In SQL, the ________ statement is used to add new rows to a table.
- ADD
- APPEND
- INSERT
- UPDATE
In SQL, the INSERT statement is used to add new rows to a table. It allows the insertion of data into specified columns or all columns in a table.
In the context of big data, how does testing in data lakes differ from traditional database testing?
- Data access in data lakes is restricted compared to traditional databases
- Data processing in data lakes is faster than in traditional databases
- Data structure in data lakes is well-defined compared to traditional databases
- Data volume in data lakes is typically much larger than in traditional databases
Testing in data lakes differs from traditional database testing primarily due to the significantly larger data volume typically found in data lakes. Traditional database testing focuses on structured data with predefined schemas, while data lakes often contain unstructured or semi-structured data requiring different testing approaches.
Which stage in ETL testing involves verifying the transformation rules?
- Extraction
- Loading
- Transformation
- Validation
The Transformation stage in ETL testing involves verifying the transformation rules. This ensures that the data is correctly transformed according to the defined business rules and requirements before being loaded into the target system.
If discrepancies are found in source-to-target count during ETL testing, what potential issues should be considered?
- Data Governance Policies, Data Archiving Strategies, Metadata Management, Data Validation Techniques
- Data Type Mismatch, Null Value Handling, Data Precision Loss, Data Transformation Errors
- ETL Tool Configuration Errors, Data Encryption Overhead, Data Compression Ratio
- Source Data Volume, Target Data Volume, Data Deduplication Techniques, Data Masking Performance
Discrepancies in source-to-target count during ETL testing may indicate issues such as data type mismatch, null value handling, data precision loss, or data transformation errors. Investigating these aspects helps ensure data integrity throughout the ETL process.
What Agile practice helps in quickly adapting ETL testing strategies to changing business requirements?
- Continuous Integration
- Retrospective Meetings
- Sprint Planning
- User Story Refinement
Continuous Integration in Agile allows for quick adaptation of ETL testing strategies to changing business requirements. Regular integration of code and automated testing ensure that the testing process aligns with evolving project needs.
________ in AI/ML aids in automating complex data correlations in ETL testing.
- Association
- Classification
- Clustering
- Regression
Clustering in AI/ML aids in automating complex data correlations in ETL testing. It involves grouping similar data points, making it useful for identifying patterns and relationships within datasets.
Which factor is most crucial for mitigating risks in ETL testing?
- Frequent changes in testing strategies
- Quick execution of test cases
- Thorough analysis of requirements
- Use of complex testing tools
Thorough analysis of requirements is the most crucial factor for mitigating risks in ETL testing. Understanding the requirements thoroughly helps in identifying potential risks and ensures that testing efforts are focused on critical areas.
__________ is critical for managing risks associated with data transformation in complex ETL processes.
- Data profiling
- Incremental loading
- Source-to-target mapping
- Version control
Data profiling is critical for managing risks in data transformation. By understanding the characteristics and quality of the data, testers can identify potential issues and implement measures to mitigate risks during the ETL process.
________ is a technique used to identify test cases that examine the boundaries of input ranges.
- Boundary Value Analysis
- Decision Table Testing
- Equivalence Partitioning
- State Transition Testing
Boundary Value Analysis is a technique used to identify test cases that examine the boundaries of input ranges. This helps ensure that the application handles inputs at the edge conditions effectively.
What is the primary goal of performance optimization in the context of ETL?
- Increasing Extraction Speed
- Maximizing Data Volume
- Minimizing Processing Time
- Reducing Transformation Complexity
The primary goal of performance optimization in ETL is to minimize processing time. This involves enhancing the efficiency of data extraction, transformation, and loading processes to ensure timely and reliable data delivery.
A financial institution is implementing an ETL process for regulatory reporting. What data validation and verification strategies should be prioritized?
- Data Comparison, Metadata Validation, and Data Reconciliation
- Sampling, Data Snapshot, and Pattern Matching
- Schema Mapping, Data Obfuscation, and Record Deduplication
- Statistical Analysis, Data Profiling, and Rule-Based Checks
In the context of regulatory reporting, prioritizing Statistical Analysis, Data Profiling, and Rule-Based Checks helps ensure data accuracy and compliance. Statistical analysis detects anomalies, data profiling assesses data quality, and rule-based checks enforce regulatory requirements.
Which test case design technique focuses on the functionality of the system?
- Boundary Value Analysis
- Equivalence Partitioning
- Functional Testing
- Performance Testing
Functional Testing is a test case design technique that focuses on the functionality of the system. It ensures that the software functions according to the specified requirements.