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CompTIA DA0-002 Exam Overview:

Certification Vendor:CompTIA
Exam Name:CompTIA Data+ Exam (2025)
Exam Number:DA0-002
Certificate Validity Period:3 years
Available Languages:English
Exam Format:Performance-based, Multiple-choice
Exam Duration:90 minutes
Related Certifications:CompTIA Data+
Real Exam Qty:Up to 90
Exam Price:USD 255
Passing Score:720 (scale of 100–900)
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
  • 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 2
  • 5.0 Data Governance, Quality, and Controls
Topic 3
  • 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 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 (Q194-Q199):

NEW QUESTION # 194
Which of the following is found in metadata?

Answer: C

Explanation:
Data lineage describes the origin, movement, and transformation history of data, which is a key component of metadata used to understand how data is created and used.


NEW QUESTION # 195
A project manager requests an unscheduled report that provides a list of clients. Which of the following frequencies is best for this report?

Answer: B

Explanation:
An ad hoc report is created on demand to satisfy a specific, unscheduled request. Since the project manager requested a report outside the regular reporting schedule, an ad hoc report is the most appropriate choice.


NEW QUESTION # 196
A data analyst is building a new report using data from a sales column. Upon review, the analyst finds that the sales date is stored as a character type. Which of the following function types should the analyst use to filter by a specific month?

Answer: A

Explanation:
Since the sales date is stored as a character (text) data type, the analyst must use string functions to extract or identify the month portion of the date value before filtering. String functions are designed to manipulate and analyze text data stored in character fields.


NEW QUESTION # 197
Which of the following AI types is the best option for time-series forecasting?

Answer: B

Explanation:
Foundational models are large AI models trained on vast amounts of data, often exhibiting strong generalization capabilities. While not specifically architected for time-series, their ability to learn complex patterns could potentially be leveraged for forecasting tasks through fine-tuning or specialized architectures built upon them.
In reality, the best AI types specifically designed for time-series forecasting include:
* Recurrent Neural Networks (RNNs), especially LSTMs and GRUs:These architectures are designed to handle sequential data and capture temporal dependencies.
* Transformer Networks:Originally developed for NLP, Transformers have shown remarkable success in time-series forecasting due to their ability to capture long-range dependencies.
* Traditional statistical models:ARIMA, Exponential Smoothing, and other statistical methods remain powerful and interpretable options for time-series analysis.
Therefore, while "foundational models" have some potential, it's important to understand that they aren't the primary or specifically designed AI type for time-series forecasting.


NEW QUESTION # 198
A data analyst creates a report that identifies the middle 50% of the collected data. Which of the following best describes the analyst's findings?

Answer: B

Explanation:
The interquartile range (IQR) represents the spread of the middle 50% of a dataset. It is calculated as the difference between the third quartile (75th percentile) and the first quartile (25th percentile), making it a useful measure of variability that is less affected by outliers.


NEW QUESTION # 199
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