To retrieve a specific set of records, SQL uses the ________ statement with conditions.
- CONDITION
- EXTRACT
- FILTER
- SELECT
To retrieve a specific set of records in SQL, the SELECT statement is used with conditions specified in the WHERE clause. This allows filtering based on certain criteria.
In Agile ETL testing, the process of continuously refining and improving test cases is known as ________.
- Behavior Driven Development (BDD)
- Continuous Testing
- Test Case Refactoring
- Test Driven Development (TDD)
The process of continuously refining and improving test cases in Agile ETL testing is known as Test Case Refactoring. It ensures that test cases stay relevant and effective as the project evolves.
________ technology in AI/ML can predict future data trends in ETL testing.
- Advanced
- Analytical
- Machine Learning
- Predictive
Machine Learning technology in AI can predict future data trends in ETL testing. It utilizes algorithms to analyze historical data, identify patterns, and make predictions about future trends, enhancing the testing process.
________ strategy in data loading is beneficial for reducing load times and system impact.
- Indexing
- Normalization
- Parallel
- Partitioning
Parallel loading strategy in data loading is beneficial for reducing load times and system impact. It involves dividing the data into smaller subsets and loading them concurrently, improving overall efficiency.
Implementing ________ in the test environment allows for dynamic scaling and testing of ETL processes.
- Auto Scaling
- Data Encryption
- Load Balancing
- Version Control
Implementing Auto Scaling in the test environment allows for dynamic scaling and testing of ETL processes. This ensures optimal resource utilization during varying workloads.
What is a data anomaly in the context of ETL testing?
- Data Abnormality
- Data Discrepancy
- Data Inconsistency
- Data Irregularity
In ETL testing, a data anomaly refers to a Data Discrepancy, which indicates differences or irregularities in the data between the source and target systems. Identifying and resolving such anomalies is crucial for ensuring data accuracy and integrity.
In data transformation, what is a lookup transformation used for?
- Aggregating data values
- Applying calculations to data
- Matching and retrieving related data from another table
- Sorting data based on a specified criterion
A lookup transformation in data transformation is used for matching and retrieving related data from another table. It helps enrich the data by incorporating additional information based on matching keys.
________ constraints ensure that all values in a column are different.
- Check
- Foreign Key
- Primary Key
- Unique
Unique constraints in SQL ensure that all values in a column are different. This is commonly used to enforce uniqueness in a specific column, such as a unique username in a user table.
In a scenario where the ETL process needs continuous updates and integration, how would you decide the balance between automated and manual testing?
- Base Decision on Complexity of Changes
- Prioritize Automated Testing
- Rely Solely on Manual Testing
- Use Both Automated and Manual Testing in Equal Proportions
In a scenario requiring continuous updates and integration, it's essential to strike a balance between automated and manual testing. Using both approaches in equal proportions allows for comprehensive coverage, leveraging the efficiency of automation while retaining the flexibility and insight provided by manual testing.
What is the primary purpose of setting up a test environment in ETL testing?
- Evaluate performance
- Monitor network traffic
- Reduce costs
- Validate data integrity
The primary purpose of setting up a test environment in ETL testing is to validate data integrity. It allows testers to ensure that data is accurately transformed and loaded without compromising its quality.
During a data migration project, you encounter numerous discrepancies in date formats. How should you handle this anomaly in the ETL process?
- Consult with stakeholders to determine the preferred date format and implement it during loading.
- Develop custom scripts to convert date formats during the transformation phase.
- Ignore the date format discrepancies as they are not critical for data migration.
- Use data profiling tools to identify and standardize date formats before transformation.
Handling date format discrepancies in a data migration project requires careful attention. Using data profiling tools helps identify variations, allowing for standardization before the transformation phase, ensuring consistency and accuracy in the loaded data.
What is the impact of using production data for testing in terms of data security?
- Data Exposure
- Increased Security
- Legal Consequences
- No Impact
Using production data for testing poses a significant risk of data exposure and potential legal consequences. It can violate data privacy regulations and compromise sensitive information, emphasizing the importance of using sanitized test data.