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.
What is the role of version control systems in ETL testing?
- Controlling data versions
- Managing ETL server versions
- Tracking changes to ETL code and configurations
- Version control systems are not relevant to ETL testing
Version control systems play a crucial role in ETL testing by tracking changes to ETL code and configurations. This ensures traceability, collaboration, and the ability to revert to previous versions if needed.
When an ETL process experiences latency issues during peak loads, what should be analyzed using performance testing tools?
- ETL Server Performance
- Network Latency
- Source System Performance
- Target System Performance
During peak loads, analyzing the performance of the ETL server is crucial. This involves assessing the server's capacity, resource utilization, and response times to identify bottlenecks and optimize performance.
In a scenario where an organization must comply with GDPR, how do data quality tools assist in maintaining compliance?
- Apply data profiling techniques
- Enforce data masking policies
- Ensure data anonymity
- Implement encryption algorithms
Data quality tools assist in GDPR compliance by ensuring data anonymity. They can anonymize sensitive information, protecting privacy and meeting regulatory requirements without compromising data quality.
Which of the following ETL tools is a Microsoft product and integrates well with SQL Server?
- Apache NiFi
- Informatica PowerCenter
- SQL Server Integration Services (SSIS)
- Talend
SQL Server Integration Services (SSIS) is a Microsoft ETL tool that seamlessly integrates with SQL Server. It allows for efficient data extraction, transformation, and loading within the Microsoft ecosystem.
Which type of testing is more efficient for repetitive test cases in ETL, automated or manual?
- Automated
- Dynamic
- Manual
- Semi-Automated
Manual testing is more efficient for repetitive test cases in ETL. It allows testers to apply human intelligence to identify patterns and variations that may not be easily captured in automated scripts.
A company integrates streaming data into their data lake. What testing strategies should be applied to handle this type of data?
- Batch and Real-time Processing Testing
- Data Profiling Testing
- Schema Validation Testing
- Source-to-Target Mapping Testing
When dealing with streaming data integration into a data lake, testing strategies should include Batch and Real-time Processing Testing. This ensures that both the traditional batch processing and real-time streaming components are validated for accuracy and performance.
________ in BI tools is crucial for handling large volumes of data efficiently.
- Caching
- Compression
- Indexing
- Partitioning
Partitioning in BI tools is crucial for handling large volumes of data efficiently. It involves dividing data into smaller, manageable segments, improving query performance and data retrieval speed.
The process of normalizing a database involves dividing a database into ________.
- Columns
- Rows
- Schemas
- Tables
The process of normalizing a database involves dividing it into Tables. Normalization is a technique that helps organize data efficiently and reduces redundancy by dividing tables into smaller, related tables.
________ in a Data Warehouse helps in maintaining the history of data changes over time.
- Change Data Capture
- Dimension Table
- Fact Table
- Metadata
Change Data Capture (CDC) in a Data Warehouse is the process that helps in maintaining the history of data changes over time. It captures and tracks modifications to the data, providing a historical perspective for analysis.