DA0-002 Exam dumps PDF | Exam-Dumps.net (Page: 2)

QUESTION: 9

A company has a document that includes the names of key metrics and the standard for how those metrics are calculated company-wide.
Which of the following describes this documentation?

Data dictionary
Data explainability report
Data lineage
Data flow diagram

Answer(s): A

Explanation:

This question falls under the Data Concepts and Environments domain, which involves understanding documentation types related to data management. The document describes key metrics and their calculation standards, which points to a specific type of metadata documentation.

Data dictionary (Option A): A data dictionary defines data elements, including metrics, their meanings, and calculation methods, ensuring consistency across the organization. This matches the description.

Data explainability report (Option B): This term is more associated with AI/ML, explaining model decisions, not metric definitions.

Data lineage (Option C): Data lineage tracks the flow of data through systems, not metric definitions or calculations.

Data flow diagram (Option D): A data flow diagram visualizes data processes, not metric standards.

The DA0-002 Data Concepts and Environments domain includes understanding "basic concepts of data schemas and dimensions" , and a data dictionary is a foundational tool for defining metrics.

Reference:

CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 1.0 Data Concepts and Environments

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QUESTION: 10

A data analyst needs to create and deliver a dashboard that displays the company's financial transactions as they are updated.
Which of the following delivery methods should the analyst consider? (Select two).

Real-time
Snapshot
Dynamic
Static
Ad hoc
Time series

Answer(s): A,C

Explanation:

This question is part of the Visualization and Reporting domain, focusing on delivery methods for dashboards. The requirement for displaying financial transactions "as they are updated" implies a need for real-time updates and interactivity, which narrows down the options.

Real-time (Option A): Real-time delivery ensures the dashboard reflects the latest data as transactions are updated, meeting the requirement.

Snapshot (Option B): A snapshot provides a static view at a specific point, not suitable for ongoing updates.

Dynamic (Option C): A dynamic dashboard allows for interactivity and can be updated as data changes, complementing real-time delivery.

Static (Option D): Static dashboards don't update automatically, making this incorrect.

Ad hoc (Option E): Ad hoc delivery is for one-time reports, not ongoing updates.

Time series (Option F): Time series refers to a data type or visualization, not a delivery method.

The DA0-002 Visualization and Reporting domain includes understanding "the appropriate visualization in the form of a report or dashboard" with delivery methods Real-time and dynamic methods best support the need for updated financial transaction dashboards.

Reference:

CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 4.0 Visualization and Reporting

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QUESTION: 11

A data analyst receives a request for the current employee head count and runs the following SQL statement:

SELECT COUNT(EMPLOYEE_ID) FROM JOBS

The returned head count is higher than expected because employees can have multiple jobs.
Which of the following should return an accurate employee head count?

SELECT JOB_TYPE, COUNT DISTINCT(EMPLOYEE_ID) FROM JOBS
SELECT DISTINCT COUNT(EMPLOYEE_ID) FROM JOBS
SELECT JOB_TYPE, COUNT(DISTINCT EMPLOYEE_ID) FROM JOBS
SELECT COUNT(DISTINCT EMPLOYEE_ID) FROM JOBS

Answer(s): D

Explanation:

This question falls under the Data Analysis domain of CompTIA Data+ DA0-002, which involves using SQL queries to analyze data and address issues like duplicates in datasets. The issue here is that the initial query counts all instances of EMPLOYEE_ID in the JOBS table, but employees can have multiple jobs, leading to an inflated head count. The goal is to count unique employees.

SELECT JOB_TYPE, COUNT DISTINCT(EMPLOYEE_ID) FROM JOBS (Option A): This query is syntactically incorrect because COUNT DISTINCT(EMPLOYEE_ID) should use parentheses as COUNT(DISTINCT EMPLOYEE_ID). It also groups by JOB_TYPE, which is unnecessary for a total head count.

SELECT DISTINCT COUNT(EMPLOYEE_ID) FROM JOBS (Option B): This query is incorrect because DISTINCT applies to the rows returned, not the COUNT function directly. It doesn't address the duplicate EMPLOYEE_ID issue.

SELECT JOB_TYPE, COUNT(DISTINCT EMPLOYEE_ID) FROM JOBS (Option C): While this query correctly uses COUNT(DISTINCT EMPLOYEE_ID) to count unique employees, grouping by JOB_TYPE breaks the count into separate groups, which isn't required for a total head count.

SELECT COUNT(DISTINCT EMPLOYEE_ID) FROM JOBS (Option D): This query correctly counts only unique EMPLOYEE_IDs by using the DISTINCT keyword within the COUNT function, providing an accurate total head count without grouping.

The DA0-002 Data Analysis domain emphasizes "given a scenario, applying the appropriate descriptive statistical methods using SQL queries," which includes handling duplicates with functions like COUNT(DISTINCT). Option D is the most direct and accurate method for a total unique head count.

Reference:

CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.

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QUESTION: 12

A data analyst created a dashboard to illustrate the traffic volume and mean response time for a call center. The traffic data is current, but the mean response time has not updated for more than an hour.
Which of the following is the best way to verify the data's freshness?

Refactoring the code base
Testing for network connectivity issues
Checking the last time the calculation script ran
Determining the number of calls with no timestamps

Answer(s): C

Explanation:

This question pertains to the Data Governance domain, which in DA0-002 includes ensuring data quality and freshness, especially in dashboards. The issue is that the mean response time isn't updating, while traffic data is current, indicating a potential issue with the data refresh process for the response time metric.

Refactoring the code base (Option A): Refactoring might improve long-term performance but doesn't directly address verifying data freshness.

Testing for network connectivity issues (Option B): Network issues could cause delays, but since traffic data is updating, connectivity is likely not the issue.

Checking the last time the calculation script ran (Option C): Mean response time is a calculated metric, likely derived from a script. Checking when the script last ran directly verifies if the data refresh process failed, making this the best approach.

Determining the number of calls with no timestamps (Option D): Missing timestamps might indicate data quality issues, but it doesn't directly verify why the mean response time isn't updating.

The DA0-002 Data Governance domain focuses on "data quality control concepts," including ensuring data freshness in reporting. Checking the script's last run time aligns with this objective.

Reference:

CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.

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QUESTION: 13

Which of the following pieces of information, if made public, results in a data privacy violation?

Gender
Driver's license
Age
Employment status

Answer(s): B

Explanation:

This question falls under the Data Governance domain, which in DA0-002 includes understanding data privacy and compliance with regulations like GDPR. The question asks which piece of information, if made public, constitutes a privacy violation, meaning it must be personally identifiable information (PII).

Gender (Option A): Gender is not typically considered PII on its own, as it's not uniquely identifiable.

Driver's license (Option B): A driver's license number is PII because it uniquely identifies an individual and can be linked to other personal information, such as name and address. Making it public violates privacy regulations.

Age (Option C): Age alone isn't PII, as it's not uniquely identifiable.

Employment status (Option D): Employment status (e.g., employed, unemployed) isn't PII, as it doesn't uniquely identify an individual.

The DA0-002 Data Governance domain includes "identifying PII and data privacy concepts," and a driver's license is a clear example of PII that, if exposed, results in a privacy violation.

Reference:

CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.

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QUESTION: 14

A data analyst receives four files that need to be unified into a single spreadsheet for further analysis. All of the files have the same structure, number of columns, and field names, but each file contains different values.
Which of the following methods will help the analyst convert the files into a single spreadsheet?

Merging
Appending
Parsing
Clustering

Answer(s): B

Explanation:

This question is part of the Data Acquisition and Preparation domain, which involves combining data from multiple sources. The files have the same structure but different values, meaning they need to be stacked vertically into one dataset.

Merging (Option A): Merging typically involves joining datasets on a common key (e.g., a customer ID), which isn't indicated here since the files only differ in values, not keys.

Appending (Option B): Appending stacks datasets vertically, combining rows from files with the same structure into a single dataset, which matches the scenario.

Parsing (Option C): Parsing involves breaking down data (e.g., splitting text), not combining files.

Clustering (Option D): Clustering is a machine learning technique for grouping similar data points, not for combining files.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," such as appending datasets with identical structures.

Reference:

CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation.

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QUESTION: 15

A data analyst team needs to segment customers based on customer spending behavior. Given one million rows of data like the information in the following sales order table:

Customer_ID Region Amount_spent Product_category Quantity_of_items

00123 East 20000 Baby 4

00124 West 30000 Home 6

00125 South 40000 Garden 7

00126 North 50000 Furniture 8

00127 East 60000 Baby 10

Which of the following techniques should the team use for this task?

Standardization
Concatenate
Binning
Appending

Answer(s): C

Explanation:

This question falls under the Data Analysis domain, focusing on techniques for segmenting data. The task is to segment customers based on spending behavior, which involves grouping numerical data (Amount_spent) into categories.

Standardization (Option A): Standardization scales numerical data to a common range (e.g., z-scores), but it doesn't segment customers into groups.

Concatenate (Option B): Concatenation combines text fields, not numerical data for segmentation.

Binning (Option C): Binning involves grouping numerical data into discrete intervals (e.g., low, medium, high spending), which is ideal for segmenting customers based on spending behavior.

Appending (Option D): Appending combines datasets vertically, not relevant for segmentation.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and binning is a common method for segmenting numerical data like spending amounts.

Reference:

CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.

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QUESTION: 16

A data analyst receives a notification that a customized report is taking too long to load. After reviewing the system, the analyst does not find technical or operational issues.
Which of the following should the analyst try next?

Check that the appropriate filters are applied.
Check data source connections.
Check for data structure changes in the report.
Check whether other peers have the same issue.

Answer(s): A

Explanation:

This question pertains to the Data Governance domain, focusing on data quality and report performance optimization. The report is slow despite no technical issues, suggesting a data-related inefficiency.

Check that the appropriate filters are applied (Option A): Applying filters reduces the dataset size by excluding irrelevant data, improving report performance. This is a logical next step after ruling out technical issues.

Check data source connections (Option B): The analyst already reviewed the system and found no operational issues, so connectivity is likely not the problem.

Check for data structure changes in the report (Option C): While possible, this is a deeper investigation step and less likely to be the immediate cause of slowness.

Check whether other peers have the same issue (Option D): This might confirm the issue's scope but doesn't directly address the performance problem.

The DA0-002 Data Governance domain emphasizes "data quality control concepts," including optimizing report performance through techniques like filtering.

Reference:

CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.

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CompTIA DA0-002 Exam (page: 2) CompTIA Data+ (2025) Updated on: 31-Mar-2026

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CompTIA DA0-002 Exam (page: 2)
CompTIA Data+ (2025)
Updated on: 31-Mar-2026