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CompTIA DA0-002 Exam Overview:

Certification Vendor:	CompTIA
Exam Name:	CompTIA Data+ Exam (2025)
Exam Number:	DA0-002
Passing Score:	720 (scale of 100–900)
Related Certifications:	CompTIA Data+
Exam Format:	Performance-based, Multiple-choice
Certificate Validity Period:	3 years
Exam Price:	USD 255
Exam Duration:	90 minutes
Real Exam Qty:	Up to 90
Available Languages:	English
Sample Questions:	CompTIA DA0-002 Sample Questions
Exam Way:	Online proctored or at Pearson VUE authorized testing centers
Pre Condition:	No formal prerequisites; recommended 18–24 months of experience in a report/business analyst role or equivalent knowledge
Official Syllabus URL:	https://www.comptia.org/certifications/data

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CompTIA DA0-002 Exam Syllabus Topics:

Topic	Details
Topic 1	Data Mining: This section of the exam measures skills of a Business Intelligence Analyst and covers how data is collected, cleaned, and prepared for analysis. It explains methods like ETL and ELT for data integration, as well as web scraping, API use, and survey data collection. Candidates are expected to identify issues like missing or duplicated data and apply techniques like filtering, sorting, merging, and normalizing. The section also touches on query optimization strategies to improve data handling efficiency.
Topic 2	Data Analysis: This section of the exam measures skills of a Reporting Analyst and includes foundational knowledge of statistical methods such as averages, variances, and standard deviation. It covers how to use data to find patterns, track performance, and make predictions. This domain also introduces hypothesis testing, regression, correlation, and different types of analysis like exploratory and trend analysis. Candidates should also be aware of common tools used for analysis, including Excel, SQL, Python, R, and popular BI platforms like Tableau and Power BI.
Topic 3	Data Concepts and Environments: This section of the exam measures the skills of a Junior Data Analyst and focuses on understanding core data concepts such as database types, schema structures, and data formats. It highlights differences between structured and unstructured data, compares file types like CSV, JSON, and XML, and introduces key ideas about data dimensions and slowly changing dimensions. Knowing how data is stored and organized helps professionals better prepare for analysis and reporting tasks.
Topic 4	Visualization: This section of the exam measures skills of a Data Visualisation Specialist and focuses on turning raw data into clear, visual insights. It teaches how to match visual formats like bar charts, heat maps, and line graphs to specific audiences and needs. Candidates must understand how to create dashboards and reports using proper design elements such as labels, layout, branding, and colour schemes. This section also includes best practices for dashboard development and delivery through various platforms and user access levels.
Topic 5	This section of the exam measures skills of a Data Governance Associate and introduces principles for keeping data secure, accurate, and compliant. It covers access controls, encryption, classification of sensitive data like PII and PHI, and legal requirements tied to data use. Candidates must know how to apply quality checks, validate data, and manage master data effectively. It also addresses best practices for maintaining integrity through data dictionaries, audits, and standardisation processes.

CompTIA Data+ Exam Sample Questions (Q109-Q114):

NEW QUESTION # 109
A data analyst receives a report from a dashboard user stating that their report is taking longer than usual to load. The user needs five components for the dashboard. However, upon investigating, the analyst notices ten components that are attempting to load. Which of the following steps should the analyst take to correct this issue?

A. Check the filters to include only the required information.
B. Check with administrators to review the logs of the report server.
C. Check the refresh rate on the report server.
D. Check with the network team for stale network connections.

Answer: A

Explanation:
Reducing the number of components being loaded by applying appropriate filters ensures only the required data is retrieved and displayed, improving performance and resolving the delay.

NEW QUESTION # 110
A data analyst is building a new report on sales data. The analyst wants to find the total sales per channel, but only for those channels which have a total sales higher than 20.0. Given the following:

Which of the following should the analyst use?

Answer: B

Explanation:
To find the total sales per channel, the query must aggregate sales values using SUM(VALUE) and group the results by channel. Because the requirement is to return only channels whose total sales exceed 20.0, the aggregate result must be filtered with a HAVING clause. HAVING SUM(VALUE) > 20.0 correctly filters the grouped totals after aggregation.

NEW QUESTION # 111
Due to new reporting requirements, a data analyst must add new classification codes to historical data. Which of the following is the best technique for this task?

A. Union
B. Binning
C. Append
D. Parsing

Answer: C

Explanation:
This question falls under theData Acquisition and Preparationdomain, focusing on modifying historical data. The task is to add new classification codes to existing data, which involves adding new rows or columns.
* Append (Option A): Appending adds new rows to a dataset, which is suitable if the classification codes are new records (e.g., a new table of codes to combine with historical data). If the codes are a new column, a join or update might be used, but append fits the context of adding new data.
* Binning (Option B): Binning groups data into categories, not suitable for adding classification codes.
* Parsing (Option C): Parsing breaks down data (e.g., splitting strings), not relevant for adding codes.
* Union (Option D): Union stacks tables with identical structures, but the task involves adding new data (codes) to historical data, not combining identical tables.
The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and appending is a common technique for adding new data to historical datasets.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation.

NEW QUESTION # 112
An analyst needs to produce a final dataset using the following tables:
CourseID
SectionNumber
StudentID
MATH1000
1
10009
MATH1000
2
10007
PSYC1500
1
10009
PSYC1500
1
10015
StudentID
FirstName
LastName
10009
Jane
Smith
10007
John
Doe
10015
Robert
Roe
The expected output should be formatted as follows:
| CourseID | SectionNumber | StudentID | FirstName | LastName |
Which of the following actions is the best way to produce the requested output?

A. Aggregate
B. Group
C. Filter
D. Join

Answer: D

Explanation:
This question falls under theData Acquisition and Preparationdomain, focusing on combining tables to produce a dataset. The task requires combining the Courses and Students tables to include student names with course details, based on the StudentID.
* Aggregate (Option A): Aggregation (e.g., SUM, COUNT) summarizes data, not suitable for combining tables to include names.
* Join (Option B): A join operation (e.g., INNER JOIN on StudentID) combines the tables, matching records to produce the requested output with CourseID, SectionNumber, StudentID, FirstName, and LastName.
* Group (Option C): Grouping is used for aggregation (e.g., GROUP BY in SQL), not for combining tables.
* Filter (Option D): Filtering selects specific rows, not relevant for combining tables.
The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," such as joining tables to create a unified dataset.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation.

NEW QUESTION # 113
A data analyst wants to use the following tables to find all the customers who have not placed an order:

Which of the following SQL statements is the best way to accomplish this task?

A. A SELECT * FROM CUSTOMERS AS C LEFT JOIN PRODUCTS AS P ON C.ID = P.Customer_ID WHERE P.Customer_ID IS NULL
B. SELECT * FROM PRODUCTS AS P INNER JOIN CUSTOMERS AS C ON P.Customer_ID =C.ID WHERE (SELECT COUNT(P.) = 0)
C. SELECT * FROM CUSTOMERS AS C INNER JOIN PRODUCTS AS P ON C.ID = C.IDWHERE COUNT(P.) =
D. SELECT * FROM PRODUCTS AS P LEFT JOIN CUSTOMERS AS C ON P.Customer_ID = C.ID WHERE P.Customer_ID IS NOT NULL

Answer: A

NEW QUESTION # 114
......

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