In complex ETL processes, ________ testing ensures the consistency of data between source and target systems.
- Data Consistency
- Integration
- Performance
- Regression
In complex ETL processes, Data Consistency testing ensures the consistency of data between source and target systems, especially when multiple transformations and manipulations are involved. It helps identify any discrepancies or data integrity issues.
In complex Data Warehousing, ________ is used to manage and store high volumes of diverse data.
- Data Integration
- Data Mining
- Data Profiling
- Hadoop
In complex Data Warehousing, Hadoop is used to manage and store high volumes of diverse data. Hadoop's distributed storage and processing capabilities make it suitable for handling large-scale and varied datasets.
Which phase in the Test Execution Lifecycle involves the actual execution of test cases?
- Test Closure
- Test Design
- Test Execution
- Test Planning
Test Execution is the phase where actual testing takes place. Test cases are executed, and the system's behavior is observed to verify if it aligns with the expected outcomes.
Real-time data integration testing often requires ________ to handle high-volume data scenarios.
- Load Balancing
- Parallel Processing
- Replication
- Scalability
Real-time data integration testing often requires Scalability to handle high-volume data scenarios. Scalability ensures that the system can handle increased data loads efficiently, maintaining performance and responsiveness.
What is the significance of Star Schema in Data Warehousing?
- It emphasizes denormalized data for simplicity.
- It focuses on normalizing data to minimize redundancy.
- It is designed for complex relationships between multiple fact and dimension tables.
- It provides a simple structure with a central fact table connected to dimension tables.
The Star Schema in Data Warehousing is significant as it provides a simple structure with a central fact table connected to dimension tables. This design simplifies queries and enhances performance.
During peak data loads, the ETL process slows down significantly. What metrics should be analyzed to identify bottlenecks?
- CPU Utilization, Disk I/O, Memory Usage, Network Bandwidth
- Data Quality Score, Data Latency, Data Duplication Rate, Data Partitioning
- Source Data Volume, Target Data Volume, ETL Tool License Usage, Data Compression Ratio
- Source-to-Target Mapping, Data Encryption Overhead, Data Archiving Efficiency, Data Masking Performance
To identify bottlenecks during peak data loads, metrics such as CPU utilization, disk I/O, memory usage, and network bandwidth should be analyzed. These metrics help pinpoint resource constraints affecting ETL performance.
To handle complex logic, ETL processes may use a ________ transformation, which allows custom code or scripts.
- Advanced
- Complex
- Custom
- Script
In ETL processes, a "Script" transformation is employed to handle complex logic. This transformation allows the use of custom code or scripts to manipulate and transform data according to specific requirements.
How does the concept of data variety affect Big Data testing strategies?
- Dealing with large volumes of data
- Ensuring data security
- Handling diverse data types and structures
- Managing data velocity
Data variety in Big Data refers to the diverse types and structures of data, such as structured, unstructured, and semi-structured data. Testing strategies must accommodate this variety to ensure comprehensive validation of all data types.
A retail company is extracting data from various sources for market analysis. What should be the key focus in their extraction technique for accurate trend prediction?
- Data Consistency
- Data Quality
- Data Variety
- Data Volume
In the context of market analysis, the key focus in data extraction should be on ensuring Data Quality. High-quality data ensures accuracy in trend prediction and decision-making. It involves verifying data integrity, removing duplicates, and standardizing formats across different sources.
In data governance, ________ ensures that data usage complies with internal and external regulations.
- Data cataloging
- Data compliance
- Data masking
- Data stewardship
Data stewardship in data governance ensures that data usage complies with internal and external regulations. It involves defining and enforcing data policies to maintain data quality and compliance.