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.
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.
________ 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.
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 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.
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.
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.
How is data integrity maintained in a relational database?
- By allowing NULL values in critical columns.
- By implementing foreign key constraints to enforce relationships between tables.
- By using triggers for automatic data validation.
- Through regular backups and restores.
Data integrity in a relational database is maintained by implementing foreign key constraints. These constraints ensure that relationships between tables are enforced, preventing orphaned or inconsistent data. Understanding and properly defining these relationships is fundamental to database design.
What type of data model is commonly used in BI tools to optimize query performance?
- Hierarchical schema
- Relational schema
- Snowflake schema
- Star schema
Star schema is commonly used in BI tools to optimize query performance. It consists of a central fact table linked to multiple dimension tables, allowing for efficient data retrieval and analysis.
In a scenario where data consistency is critical and the data volume is large, what data loading technique is most appropriate?
- Batch Loading
- Direct Loading
- Parallel Loading
- Streaming Loading
Batch Loading is the most appropriate technique in this scenario. It involves processing data in predefined batches, ensuring consistency by handling large volumes of data efficiently. Although it may not provide real-time updates, it ensures data integrity and consistency, which is crucial in scenarios where data consistency is paramount.