The identification of ________ is a critical part of test requirement analysis for ensuring data accuracy.
- Data Flow
- Source Systems
- Target Systems
- Transformations
Identifying transformations is a critical aspect of test requirement analysis in ETL testing. It ensures that the data is accurately processed and transformed according to the defined business rules.
To test the scalability of an ETL process, performance testing tools often measure the ________ under varying loads.
- Data Processing Time
- Network Latency
- System Resource Utilization
- Throughput
To test the scalability of an ETL process, performance testing tools often measure the Throughput under varying loads. Throughput quantifies the amount of data processed per unit of time, reflecting system capacity.
What role does containerization play in cloud-based ETL testing?
- Data Encryption
- Isolation and Portability
- Load Balancing
- Parallel Processing
Containerization in cloud-based ETL testing provides isolation and portability. Containers encapsulate ETL processes, ensuring consistency across different environments and facilitating easier deployment and scaling.
In SQL, ________ is a property that ensures either all or no operations of a transaction are performed.
- Atomicity
- Consistency
- Durability
- Isolation
In SQL, Atomicity is a property of transactions that ensures either all operations within a transaction are performed (committed) or none of them are performed (rolled back). It ensures the reliability of the database state.
After a significant update in the ETL tool, what regression testing approach should be taken to ensure data accuracy?
- Focus on impacted areas and perform selective regression testing
- Re-run all existing test cases
- Run only performance tests
- Skip regression testing for this update
After a significant update in the ETL tool, the testing team should focus on the impacted areas and perform selective regression testing to ensure data accuracy. This approach optimizes testing efforts while ensuring the integrity of the updated components.
How can decision table testing be beneficial in handling multiple conditions?
- It is not applicable in handling multiple conditions
- It is only useful for handling binary conditions
- It provides a systematic way to examine all possible combinations of conditions and their corresponding actions
- It simplifies the testing process by ignoring certain conditions
Decision table testing is valuable in handling multiple conditions as it systematically explores all possible combinations of conditions and their associated actions, ensuring comprehensive test coverage for complex scenarios.
After test execution, what is the next step in the Test Execution Lifecycle?
- Test Closure
- Test Design
- Test Execution
- Test Planning
The next step after test execution is Test Closure. In this phase, the testing activities are formally concluded. It involves preparing test summary reports, documenting lessons learned, and obtaining approval to close the testing phase.
What type of ETL testing is conducted to validate the data aggregation and summarization processes?
- Data Aggregation Testing
- Data Consolidation Testing
- Data Summarization Testing
- Data Transformation Testing
Data Aggregation Testing focuses on validating the accuracy and correctness of the processes involved in aggregating and summarizing data during the ETL transformation phase. It ensures that the summarized data is accurate and meets the intended requirements.
In ETL testing, ________ helps in identifying potential data quality issues early in the process.
- Data extraction
- Data loading
- Data masking
- Data profiling
In ETL testing, data profiling helps in identifying potential data quality issues early in the process. Data profiling involves analyzing source data to understand its structure, quality, and relationships, aiding in the identification of anomalies or inconsistencies.
In ETL, ________ testing is crucial for verifying the transformation rules.
- Integration
- Regression
- Transformation
- Validation
In ETL, Transformation testing is crucial for verifying the accuracy and effectiveness of the transformation rules applied to the data. It ensures that the data is correctly transformed according to the defined business rules.