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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
  • 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 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.
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.

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CompTIA Data+ Exam Sample Questions (Q148-Q153):

NEW QUESTION # 148
Which of the following best describes the reason an analyst would reference a data dictionary versus a source's metadata?

Answer: D

Explanation:
A data dictionary provides detailed definitions of fields, including their meaning, structure, and attributes, which helps an analyst understand the specific contents of a data set.


NEW QUESTION # 149
Which of the following explains the purpose of UAT?

Answer: B

Explanation:
User Acceptance Testing (UAT) ensures the application satisfies business requirements and user needs by validating that it works as intended in real-world scenarios before final release.


NEW QUESTION # 150
A data analyst is modeling a new data warehouse to analyze sales. One of the requirements is to analyze sales by region. Which of the following best describes the region table?

Answer: D

Explanation:
A region table provides descriptive attributes used to categorize and analyze facts such as sales, which makes it a dimension table in a data warehouse schema.


NEW QUESTION # 151
Which of the following file formats uses curly brackets to surround objects?

Answer: A

Explanation:
JSON (JavaScript Object Notation) represents objects using curly brackets { }. Each object contains key-value pairs enclosed within these brackets, making JSON a common format for exchanging structured data between systems and applications.


NEW QUESTION # 152
Which of the following supports capabilities such as automatic versioning, corruption checks, KPIs, and user authentication?

Answer: B

Explanation:
This question falls under theData Governancedomain, focusing on tools that support data management and quality control features. The task is to identify a tool with capabilities like versioning, corruption checks, KPIs, and authentication.
* Notebook (Option A): Notebooks (e.g., Jupyter) are for data analysis and coding but don't inherently support versioning, corruption checks, or authentication.
* REST API (Option B): REST APIs enable data access but don't provide versioning or corruption checks as a primary function.
* Pipeline (Option C): Data pipelines automate data workflows but don't typically include versioning or authentication.
* Source control (Option D): Source control systems (e.g., Git) support automatic versioning (tracking changes), corruption checks (integrity verification), KPIs (e.g., commit frequency), and user authentication (access control), making this the best fit.
The DA0-002 Data Governance domain includes "data quality control concepts," and source control systems provide the listed capabilities to ensure data integrity and security.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.


NEW QUESTION # 153
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