For advanced analytics, ________ techniques are applied to data in a Data Warehouse.
- Data Archiving
- Data Cleansing
- Data Encryption
- Machine Learning
For advanced analytics, Machine Learning techniques are applied to data in a Data Warehouse. Machine Learning algorithms analyze and derive insights from the data, enabling more sophisticated analytics and predictions.
During a data migration project, a data quality issue is identified in the source system. What transformation strategy should be applied to clean and standardize this data?
- Apply cleansing rules and transformations to correct data issues
- Bypass the data quality issue and load the data as-is
- Implement data profiling and analysis to identify patterns
- Reject and log the erroneous records for manual correction
In the case of a data quality issue during migration, the recommended strategy is to apply cleansing rules and transformations to correct the data issues. This ensures that the data meets the required quality standards before being loaded into the target system.
What advanced feature in SSIS assists in handling complex data integration scenarios?
- Change Data Capture
- Data Profiling
- Data Quality Services
- Fuzzy Logic Transformations
Fuzzy Logic Transformations is an advanced feature in SSIS that aids in handling complex data integration scenarios. It allows for sophisticated matching and transformation of data, especially useful in scenarios with varying data formats and structures.
How does the concept of 'shift left' in ETL testing impact risk management?
- It has no impact on risk management
- It increases risk by delaying testing until later stages
- It increases risk by skipping testing altogether
- It reduces risk by conducting testing early in the development lifecycle
'Shift left' in ETL testing refers to conducting testing early in the development lifecycle. This helps identify and address issues sooner, reducing the overall risk in the project by catching problems in the early stages.
Test requirement analysis should always include a review of the recovery strategy for ETL processes.
- Auditing
- Error Handling
- Recovery
- Transformation
As part of test requirement analysis, evaluating the recovery strategy is crucial for ETL processes. This ensures that in case of failures or errors, there is a robust plan in place to recover and maintain data integrity.
To manage different test environments, version control can be used to maintain separate ________ for each environment.
- Branches
- Commits
- Merges
- Repositories
In version control for ETL testing, maintaining separate branches for each test environment is common practice. It helps in isolating changes specific to different environments and ensures stability.
________ is a key feature in data quality tools for ensuring data integrity.
- Data Compression
- Data Duplication
- Data Encryption
- Data Profiling
Data profiling is a key feature in data quality tools. It involves analyzing the content, structure, and relationships within the data to ensure data integrity and understand its characteristics.
How does the integration of cloud data lakes affect ETL testing?
- All of the above
- Data security concerns
- Integration complexity
- Scalability challenges
The integration of cloud data lakes introduces additional complexity to ETL testing due to factors such as scalability challenges, data security concerns, and integration complexity. Testing in this environment requires thorough validation of data movement, transformation, and security protocols.
For an ETL process that involves complex data transformations, what testing strategy would you implement to ensure data integrity?
- Data Completeness Testing
- Data Consistency Testing
- Data Masking
- Data Profiling
Data Consistency Testing would be crucial in ensuring data integrity for an ETL process with complex transformations. This strategy involves verifying that data remains consistent across different stages of the transformation process, safeguarding against errors or discrepancies.
Which metric is commonly used to assess the accuracy of data in ETL testing?
- Data Accuracy
- Data Completeness
- Data Consistency
- Data Integrity
Data Accuracy is commonly used to assess the correctness and precision of data in ETL testing. It measures how closely the extracted, transformed, and loaded data aligns with the expected results or business requirements. Evaluating data accuracy helps ensure that the ETL process maintains the integrity and reliability of the data.