CompTIA Updated DA0-002 Exam Questions and Answers by casey

This question falls under the Data Acquisition and Preparation domain, focusing on combining data from multiple tables. The analyst needs specific columns from each table, suggesting a method to combine data horizontally based on relationships.

Aggregate (Option A): Aggregation (e.g., SUM, COUNT) summarizes data, not suitable for combining columns from tables.

Union (Option B): Union stacks tables vertically, requiring identical structures, but the analyst needs specific columns, likely based on relationships, not a vertical stack.

Nest (Option C): Nesting is used for hierarchical data (e.g., JSON), not for combining relational tables.

Join (Option D): A join (e.g., INNER JOIN) combines tables horizontally based on a common key, allowing the analyst to select specific columns from each table, which fits the task.

The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation," and joining tables is the best method for combining specific columns from multiple tables.

This question pertains to the Data Concepts and Environments domain, focusing on data repositories. The task is to identify a repository that stores raw, unformatted data.

Data warehouse (Option A): A data warehouse stores structured, processed data in a predefined schema, not raw data.

Data silo (Option B): A data silo is an isolated repository, often structured, not designed for raw data storage.

Data mart (Option C): A data mart is a subset of a data warehouse, also storing structured data.

Data lake (Option D): A data lake stores raw, unformatted data in its original format (structured, semi-structured, or unstructured), making it the correct choice.

The DA0-002 Data Concepts and Environments domain includes understanding "different types of databases and data repositories," and a data lake is designed for raw data storage.

This question falls under the Visualization and Reporting domain, focusing on tailoring reports for different audiences. The same data is used for three distinct groups (supervisors, city council, public), requiring differentiation.

Persona type (Option A): Persona types define the audience’s needs and preferences (e.g., supervisors need operational details, the city council needs summaries, the public needs simplified data), making this the best way to differentiate the reports.

Detail levels (Option B): Detail levels are a result of persona types, not the method of differentiation.

Accessibility (Option C): Accessibility ensures access for all users (e.g., screen readers), but it’s not the primary way to differentiate content.

Sensitivity (Option D): Sensitivity determines data access (e.g., confidential vs. public), but the scenario implies all reports use the same data, just presented differently.

The DA0-002 Visualization and Reporting domain emphasizes "translating business requirements to form the appropriate visualization," and persona types guide report differentiation for diverse audiences.

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.