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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
  • 5.0 Data Governance, Quality, and Controls
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.

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CompTIA Data+ Exam Sample Questions (Q193-Q198):

NEW QUESTION # 193
Which of the following is a flat file data format?

Answer: D

Explanation:
CSV (Comma-Separated Values) is a flat file format storing data in plain text, organized in rows and columns without hierarchical structure.


NEW QUESTION # 194
Which of the following tables holds relational keys and numeric values?

Answer: C

Explanation:
This question falls under theData Concepts and Environmentsdomain, focusing on understanding table types in data warehousing. The task is to identify a table that holds relational keys and numeric values, typically used in a star schema.
* Fact (Option A): Fact tables in a star schema store quantitative data (numeric values, e.g., sales amounts) and foreign keys (relational keys) linking to dimension tables, making this the correct choice.
* Graph (Option B): Graph tables are used in graph databases for relationships (e.g., nodes, edges), not typically for relational keys and numeric values in a traditional sense.
* Dimensional (Option C): Dimension tables store descriptive attributes (e.g., product names) and primary keys, not typically numeric measures.
* Transactional (Option D): Transactional tables are used in OLTP systems and may contain numeric values, but they're not specifically designed for relational keys in a data warehousing context.
The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and fact tables are designed to hold relational keys and numeric values in a data warehouse.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments.


NEW QUESTION # 195
A data analyst creates a report about the time that elapses between sequential events. The data set has several rows with negative integers in the elapsed time column. Which of the following is the best way to address this issue?

Answer: D

Explanation:
The presence of negative elapsed times suggests a potential data quality issue, such as incorrect event ordering, timestamp errors, or data entry problems. The best approach is to validate the data set before analysis to determine the cause of the negative values and ensure the data is accurate and reliable.


NEW QUESTION # 196
Which of the following best describes the reason an analyst would reference a data dictionary versus a source's metadata?

Answer: A

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 # 197
A data analyst pulls a table similar to the following one:

Which of the following best explains the data issue with TypeID?

Answer: A

Explanation:
The TypeID column contains information that is already present in the Type column. Values such as "Full Time 1", "Part Time 2", and "Full Time 3" repeat the employment type while adding an identifier, resulting in unnecessary duplication of information. This is an example of redundancy because the same data is being stored in multiple places.


NEW QUESTION # 198
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