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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 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 3
  • 5.0 Data Governance, Quality, and Controls
Topic 4
  • 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 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.

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CompTIA Data+ Exam (2025) Sample Questions (Q48-Q53):

NEW QUESTION # 48
Which of the following best describes the semi-structured data that is gathered when web scraping?

Answer: B

Explanation:
This question pertains to theData Acquisition and Preparationdomain, which in DA0-002 includes understanding data acquisition concepts and the types of data gathered from varioussources, such as web scraping. Web scraping involves extracting data from websites, and the data gathered is often semi-structured, meaning it has some organizational structure but isn't fully relational like a database table.
* JSON (Option A): JSON (JavaScript Object Notation) is a semi-structured data format commonly used in web applications. Web scraping often retrieves data in JSON format via APIs or embedded scripts, as it's lightweight and structured with key-value pairs, making it ideal for semi-structured data.
* CSV (Option B): CSV (Comma-Separated Values) is a structured format typically used for tabular data. It's not commonly the direct output of web scraping, though scraped data might be converted to CSV later.
* CSS (Option C): CSS (Cascading Style Sheets) is used for styling web pages and isn't a data format, making it irrelevant for describing scraped data.
* HTML (Option D): HTML (HyperText Markup Language) is the structure of web pages and is often the raw format scraped during web scraping. While HTML is semi-structured due to its tag-based hierarchy, it's primarily a markup language, not a data format, and the actual data extracted is often parsed into formats like JSON.
The DA0-002 Data Acquisition and Preparation domain aligns with the DA0-001 focus on "data acquisition concepts" (web ID: 14), which includes identifying formats like JSON as semi-structured data commonly acquired through web scraping. JSON is the best fit here due to its prevalence in web data exchange.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation


NEW QUESTION # 49
A data analyst encounters an issue with new software and a code that they are using. The analyst includes print statements in the code to try to identify the issue, without success. An informal peer review of the code also produces the same result. The analyst confirms that the software is updated to the latest version and compatible with the code. Which of the following troubleshooting steps should the analyst take next?

Answer: B

Explanation:
This question pertains to theData Governancedomain, focusing on troubleshooting andmaintaining data quality in software processes. The analyst has already tried basic debugging and confirmed compatibility, so the next step involves seeking external resources.
* Use the old software and preexisting code, since both were functional (Option A): Reverting to old software avoids solving the issue and may introduce other risks (e.g., security vulnerabilities).
* Contact the IT department and inform them that the software has a bug (Option B): Assuming a bug without further investigation is premature, especially since compatibility was confirmed.
* Escalate to the department manager and ask for assistance (Option C): Escalation is a later step after exhausting technical troubleshooting options.
* Research the issue online and see if a solution is available (Option D): Researching online (e.g., forums, documentation) is a logical next step to find solutions or identify known issues, especially after local debugging fails.
The DA0-002 Data Governance domain includes "data quality control concepts," and researching online is a standard troubleshooting step to maintain data process integrity.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.


NEW QUESTION # 50
Which of the following data repositories should a company use when structured data about the whole company needs to be stored in a predefined data structure?

Answer: B

Explanation:
This question pertains to theData Concepts and Environmentsdomain, focusing on selecting the appropriate repository for structured data across an entire company. The requirement for a predefined structure narrows the options.
* Data mart (Option A): A data mart stores structured data for a specific business area (e.g., sales), not the whole company.
* Data warehouse (Option B): A data warehouse is designed to store structured data from across the entire company in a predefined schema, optimized for analytics and reporting.
* Data silo (Option C): A data silo is an isolated repository, often structured, but not designed for company-wide integration.
* Data lake (Option D): A data lake stores raw data (structured and unstructured) without a predefined structure, not suitable for this requirement.
The DA0-002 Data Concepts and Environments domain includes understanding "different types of databases and data repositories," and a data warehouse is ideal for company-wide structured data.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.


NEW QUESTION # 51
A data analyst receives an email from the IT department about renewing the company password, and the analyst follows the password reset link as required. Later in the week, the analyst receives the following notification when running a recurring analysis that connects to the database:
Log-in failed for user '<username>'
Which of the following is most likely the reason for this issue?

Answer: B

Explanation:
This question falls under theData Governancedomain, focusing on data access and security troubleshooting.
The analyst reset their password, but the recurring analysis failed to log in, indicating a mismatch.
* The company changed its database authentication method (Option A): This would affect all users, not just the analyst, and there's no indication of a broader change.
* The password expiration process locked the account (Option B): The analyst reset the password as required, so the account isn't likely locked due to expiration.
* The analyst did not change the password used to launch the report (Option C): Recurring analyses often use stored credentials. If the analyst updated their password but didn't update the stored credentials for the analysis, the login would fail, making this the most likely reason.
* The company is experiencing issues with password replication (Option D): This is possible but less likely without evidence of broader system issues.
The DA0-002 Data Governance domain includes "data privacy concepts," and ensuring stored credentials match updated passwords is a common issue in recurring analyses.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.


NEW QUESTION # 52
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

Explanation:
This question falls under theData Governancedomain, focusing on troubleshooting data freshness issues in dashboards. The dashboard shows stale data despite a successful refresh, and the server migration updated naming conventions, suggesting a potential mismatch.
* Confirm the dashboard is pointed to the newest dataset (Option A): The server migration updated dataset naming conventions, so the dashboard might still be pointing to an old dataset name, causing stale data. Confirming the dataset connection is the first step.
* Filter the data in the dashboard (Option B): Filtering might adjust the view but doesn't address the root cause of stale data.
* Escalate user permissions on the server (Option C): Permissions issues would likely prevent access, not cause stale data, especially since the dataset refreshed successfully.
* Verify that the dashboard subscription is not expired (Option D): An expired subscription might prevent access, but the dashboard is accessible, just showing stale data.
The DA0-002 Data Governance domain includes "data quality control concepts," such as ensuring dashboards connect to the correct, updated datasets after changes like server migrations.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.


NEW QUESTION # 53
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