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

This question falls under the Data Concepts and Environments domain, focusing on database operations like joins. A FULL JOIN combines all rows from both tables, including matches and non-matches, filling in NULLs where there’s no corresponding data.

The first table has IDs: 23525 (Frank), 11290 (Martha), 12141 (Ellen).

The second table has IDs: 23525, 11040, 11290.

A FULL JOIN includes all IDs: 23525, 11290, 12141, 11040.

23525 matches (Frank with 1234 Harding, Chicago, IL).

11290 matches (Martha with 2 Speedway, Miami, FL).

12141 has no match in the second table, so Address, City, and State are NULL.

11040 has no match in the first table, so Name and Date of birth are NULL.

Option A: Incorrect; it includes a row for Ellen with "2 Speedway," but Ellen’s ID (12141) doesn’t match any address, and 11040 is missing.

Option B: Identical to Option A, so incorrect for the same reasons.

Option C: Incorrect; it mismatches addresses (e.g., Ellen with 935 Terrace Hills, which belongs to 11040).

Option D: Correct; it includes all IDs, with NULLs for non-matching rows (Ellen has no address, and 11040 has no name).

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," such as performing joins in relational databases.

This question is part of the Data Concepts and Environments domain, focusing on the purpose of data documentation tools like data dictionaries and metadata. The question compares their uses.

To gather information and resources about the data (Option A): This is too vague and not specific to a data dictionary’s purpose.

To find the content and specific attributes for a dataset (Option B): A data dictionary provides detailed definitions of data elements (e.g., field names, types, descriptions), which is more specific than metadata, which often includes broader information like creation date or source.

To find a summary of basic information about the dataset (Option C): This better describes metadata, which provides high-level summaries, not detailed attributes.

To gather information about the availability of the data (Option D): Neither a data dictionary nor metadata typically focuses on data availability.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," and a data dictionary is specifically used to find detailed attributes of a dataset.

This question falls under the Visualization and Reporting domain, focusing on selecting the appropriate visualization for a specific data trend. The task is to show sales trends over time (year over year).

Line (Option A): Line charts are ideal for displaying trends over time, such as year-over-year sales, as they clearly show changes and patterns across a continuous time axis.

Donut (Option B): Donut charts show proportions or percentages of a whole, not suitable for time-based trends.

Bubble (Option C): Bubble charts display three dimensions of data (e.g., size, x-axis, y-axis), not ideal for simple time trends.

Hierarchy (Option D): Hierarchy charts (e.g., treemaps) show nested relationships, not time-based trends.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and a line chart is best for time-series trends.

This question pertains to the Data Concepts and Environments domain, focusing on types of server infrastructure. The task is to identify an infrastructure where a company owns and maintains all servers.

Private (Option A): A private infrastructure (often on-premises) means the company owns and maintains its own servers, typically in a private data center, which matches the requirement.

Cloud (Option B): Cloud infrastructure is managed by third-party providers, not owned by the company.

Hybrid (Option C): Hybrid combines on-premises and cloud, so not all servers are owned by the company.

Public (Option D): Public infrastructure is a cloud model shared across multiple organizations, not owned by the company.

The DA0-002 Data Concepts and Environments domain includes understanding "data environments," and a private infrastructure requires the company to purchase and maintain its own servers.

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.

This question falls under the Data Concepts and Environments domain of CompTIA Data+ DA0-002, focusing on selecting appropriate data types for a given dataset. The Value column contains decimal numbers (1.5, 24.456, 113), requiring a data type that supports such values.

Double (Option A): Double is a floating-point data type that supports decimals with higher precision than Float, but it’s often overkill for typical datasets unless very high precision is needed, which isn’t indicated here.

Float (Option B): Float is a floating-point data type that supports decimal numbers (e.g., 1.5, 24.456) and is commonly used for such values in databases, making it the best choice.

Boolean (Option C): Boolean is for true/false values, not numeric data.

Integer (Option D): Integer is for whole numbers, but the values (e.g., 1.5, 24.456) have decimals, so Integer is not suitable.

The DA0-002 Data Concepts and Environments domain includes understanding "data schemas and dimensions," such as selecting data types like Float for decimal numeric values.

This question pertains to the Data Acquisition and Preparation domain, focusing on combining and deduplicating data using SQL. The task is to remove duplicates between two tables, meaning the analyst needs a unique set of records from both.

SELECT * FROM Employees UNION ALL SELECT * FROM Managers (Option A): UNION ALL combines all rows from both tables, including duplicates, which doesn’t meet the requirement.

SELECT * FROM Employees UNION SELECT * FROM Managers (Option B): UNION combines rows from both tables and automatically removes duplicates, providing a unique set of records, which fits the task.

SELECT * FROM Employees JOIN SELECT * FROM Managers (Option C): This syntax is incorrect; a JOIN requires an ON clause, and it wouldn’t remove duplicates.

SELECT * FROM Employees CROSS JOIN SELECT * FROM Managers (Option D): A CROSS JOIN creates a Cartesian product, resulting in all possible combinations, not removing duplicates.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and UNION is the correct SQL operation for combining tables while removing duplicates.

This question falls under the Data Acquisition and Preparation domain, focusing on modifying historical data. The task is to add new classification codes to existing data, which involves adding new rows or columns.

Append (Option A): Appending adds new rows to a dataset, which is suitable if the classification codes are new records (e.g., a new table of codes to combine with historical data). If the codes are a new column, a join or update might be used, but append fits the context of adding new data.

Binning (Option B): Binning groups data into categories, not suitable for adding classification codes.

Parsing (Option C): Parsing breaks down data (e.g., splitting strings), not relevant for adding codes.

Union (Option D): Union stacks tables with identical structures, but the task involves adding new data (codes) to historical data, not combining identical tables.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and appending is a common technique for adding new data to historical datasets.