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Get All CompTIA Data+ Exam (2025) Exam Questions with Validated Answers
| Vendor: | CompTIA |
|---|---|
| Exam Code: | DA0-002 |
| Exam Name: | CompTIA Data+ Exam (2025) |
| Exam Questions: | 121 |
| Last Updated: | September 25, 2026 |
| Related Certifications: | CompTIA Data+ |
| Exam Tags: | Data analysis certifications Entry-level to Intermediate CompTIA Data AnalystsReporting Analysts |
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A data analyst pulls a table similar to the following one:
ID Type TypeID Phone
1 Full Time Full Time 1 Mobile
2 Part Time Part Time 2 Work
3 Full Time Full Time 3 Mobile
Which of the following best explains the data issue with TypeID?
This question is part of the Data Concepts and Environments domain, focusing on identifying data quality issues. The table shows Type and TypeID columns, where TypeID seems to repeat information from Type with an additional identifier.
Redundancy (Option A): The TypeID column (e.g., 'Full Time 1') redundantly includes the Type value ('Full Time') with an extra identifier, which is unnecessary and could be simplified by using a numeric ID instead.
Outlier (Option B): Outliers are data points that deviate significantly, which isn't applicable here.
Missing data (Option C): There are no missing values in the table.
Duplication (Option D): Duplication refers to identical rows, but the rows here are unique; the issue is with the column content.
The DA0-002 Data Concepts and Environments domain includes understanding 'data schemas and dimensions,' and redundancy is a common data quality issue in schema design.
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A data analyst needs to identify outliers from a given dataset. Which of the following visualizations is the best way to identify outliers?
This question falls under the Visualization and Reporting domain, focusing on selecting the appropriate visualization to identify outliers in a dataset.
Box plot (Option A): A box plot displays the distribution of data, including the median, quartiles, and outliers (data points beyond the whiskers), making it the best choice for identifying outliers.
Scatter plot (Option B): A scatter plot shows relationships between two variables, and while outliers may be visible, it's not specifically designed for outlier detection.
Gantt chart (Option C): Gantt charts are for project scheduling, not suitable for outlier identification.
Waterfall chart (Option D): Waterfall charts show cumulative changes (e.g., financial contributions), not designed for outlier detection.
The DA0-002 Visualization and Reporting domain emphasizes 'translating business requirements to form the appropriate visualization,' and a box plot is the standard visualization for identifying outliers.
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A data analyst creates a report, and some of the fields are empty. Which of the following conditions should the analyst add to a query to provide a list of all the records with empty fields?
This question falls under the Data Analysis domain, focusing on SQL queries to identify data issues. The task is to find records with empty fields, which in SQL means NULL values.
WHERE [ColumnName] = NULL (Option A): In SQL, NULL cannot be compared using '='; this syntax is incorrect.
WHERE [ColumnName] IS NULL (Option B): This is the correct SQL syntax to identify NULL values, which represent empty fields.
WHERE [ColumnName] IS NOT NULL (Option C): This finds non-empty fields, the opposite of the requirement.
WHERE [ColumnName] = 'NULL' (Option D): This checks for the string 'NULL,' not a true NULL value, which is incorrect.
The DA0-002 Data Analysis domain includes 'applying the appropriate descriptive statistical methods using SQL queries,' such as identifying NULL values with IS NULL.
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The following SQL code returns an error in the program console:
SELECT firstName, lastName, SUM(income)
FROM companyRoster
SORT BY lastName, income
Which of the following changes allows this SQL code to run?
This question falls under the Data Analysis domain, focusing on SQL query correction. The query uses an aggregate function (SUM) but has two issues: it uses 'SORT BY' (incorrect syntax) and lacks a GROUP BY clause for non-aggregated columns.
The query selects firstName, lastName, and SUM(income), but firstName and lastName are not aggregated, requiring a GROUP BY clause.
'SORT BY' is incorrect; the correct syntax is 'ORDER BY.'
Option A: SELECT firstName, lastName, SUM(income) FROM companyRoster HAVING SUM(income) > 10000000
This adds a HAVING clause but doesn't fix the GROUP BY issue, so it's still invalid.
Option B: SELECT firstName, lastName, SUM(income) FROM companyRoster GROUP BY firstName, lastName
This adds the required GROUP BY clause for firstName and lastName, fixing the aggregation error. While it removes the ORDER BY, the query will run without it, addressing the primary error.
Option C: SELECT firstName, lastName, SUM(income) FROM companyRoster ORDER BY firstName, income
This fixes 'SORT BY' to 'ORDER BY' but doesn't address the missing GROUP BY, so the query remains invalid.
Option D: SELECT firstName, lastName, SUM(income) FROM companyRoster
This removes the ORDER BY but still lacks the GROUP BY clause, making it invalid.
The DA0-002 Data Analysis domain includes 'applying the appropriate descriptive statistical methods using SQL queries,' and adding GROUP BY fixes the aggregation error, allowing the query to run.
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An analyst needs to produce a final dataset using the following tables:
CourseID
SectionNumber
StudentID
MATH1000
1
10009
MATH1000
2
10007
PSYC1500
1
10009
PSYC1500
1
10015
StudentID
FirstName
LastName
10009
Jane
Smith
10007
John
Doe
10015
Robert
Roe
The expected output should be formatted as follows:
| CourseID | SectionNumber | StudentID | FirstName | LastName |
Which of the following actions is the best way to produce the requested output?
This question falls under the Data Acquisition and Preparation domain, focusing on combining tables to produce a dataset. The task requires combining the Courses and Students tables to include student names with course details, based on the StudentID.
Aggregate (Option A): Aggregation (e.g., SUM, COUNT) summarizes data, not suitable for combining tables to include names.
Join (Option B): A join operation (e.g., INNER JOIN on StudentID) combines the tables, matching records to produce the requested output with CourseID, SectionNumber, StudentID, FirstName, and LastName.
Group (Option C): Grouping is used for aggregation (e.g., GROUP BY in SQL), not for combining tables.
Filter (Option D): Filtering selects specific rows, not relevant for combining tables.
The DA0-002 Data Acquisition and Preparation domain includes 'executing data manipulation,' such as joining tables to create a unified dataset.
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