________ testing is employed in data lakes to verify the accuracy and consistency of analytical queries.
- Integration
- Performance
- Query
- Regression
"Query" testing is employed in data lakes to verify the accuracy and consistency of analytical queries. This ensures that the queries provide reliable results, supporting the data lake's analytical capabilities.
A company notices that its nightly ETL jobs are taking longer to complete than expected. What should be the first step in diagnosing and optimizing the performance?
- Adding more source systems
- Increasing server capacity
- Reviewing ETL job logs and performance metrics
- Rewriting ETL code from scratch
The first step in diagnosing and optimizing ETL performance is to review ETL job logs and performance metrics. This helps identify bottlenecks, such as slow-running queries or resource constraints, that may be causing delays.
In ETL, the process of splitting a single data column into multiple columns is referred to as ________ transformation.
- Decompose
- Split
- Unpack
- Unpivot
The correct term is "Unpivot" transformation. This involves breaking down a single column with multiple values into separate columns, each representing a specific attribute. It is useful for organizing and structuring data for analysis.
In ETL, ________ is used to extract data from different sources.
- Extraction
- Integration
- Loading
- Transform
In ETL, the process of Extraction is used to gather data from various sources. It is the initial step where data is collected for further processing in the ETL pipeline.
Manual testing is often preferred when ________ is a key requirement.
- Complexity
- Precision
- Repeatability
- Speed
Manual testing is often preferred when Repeatability is a key requirement. In certain situations where test cases need to be executed with various data sets or under changing conditions, manual testing provides flexibility and adaptability.
________ in ETL involves ensuring that data is consistent and correct across the system.
- Cleansing
- Standardization
- Transformation
- Validation
The Validation step in ETL is responsible for ensuring that data is consistent and correct throughout the system. It involves checks and validation rules to maintain data quality.
________ in SQL are used to ensure the database performs actions in a logical order.
- Constraints
- Indexes
- Transactions
- Views
In SQL, Transactions are used to ensure that a series of database actions are performed in a logical order. Transactions provide consistency and help maintain the integrity of the database.
In database testing, what is the significance of testing database triggers and stored procedures?
- Testing triggers and stored procedures ensures data encryption
- Triggers and stored procedures are crucial for maintaining data integrity and enforcing business rules
- Triggers and stored procedures are only relevant for backup and recovery
- Triggers and stored procedures are primarily used for database indexing
Testing triggers and stored procedures in database testing is crucial as they play a significant role in maintaining data integrity, enforcing business rules, and ensuring the correct execution of database operations. They contribute to the overall reliability of the database system.
In the context of big data, what are the challenges associated with data extraction?
- All of the above
- Variety of data sources
- Velocity of data
- Volume of data
The challenges associated with data extraction in big data include handling large volumes of data, dealing with a variety of data sources, and managing the high velocity at which data is generated. Addressing these challenges is crucial for successful big data extraction.
A company is implementing AI/ML in its ETL testing process to handle large data volumes. What are the expected benefits and challenges?
- Efficient Resource Utilization, Reduced Processing Time, Enhanced Data Privacy, Dependency on Legacy Systems
- Faster Processing, Reduced Cost, Data Security, Increased Manual Efforts
- Improved Scalability, Enhanced Data Accuracy, Increased Automation, Potential for Bias
- Real-time Monitoring, Simplified Maintenance, Decreased Complexity, Limited Data Exploration
Implementing AI/ML in ETL testing for large data volumes can bring benefits like improved scalability, enhanced data accuracy, increased automation, but it also poses challenges such as potential bias in algorithms and the need for thorough validation.
How does regression testing differ in Agile methodology compared to traditional ETL processes?
- Agile focuses more on regression testing
- Agile has no regression testing
- No difference, it remains the same
- Traditional ETL processes emphasize more on regression testing
In Agile, regression testing is integrated throughout the development process, ensuring continuous testing with every iteration. Traditional ETL processes may have a separate regression testing phase. The difference lies in the integration and frequency of testing in Agile.
In the context of data verification, what is the importance of referential integrity?
- It ensures data is within acceptable ranges
- It maintains consistency in relationships between tables
- It validates data types and formats
- It verifies the accuracy of individual records
Referential integrity is crucial for maintaining consistency in relationships between tables. It ensures that relationships between foreign keys and primary keys are valid, preventing orphaned or inconsistent data.