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CompTIA DA0-002 Exam Overview:

Certification Vendor:CompTIA
Exam Name:CompTIA Data+ Exam (2025)
Exam Number:DA0-002
Related Certifications:CompTIA Data+
Real Exam Qty:Up to 90
Exam Duration:90 minutes
Available Languages:English
Exam Format:Multiple-choice, Performance-based
Certificate Validity Period:3 years
Passing Score:720 (scale of 100–900)
Exam Price:USD 255
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:

TopicDetails
Topic 1
  • 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 2
  • 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.
Topic 3
  • 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 4
  • 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 5
  • 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.

CompTIA Data+ Exam Sample Questions (Q100-Q105):

NEW QUESTION # 100
A data analyst is creating a document that covers the guidelines and transformation of the suite of sales dashboards that they are responsible for delivering. Which of the following is the analyst most likely creating?

Answer: B

Explanation:
Data lineage documents how data is transformed, processed, and flows through systems, including the steps and rules applied to create outputs such as dashboards.


NEW QUESTION # 101
A data analyst is helping a company achieve its strategic goals. Which of the following types of analytics should the analyst incorporate into the dashboards and tools delivered to the company?

Answer: D

Explanation:
Prescriptive analytics provides recommended actions based on data insights, directly supporting decision-making and helping organizations achieve their strategic goals.


NEW QUESTION # 102
Which of the following explains the difference between a char and an nchar data field?

Answer: B

Explanation:
Both char and nchar are fixed-length data types. The difference is that nchar stores Unicode characters, allowing support for a wider range of languages and symbols. Because Unicode characters typically require two bytes per character, nchar uses twice the storage space of char.


NEW QUESTION # 103
Which of the following is found in metadata?

Answer: A

Explanation:
This question pertains to the Data Concepts and Environments domain, focusing on the content of metadata. Metadata describes data attributes, and the task is to identify what it typically includes.
* Transformations (Option A): Transformations (e.g., data cleaning steps) are part of data lineage, not metadata.
* Data lineage (Option B): Data lineage tracks data flow and transformations, which is related to metadata but not a direct component.
* Syntax (Option C): Syntax refers to code structure, not a metadata component.
* Variable types (Option D): Metadata includes information about data fields, such as variable types (e.
g., integer, string), which is a standard component.
The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and metadata typically contains details like variable types to describe the dataset.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.


NEW QUESTION # 104
A data analyst is reviewing a new data source that contains employee IDs, job titles, dates of birth, addresses, years of service, and employees' birth months. Which of the following inconsistencies should the analyst identify?

Answer: A

Explanation:
Redundancy occurs when the same information can be derived from other data already present in the dataset. Since the data source contains both dates of birth and employees' birth months, the birth month is redundant because it can be extracted directly from the date of birth.


NEW QUESTION # 105
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