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CompTIA DA0-002 Exam Overview:

Certification Vendor:CompTIA
Exam Name:CompTIA Data+ Exam
Exam Number:DA0-002
Certificate Validity Period:3 years
Real Exam Qty:Up to 90
Exam Duration:90 minutes
Passing Score:720 (scale 100–900)
Exam Format:Performance-based questions, Multiple-choice
Exam Price:$255 USD
Available Languages:English
Recommended Training:CompTIA Official Training
Exam Registration:Pearson VUE Testing
CompTIA Official Registration
Sample Questions:CompTIA DA0-002 Sample Questions
Exam Way:Online proctored or onsite at Pearson VUE test centers
Pre Condition:Recommended 18–24 months of experience in data/business analysis role; familiarity with databases, analytical tools, basic statistics, and visualization
Official Syllabus URL:https://www.comptia.org/certifications/data

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CompTIA DA0-002 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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 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.

CompTIA Data+ Exam Sample Questions (Q32-Q37):

NEW QUESTION # 32
A data analyst learns that a report detailing employee sales is reflecting sales only for the current month. Which of the following is the most likely cause?

Answer: B


NEW QUESTION # 33
Which of the following best describes the reason an analyst would reference a data dictionary versus a source's metadata?

Answer: C

Explanation:
This question is part of theData Concepts and Environmentsdomain, focusing on the purpose of data documentation tools like data dictionaries and metadata. The question compares their uses.
* To gather information and resources about the data (Option A): This is too vague and not specific to a data dictionary's purpose.
* To find the content and specific attributes for a dataset (Option B): A data dictionary provides detailed definitions of data elements (e.g., field names, types, descriptions), which is more specific than metadata, which often includes broader information like creation date or source.
* To find a summary of basic information about the dataset (Option C): This better describes metadata, which provides high-level summaries, not detailed attributes.
* To gather information about the availability of the data (Option D): Neither a data dictionary nor metadata typically focuses on data availability.
The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and a data dictionary is specifically used to find detailed attributes of a dataset.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.


NEW QUESTION # 34
A data analyst is evaluating all conditions in a query. Which of the following is the best logical function to accomplish this task?

Answer: C

Explanation:
This question falls under theData Analysisdomain, focusing on SQL logical functions for query evaluation.
The task is to evaluate "all conditions," implying multiple conditions must be true together.
* OR (Option A): OR returns true if any condition is true, not ensuring all conditions are met.
* NOT (Option B): NOT negates a condition, not suitable for combining multiple conditions.
* AND (Option C): AND requires all conditions to be true, which aligns with evaluating "allconditions" in a query.
* IF (Option D): IF is a conditional function for decision-making, not for evaluating multiple conditions together.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods using SQL queries," and AND is the best logical function for ensuring all conditions are met.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.


NEW QUESTION # 35
A recent server migration applied an update to dataset naming conventions. Multiple users are now reporting stale information in an existing dashboard. The date in the dataset confirms a successful data refresh. Which of the following should a data analyst do first?

Answer: B


NEW QUESTION # 36
Which of the following programming languages are the most efficient at performing data mining and statistical analysis? (Choose two.)

Answer: B,C

Explanation:
R is specifically designed for statistical analysis and data mining, offering extensive statistical libraries. Python provides powerful data processing and machine learning libraries, making it highly efficient for data mining and analysis tasks.


NEW QUESTION # 37
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