Free Practice Questions for CompTIA DA0-002 Exam

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.

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 pertains to the Data Governance domain, focusing on data privacy and security. The task is to share a spreadsheet with sensitive information while protecting personal details.

Using a UUID in the data file (Option A): A UUID (Universally Unique Identifier) can anonymize records, but if other PII (e.g., names) remains, personal details are still exposed.

Redacting all PII (Option B): Redacting personally identifiable information (PII) removes sensitive details (e.g., names, addresses), ensuring personal information isn’t revealed while sharing the spreadsheet.

Adding access controls to the ID column (Option C): Access controls limit who can view the data, but the question focuses on the spreadsheet content itself, not access.

Encrypting the spreadsheet (Option D): Encryption protects the file during transmission, but once opened, personal details are still visible unless redacted.

The DA0-002 Data Governance domain includes "data privacy concepts," and redacting PII is the most direct method to protect personal details in a shared spreadsheet.

This question pertains to the Data Governance domain, focusing on data quality and consistency in survey design. Requiring all questions to have an answer ensures a specific type of data quality.

Missing values (Option A): Requiring answers prevents missing values (NULLs or blanks) in the survey responses, which is the primary inconsistency this setting addresses.

Duplication (Option B): Duplication refers to repeated records, not prevented by requiring answers.

Data corruption (Option C): Data corruption involves damaged or altered data, not related to missing answers.

Completeness (Option D): Completeness is the concept of having all necessary data, but "missing values" is the specific inconsistency prevented here.

The DA0-002 Data Governance domain includes "data quality control concepts," and preventing missing values ensures data integrity in survey responses.

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 types of server environments. The servers are located at the company’s headquarters, indicating a specific deployment model.

Cloud (Option A): Cloud environments are hosted off-site by third-party providers, not at headquarters.

On-premises (Option B): On-premises environments are located at the company’s physical location (e.g., headquarters), which matches the scenario.

Public (Option C): Public environments are cloud-based and shared across multiple organizations, not located at headquarters.

Hybrid (Option D): Hybrid environments combine on-premises and cloud, but the scenario specifies all servers are at headquarters.

The DA0-002 Data Concepts and Environments domain includes understanding "data environments," and on-premises describes a server environment located at the company’s site.

This question falls under the Data Governance domain of CompTIA Data+ DA0-002, which includes understanding compliance frameworks for data audits, especially for customer data. The scenario involves a data audit of customer invoice data, which likely contains personal information, making privacy regulations relevant.

PCI DSS (Option A): PCI DSS (Payment Card Industry Data Security Standard) applies specifically to payment card data, not general customer invoice data unless credit card details are involved, which isn’t specified.

GDPR (Option B): GDPR (General Data Protection Regulation) is a comprehensive privacy regulation for handling personal data of EU citizens, including customer invoice data, which may contain PII like names and addresses. It’s the most relevant for a company-wide data audit.

ISO (Option C): ISO standards (e.g., ISO 27001) relate to information security management but are not specific to customer data privacy audits.

PII (Option D): PII (Personally Identifiable Information) is a concept, not a framework or tool for conducting an audit.

The DA0-002 Data Governance domain emphasizes "identifying PII and data privacy concepts," and GDPR is the most appropriate framework for auditing customer data to ensure compliance with privacy laws.

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.