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| Topic | Details |
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| 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.
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| Topic 2 | - 5.0 Data Governance, Quality, and Controls
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| 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.
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| 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.
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| Topic 5 | - 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.
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Free PDF 2026 CompTIA DA0-002: CompTIA Data+ Exam Latest Latest Test Sample
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CompTIA Data+ Exam Sample Questions (Q48-Q53):
NEW QUESTION # 48
A data analyst receives a flat file that includes dates. The analyst needs to calculate the number of days from the dates on the file to the current date. Which of the following is the best way to complete this task?
- A. Convert data to date format and use date functions.
- B. Transform data to a numerical value and use mathematical functions.
- C. Validate the date format with logical functions and use date functions to analyze.
- D. Use date functions to analyze the data with no conversion.
Answer: A
Explanation:
This question pertains to theData Analysisdomain, focusing on date calculations. The task is to calculate the difference between dates in a file and the current date, requiring proper date handling.
* Convert data to date format and use date functions (Option A): Flat files often store dates as strings (e.g., "2023-01-01"). Converting them to a date format (e.g., using Python's datetime or SQL's TO_DATE) allows the use of date functions (e.g., DATEDIFF) to calculate the difference to the current date, which is the best approach.
* Validate the date format with logical functions and use date functions to analyze (Option B):
Validation is unnecessary if conversion handles format issues, making this overly complex.
* Use date functions to analyze the data with no conversion (Option C): Without converting to a date format, date functions may fail if the data is stored as strings.
* Transform data to a numerical value and use mathematical functions (Option D): This is inefficient and error-prone compared to using date functions.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and converting to date format followed by date functions is the standard method for such calculations.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
NEW QUESTION # 49
A data analyst needs to create a report that anticipates the number of calls received daily. Which of the following is the best statistical method to use?
- A. Diagnostic
- B. Inferential
- C. Descriptive
- D. Predictive
Answer: D
Explanation:
Predictive analysis uses historical data and statistical models to forecast future outcomes. Since the goal is to anticipate the number of calls that will be received each day, predictive analysis is the most appropriate method.
NEW QUESTION # 50
Given the following tables:

Which of the following data privacy practices is being used to protect employees' email addresses when creating a report?
- A. Anonymization
- B. Masking
- C. Encryption
- D. Hashing
Answer: B
Explanation:
Masking hides sensitive data by replacing portions of the original value with obscured characters, such as replacing email addresses with "xxxxx@company.com" while preserving the format.
NEW QUESTION # 51
An analyst must use the same data to create reports for police patrol supervisors, the city council, and a public website. Which of the following is the best way to differentiate the reports?
- A. Accessibility
- B. Detail levels
- C. Persona type
- D. Sensitivity
Answer: C
Explanation:
This question falls under theVisualization and Reportingdomain, focusing on tailoring reports for different audiences. The same data is used for three distinct groups (supervisors, city council, public), requiring differentiation.
* Persona type (Option A): Persona types define the audience's needs and preferences (e.g., supervisors need operational details, the city council needs summaries, the public needs simplified data), making this the best way to differentiate the reports.
* Detail levels (Option B): Detail levels are a result of persona types, not the method of differentiation.
* Accessibility (Option C): Accessibility ensures access for all users (e.g., screen readers), but it's not the primary way to differentiate content.
* Sensitivity (Option D): Sensitivity determines data access (e.g., confidential vs. public), but the scenario implies all reports use the same data, just presented differently.
The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and persona types guide report differentiation for diverse audiences.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting.
NEW QUESTION # 52
An analyst must use the same data to create reports for police patrol supervisors, the city council, and a public website. Which of the following is the best way to differentiate the reports?
- A. Accessibility
- B. Detail levels
- C. Persona type
- D. Sensitivity
Answer: C
NEW QUESTION # 53
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