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| Section | Weight | Objectives |
|---|---|---|
| Data Concepts and Environments | 20% | - Data structures and formats
|
| Data Governance, Quality, and Controls | 16% | - Security and privacy
|
| Data Mining | 25% | - Data preparation and cleansing
|
| Visualization | 15% | - Dashboard and report design
|
| Data Analysis | 24% | - Analysis tools and techniques
|
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NEW QUESTION # 22
A manager wants to know how the company's sales have improved over the last year. The manager asks a data analyst to provide a report with the required information. Which of the following approaches should the analyst use?
Answer: B
Explanation:
Key performance indicators (KPIs) are measurable metrics used to evaluate business performance over time. To determine how sales have improved during the last year, the analyst should use KPIs that track sales growth and performance trends, providing the manager with a clear assessment of progress.
NEW QUESTION # 23
A data analyst receives 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: B
Explanation:
This question falls under theData Governancedomain, focusing on identifying data quality issues. The dataset includes dates of birth and birth months, which suggests a potential inconsistency.
* Redundancy (Option A): The dataset includes both dates of birth (e.g., 1990-05-15) and birth months (e.g., May), which is redundant because the birth month can be derived from the date of birth, indicating a data quality issue.
* Equivalence (Option B): Equivalence isn't a standard data quality term in this context; it might refer to data matching, which isn't the issue here.
* Parallel (Option C): Parallel isn't a recognized data quality term; it might relate to processing, not data inconsistencies.
* Duplication (Option D): Duplication refers to identical records, but the issue here is redundant fields, not duplicate rows.
The DA0-002 Data Governance domain includes "data quality control concepts," and redundancy is a key inconsistency when the same information is stored in multiple forms unnecessarily.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.
NEW QUESTION # 24
Which of the following is a flat file data format?
Answer: A
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 # 25
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?
Answer: B
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 # 26
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: C
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 # 27
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