDER BY."

Option A: SELECT firstName, lastName, SUM(income) FROM companyRoster HAVING SUM(income) > 10000000This adds a HAVING clause but doesn’t fix the GROUP BY issue, so it’s still invalid.

Option B: SELECT firstName, lastName, SUM(income) FROM companyRoster GROUP BY firstName, lastNameThis adds the required GROUP BY clause for firstName and lastName, fixing the aggregation error. While it removes the ORDER BY, the query will run without it, addressing the primary error.

Option C: SELECT firstName, lastName, SUM(income) FROM companyRoster ORDER BY firstName, incomeThis fixes "SORT BY" to "ORDER BY" but doesn’t address the missing GROUP BY, so the query remains invalid.

Option D: SELECT firstName, lastName, SUM(income) FROM companyRosterThis removes the ORDER BY but still lacks the GROUP BY clause, making it invalid.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods using SQL queries," and adding GROUP BY fixes the aggregation error, allowing the query to run.

This question falls under the Data Analysis domain of CompTIA Data+ DA0-002, which involves using SQL queries to analyze data and address issues like duplicates in datasets. The issue here is that the initial query counts all instances of EMPLOYEE_ID in the JOBS table, but employees can have multiple jobs, leading to an inflated head count. The goal is to count unique employees.

SELECT JOB_TYPE, COUNT DISTINCT(EMPLOYEE_ID) FROM JOBS (Option A): This query is syntactically incorrect because COUNT DISTINCT(EMPLOYEE_ID) should use parentheses as COUNT(DISTINCT EMPLOYEE_ID). It also groups by JOB_TYPE, which is unnecessary for a total head count.

SELECT DISTINCT COUNT(EMPLOYEE_ID) FROM JOBS (Option B): This query is incorrect because DISTINCT applies to the rows returned, not the COUNT function directly. It doesn’t address the duplicate EMPLOYEE_ID issue.

SELECT JOB_TYPE, COUNT(DISTINCT EMPLOYEE_ID) FROM JOBS (Option C): While this query correctly uses COUNT(DISTINCT EMPLOYEE_ID) to count unique employees, grouping by JOB_TYPE breaks the count into separate groups, which isn’t required for a total head count.

SELECT COUNT(DISTINCT EMPLOYEE_ID) FROM JOBS (Option D): This query correctly counts only unique EMPLOYEE_IDs by using the DISTINCT keyword within the COUNT function, providing an accurate total head count without grouping.

The DA0-002 Data Analysis domain emphasizes "given a scenario, applying the appropriate descriptive statistical methods using SQL queries," which includes handling duplicates with functions like COUNT(DISTINCT). Option D is the most direct and accurate method for a total unique head count.

This question pertains to the Visualization and Reporting domain, focusing on selecting the appropriate communication method for a report. The requirements emphasize simplicity, clarity, accessibility, and automatic updates, which point to a specific approach.

Executive summary (Option A): An executive summary is a written document that summarizes metrics but isn’t typically updated automatically and may not be accessible to all levels without distribution.

Slide presentation (Option B): A slide presentation can be clear but requires manual updates and isn’t inherently accessible to all company levels.

Key performance indicator dashboard (Option C): A KPI dashboard provides a simple, visual summary of metrics, is clear and concise, can be understood by all levels, and can be set up to update automatically, meeting all requirements.

Open data portal (Option D): An open data portal provides raw data access, which may not be simple or easily understood by all levels.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and a KPI dashboard is the best approach for meeting these requirements.

This question pertains to the Data Governance domain, focusing on data quality and consistency in survey design. Requiring all questions to have an answer ensures a specific type of data quality.

Missing values (Option A): Requiring answers prevents missing values (NULLs or blanks) in the survey responses, which is the primary inconsistency this setting addresses.

Duplication (Option B): Duplication refers to repeated records, not prevented by requiring answers.

Data corruption (Option C): Data corruption involves damaged or altered data, not related to missing answers.

Completeness (Option D): Completeness is the concept of having all necessary data, but "missing values" is the specific inconsistency prevented here.

The DA0-002 Data Governance domain includes "data quality control concepts," and preventing missing values ensures data integrity in survey responses.