DA0-002 Exam Dumps and Practice Questions

This question pertains to the Data Concepts and Environments domain, focusing on the purpose of data lineage. Data lineage involves tracking the lifecycle of data.

To see the steps and path of data flow through different systems (Option A): This describes a data flow diagram, not data lineage, which focuses on transformations rather than just flow.

To better understand the granularity of data variable relationships (Option B): This relates to data modeling, not the purpose of data lineage.

To track data transformations from acquisition through reporting (Option C): Data lineage tracks the journey of data, including transformations (e.g., cleaning, aggregation) from its source to its final use in reporting, which is its primary purpose.

To look up data definitions, ensuring consistent use across business units (Option D): This describes a data dictionary, not data lineage.

The DA0-002 Data Concepts and Environments domain includes understanding 'data schemas and dimensions,' and data lineage specifically tracks transformations across the data lifecycle.

This question falls under the Data Analysis domain, focusing on SQL logical functions for query evaluation. The task is to evaluate 'all conditions,' implying multiple conditions must be true together.

OR (Option A): OR returns true if any condition is true, not ensuring all conditions are met.

NOT (Option B): NOT negates a condition, not suitable for combining multiple conditions.

AND (Option C): AND requires all conditions to be true, which aligns with evaluating 'all conditions' in a query.

IF (Option D): IF is a conditional function for decision-making, not for evaluating multiple conditions together.

The DA0-002 Data Analysis domain includes 'applying the appropriate descriptive statistical methods using SQL queries,' and AND is the best logical function for ensuring all conditions are met.

This question falls under the Data Analysis domain, focusing on SQL queries to handle duplicates while counting employees. The task is to count unique employees with a salary of $125,000 or more, addressing duplicates in the dataset.

Option A: SELECT DISTINCT Employee_ID FROM Employee WHERE Salary >= 125000

This lists unique Employee_IDs but doesn't provide a count, which the department needs.

Option B: SELECT COUNT(DISTINCT Employee_ID) FROM Employee WHERE Salary >= 125000

This counts unique Employee_IDs (using DISTINCT) with a salary of $125,000 or more, correctly addressing duplicates and providing the required count (2 employees: 003 and 004).

Option C: SELECT DISTINCT Employee_ID FROM Employee WHERE Salary > 125000

This lists unique Employee_IDs with a salary strictly greater than $125,000 (missing 004), and doesn't provide a count.

Option D: SELECT COUNT(Employee_ID) FROM Employee WHERE Salary >= 125000

This counts all rows without addressing duplicates, resulting in an incorrect count (2 rows, but only 2 unique employees).

The DA0-002 Data Analysis domain includes 'applying the appropriate descriptive statistical methods using SQL queries,' and COUNT(DISTINCT) is the correct method to count unique employees while handling duplicates.

This question falls under the Data Governance domain, which in DA0-002 includes understanding data privacy and compliance with regulations like GDPR. The question asks which piece of information, if made public, constitutes a privacy violation, meaning it must be personally identifiable information (PII).

Gender (Option A): Gender is not typically considered PII on its own, as it's not uniquely identifiable.

Driver's license (Option B): A driver's license number is PII because it uniquely identifies an individual and can be linked to other personal information, such as name and address. Making it public violates privacy regulations.

Age (Option C): Age alone isn't PII, as it's not uniquely identifiable.

Employment status (Option D): Employment status (e.g., employed, unemployed) isn't PII, as it doesn't uniquely identify an individual.

The DA0-002 Data Governance domain includes 'identifying PII and data privacy concepts,' and a driver's license is a clear example of PII that, if exposed, results in a privacy violation.

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.