Last Updated: Jul 21, 2026
No. of Questions: 134 Questions & Answers with Testing Engine
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Database Connectivity | 7% | - Database stage selection - Connection and query configuration - Source and target database properties |
| Topic 2: Configuration | 6% | - Project setup and configuration - Configuration file components and usage - Environment variables and settings |
| Topic 3: Job Design & Deployment | 18% | - Compilation and deployment procedures - Job sequencing and restartability - Balanced Optimization |
| Topic 4: Data Transformation | 14% | - Data conversion and mapping - Transformer stage functionality - SCD and complex transformations |
| Topic 5: Job Components & Processing | 11% | - Shared containers and reusability - Join, Lookup, and Merge stages - Sort and Filter stages |
| Topic 6: Metadata Management | 6% | - Schema and data type handling - Importing and exporting metadata - Runtime Column Propagation (RCP) |
| Topic 7: Parallel Architecture | 7% | - Partitioning and collecting methods - Node configuration and performance - Parallel execution concepts |
| Topic 8: Performance & Troubleshooting | 16% | - Performance tuning techniques - Logging and monitoring - Error handling and debugging |
| Topic 9: Persistent Storage | 15% | - File formats and properties - Sequential File stage usage - Data import/export operations |
1. In your ETL application design you have found several areas of common processing requirements in the mapping specifications. These common logic areas found include: code validation lookups and name formatting. The common logic areas have the same logic, but the jobs using them would have different column metadata. Choose the action that gives you the best reusability design to effectively implement these common logic areas in your ETL application?
A) Create separate jobs for each layout and choose the appropriate job to run within a job sequencer.
B) Create parallel shared containers and define columns combining all data formats.
C) Create parallel shared containers with Runtime Column Propagation (RCP) ON and define only necessary common columns needed for the logic.
D) Create parallel routines for each of the common logic areas and for each of the unique column metadata formats.
2. What is the correct method to process a file containing multiple record types using a Complex Flat File stage?
A) Manually break the file into multiple files by record type.
B) Define record definitions on the Constraints tab of the Complex Flat File stage.
C) Load a table definition for each record type on the Records tab of the Complex Flat File stage.
D) Flatten the record types into a single record type.
3. Which statement is correct about the Data Rules stage?
A) If you have Rule Creator role in InfoSphere Information Analyzer, you can create and publish rule definitions and rule set definitions directly from the stage itself.
B) When a job that uses the Data Rules stage runs, the output of the stage is passed to the downstream stages and results are stored in the Analysis Results database (IADB).
C) The Data Rules stage works with rule definitions only; not executable rules.
D) As a best practice, you should create and publish new rules from the Data Rules stage.
4. In a Transformer expression for a stage variable, there is a nullable input column. Assume the legacy NULL processing option is turned off. What happens when a row is processed that contains NULL in that input column?
A) The row is rejected.
B) The job aborts.
C) The value written to the stage variable is undetermined.
D) NULL is written to the stage variable.
5. When using the Column Export stage, what are two export column type property values allowed for the combined single output column result? (Choose two.)
A) Binary
B) Decimal
C) Integer
D) VarChar
E) Vector
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: A,D |
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