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.
How are advancements in data governance expected to influence ETL testing strategies?
- Enhanced Data Quality Management
- Minimized Data Governance Impact
- Reduced Data Security Concerns
- Simplified ETL Processes
Advancements in data governance are expected to influence ETL testing by enhancing data quality management. With improved governance, ETL testing strategies can ensure data integrity and compliance with data quality standards.
In a fast-paced Agile project, how should ETL testing be adjusted to accommodate a sudden change in data source formats?
- Collaborate with stakeholders, update test cases, and perform exploratory testing to validate the changes
- Modify existing test cases to accommodate the new data source formats
- Postpone testing until the next sprint to avoid disruption
- Skip testing for the impacted data sources to maintain project timelines
In a fast-paced Agile project, adapting to sudden changes in data source formats requires collaboration with stakeholders, updating test cases, and performing exploratory testing to validate the changes. This ensures that testing remains effective despite evolving project requirements.
What approach is recommended for dealing with defects that cannot be resolved immediately in ETL testing?
- Automated Resolution
- Deferred Resolution
- Ignored Defects
- Immediate Fix
The recommended approach for dealing with defects that cannot be resolved immediately in ETL testing is Deferred Resolution. This involves documenting the defect and planning its resolution in a subsequent release or update, allowing for a more thorough and non-disruptive resolution process.