________ is a critical phase in the Test Execution Lifecycle where test results are compared against expected outcomes.
- Analysis
- Execution
- Validation
- Verification
Execution is a critical phase in the Test Execution Lifecycle where test results are compared against expected outcomes. This step determines whether the actual results align with the expected results.
What type of data validation involves checking the relationships between data in different tables?
- Cross-table validation
- Field-level validation
- Metadata validation
- Record-level validation
Cross-table validation involves checking the relationships between data in different tables. It ensures that the relationships are valid and consistent across the entire database.
How does data virtualization contribute to efficient Test Data Management?
- Centralized Storage
- Data Deletion
- Data Replication
- On-Demand Access
Data virtualization allows for on-demand access to virtualized datasets without the need for physical replication. This contributes to efficient Test Data Management by providing real-time access to diverse datasets without the overhead of maintaining multiple copies.
The strategy of ________ testing is employed to ensure that the most critical functions are tested first in ETL regression testing.
- Bottom-Up
- Random
- Risk-Based
- Top-Down
The strategy of Risk-Based testing is employed to ensure that the most critical functions are tested first in ETL regression testing. This approach focuses on identifying and prioritizing high-risk areas, optimizing testing efforts.
________ is a significant challenge in automating ETL tests.
- Data Extraction
- Data Loading
- Data Transformation
- Data Variability
Regression is a significant challenge in automating ETL tests. ETL processes often involve complex transformations, and changes in any part of the process can have unintended consequences. Regression testing is crucial to identify and address any issues that may arise due to modifications.
In advanced ETL processes, how is machine learning utilized for data validation and verification?
- Machine learning automates the entire ETL process
- Machine learning identifies patterns and anomalies for improved validation
- Machine learning is not applicable in data validation and verification
- Machine learning only applies to data extraction
Machine learning is utilized in advanced ETL processes for data validation by identifying patterns, anomalies, and trends in the data. This enhances the accuracy and efficiency of the validation process.
How does the concept of 'sprints' in Agile methodology impact ETL testing timelines?
- It has no impact on ETL testing timelines.
- It introduces unpredictability, making it difficult to estimate testing timelines.
- It lengthens the testing timelines by delaying testing until the end of the project.
- It shortens the testing timelines by allowing incremental testing within each sprint.
In Agile methodology, 'sprints' are time-boxed iterations during which specific features or functionalities are developed and tested. ETL testing within each sprint ensures incremental validation of data integration processes, leading to shorter testing timelines and early detection of issues.
________ is essential to ensure that the test environment closely mirrors the production setup.
- Data Extraction
- Data Masking
- Data Simulation
- Data Validation
Data Masking is essential to ensure that the test environment closely mirrors the production setup. It helps in protecting sensitive information by replacing, encrypting, or scrambling data in a way that it remains realistic for testing purposes.
In ETL testing, ________ is crucial for maintaining the integrity and security of sensitive data.
- Data Encryption
- Data Masking
- Data Profiling
- Data Validation
In ETL testing, Data Encryption is crucial for maintaining the integrity and security of sensitive data. Encryption ensures that the data is transformed into a secure format, protecting it from unauthorized access during the testing process.
________ analytics is becoming a key component in ETL processes for predictive and prescriptive analysis.
- Descriptive
- Diagnostic
- Predictive
- Prescriptive
Prescriptive analytics is becoming a key component in ETL processes, guiding decision-making by recommending actions to optimize outcomes. It goes beyond predicting future trends to suggesting actions based on analysis.