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CompTIA DA0-002 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 5.0 Data Governance, Quality, and Controls
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 (Q174-Q179):

NEW QUESTION # 174
After a database upgrade, a report using datetime calculations does not complete due to an arithmetic overflow error. Which of the following is the best way for a data analyst to troubleshoot the issue?

Answer: A

Explanation:
Arithmetic overflow in datetime calculations typically occurs when the data type cannot handle the computed value, so verifying that the columns use appropriate datetime or numeric data types is the most effective way to troubleshoot the issue.


NEW QUESTION # 175
A user needs a report that shows the main causes of customer churn rate in a three-year period. Which of the following methods provides this information?

Answer: D

Explanation:
This question falls under theData Analysisdomain, focusing on analytical methods for reporting. The task is to identify the causes of customer churn over three years, which involves analyzing historical data.
* Inferential (Option A): Inferential statistics make predictions or generalizations about a population, not focused on identifying causes in historical data.
* Descriptive (Option B): Descriptive analytics summarizes historical data to identify patterns and causes (e.g., reasons for churn), which fits the task.
* Prescriptive (Option C): Prescriptive analytics provides recommendations, which goes beyond identifying causes.
* Predictive (Option D): Predictive analytics forecasts future outcomes, not focused on historical causes.
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and descriptive analytics is best for identifying causes in historical data.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.


NEW QUESTION # 176
A data analyst is reviewing a new data source that contains employee IDs, job titles, dates of birth, addresses, years of service, and employees' birth months. Which of the following inconsistencies should the analyst identify?

Answer: A

Explanation:
Redundancy occurs when the same information can be derived from other data already present in the dataset. Since the data source contains both dates of birth and employees' birth months, the birth month is redundant because it can be extracted directly from the date of birth.


NEW QUESTION # 177
A data analyst wants to isolate a serial number from a string of code. Which of the following data transformation techniques should the analyst use?

Answer: B

Explanation:
Parsing separates a string into its individual components based on a defined pattern or delimiter.
To isolate a serial number from a larger string of code, the analyst would use parsing to extract the specific portion containing the serial number.


NEW QUESTION # 178
Which of the following best describes an assessment a data analyst would use to validate that the number of records in a dataset matches the expected results?

Answer: C


NEW QUESTION # 179
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