DA0-002 Exam Dumps - CompTIA Data+ Exam (2025)

This question pertains to the Data Concepts and Environments domain, focusing on documentation for understanding datasets. The analyst needs variable-level details (e.g., field definitions), which points to a specific type of documentation.

Data lineage (Option A): Data lineage tracks data flow and transformations, not variable definitions.

Data dictionary (Option B): A data dictionary provides detailed information about variables (e.g., name, type, description), which is exactly what the analyst needs.

Data flow diagram (Option C): A data flow diagram shows data movement, not variable-level details.

Data versioning (Option D): Data versioning tracks changes to datasets, not variable definitions.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and a data dictionary is the primary tool for variable-level information.

This question pertains to the Data Analysis domain, focusing on statistical measures. The middle 50% of a dataset refers to a specific statistical concept related to data distribution.

Interquartile range (Option A): The interquartile range (IQR) is the range between the first quartile (Q1, 25th percentile) and the third quartile (Q3, 75th percentile), representing the middle 50% of the data, which matches the description.

The difference between mode and median (Option B): This measures the spread between two central tendency metrics but doesn’t represent the middle 50% of the data.

Mean variance (Option C): Variance measures data dispersion around the mean, not the middle 50%.

Skewness from the slope (Option D): Skewness measures data asymmetry, and "slope" is irrelevant here.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and the IQR is the standard measure for the middle 50% of a dataset.

This question pertains to the Data Governance domain, focusing on troubleshooting data freshness issues in reports. The report isn’t showing the latest data despite a recent source update, indicating a potential refresh or connectivity issue.

Event log (Option A): Event logs might provide insight into errors, but they’re not the first step for checking data freshness.

User privileges (Option B): Privileges might affect access, but the user can see the report, so this isn’t the issue.

Database connection (Option C): If the database connection failed or isn’t refreshing properly, the report won’t reflect the latest data, making this the first thing to check.

Report corruption (Option D): Corruption might cause errors, but it’s less likely than a connectivity issue for this scenario.

The DA0-002 Data Governance domain includes "data quality control concepts," such as ensuring data freshness by verifying database connections.

This question falls under the Data Governance domain, focusing on tools that support data management and quality control features. The task is to identify a tool with capabilities like versioning, corruption checks, KPIs, and authentication.

Notebook (Option A): Notebooks (e.g., Jupyter) are for data analysis and coding but don’t inherently support versioning, corruption checks, or authentication.

REST API (Option B): REST APIs enable data access but don’t provide versioning or corruption checks as a primary function.

Pipeline (Option C): Data pipelines automate data workflows but don’t typically include versioning or authentication.

Source control (Option D): Source control systems (e.g., Git) support automatic versioning (tracking changes), corruption checks (integrity verification), KPIs (e.g., commit frequency), and user authentication (access control), making this the best fit.

The DA0-002 Data Governance domain includes "data quality control concepts," and source control systems provide the listed capabilities to ensure data integrity and security.

This question falls under the Data Acquisition and Preparation domain, focusing on combining data from multiple tables. The tables have different content but share a common field, indicating a join operation.

Match the records of the related columns and merge the tables (Option A): This describes a join operation, where records are matched on the common field (e.g., a key like Customer_ID) and the tables are merged, which is the most efficient method.

Create a cluster to facilitate data integration between the tables (Option B): Clustering is a machine learning technique, not a method for joining tables.

Explode both tables to identify unique values and reorder the fields in one table (Option C): Exploding is used in nested data (e.g., JSON arrays), and this approach is overly complex and unnecessary.

Append the values of the matching columns and concatenate the other data fields (Option D): Appending stacks tables vertically, and concatenation applies to text, neither of which is appropriate for joining tables with a common field.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," such as joining tables using a common field.

This question pertains to the Visualization and Reporting domain, focusing on survey design considerations, particularly for accessibility. The survey is for Paralympic athletes, who may have visual impairments, requiring specific design considerations.

Idioms (Option A): Idioms (e.g., colloquial phrases) might confuse non-native speakers, but they’re not a primary survey design concern for Paralympic athletes.

Color contrast (Option B): High color contrast ensures readability for athletes with visual impairments (e.g., color blindness), a critical accessibility consideration for Paralympic surveys.

Refresh speed (Option C): Refresh speed is relevant for dashboards, not static surveys.

Granularity (Option D): Granularity refers to data detail levels, not a survey design consideration.

The DA0-002 Visualization and Reporting domain includes "translating business requirements to form the appropriate visualization," and color contrast is a key accessibility factor in survey design for diverse audiences.

This question pertains to the Data Analysis domain, focusing on SQL queries to analyze data relationships. The task is to find customers who have not placed an order, meaning customers in the Customers table without a matching Customer_ID in the Products table.

Option A: SELECT * FROM CUSTOMERS AS C LEFT JOIN PRODUCTS AS P ON C.ID = P.Customer_ID WHERE P.Customer_ID IS NULLA LEFT JOIN includes all customers, even those without orders (where Products columns are NULL). Filtering with WHERE P.Customer_ID IS NULL selects only customers without a match in Products, correctly identifying those who haven’t ordered.

Option B: SELECT * FROM CUSTOMERS AS C INNER JOIN PRODUCTS AS P ON C.ID = C.ID WHERE COUNT(P.*) = 0An INNER JOIN only includes matching records, so it won’t return customers without orders. The join condition C.ID = C.ID is also incorrect, and COUNT requires a GROUP BY, making this invalid.

Option C: SELECT * FROM PRODUCTS AS P INNER JOIN CUSTOMERS AS C ON P.Customer_ID = C.ID WHERE (SELECT COUNT(P.*) = 0)An INNER JOIN excludes customers without orders, and the subquery syntax is incorrect (COUNT needs a GROUP BY or to be part of a HAVING clause).

Option D: SELECT * FROM PRODUCTS AS P LEFT JOIN CUSTOMERS AS C ON P.Customer_ID = C.ID WHERE P.Customer_ID IS NOT NULLThis starts with Products and joins Customers, returning only records with orders (opposite of the task), and IS NOT NULL further excludes non-ordering customers.

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods using SQL queries," and a LEFT JOIN with a NULL check is the standard method for finding non-matching records.

This question falls under the Data Concepts and Environments domain, 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.