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IBM C2090-421 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Metadata Management | 6% | - Import and share metadata - Runtime Column Propagation (RCP) - Schema orchestration |
| Topic 2: Persistent Storage | 10% | - Restructure and format stages - Sequential files and file sets - Data sets and file structures |
| Topic 3: Advanced Techniques | 10% | - Complex Flat File (CFF) stage - Environment variables and parameter sets - Job deployment and packaging |
| Topic 4: Performance & Troubleshooting | 15% | - Handle errors and rejects - Partitioning and sorting optimization - Monitor and log execution |
| Topic 5: Parallel Architecture | 9% | - Data partitioning and collecting - Node pools and parallel processing - Parallel execution and configuration files |
| Topic 6: Job Design & Development | 25% | - Slowly Changing Dimensions (SCD) - Lookup, Join, Merge, and Funnel stages - Transformer and derivation logic - Job sequences and parameterization |
| Topic 7: Database Connectivity | 9% | - Source and target database properties - ODBC, DB2, Oracle integration - Database stage selection |
| Topic 8: Configuration | 6% | - Understand architecture and components - Install and configure InfoSphere DataStage v8.5 - Create and configure projects |
IBM InfoSphere DataStage v8.5 Sample Questions:
Click the exhibit button.
You submit a job from DataStage Director and then log onto your DataStage Linux server to issue the command "ps -ef | grep ds" and receive the following screen: Which process is a player?
- A. 7217
- B. 7215
- C. 7216
- D. 7117
You are about to begin major changes to jobs in a project. You want to conveniently identify job changes on an ad hoc basis. What two tasks will allow you to identify changes to your jobs? (Choose two.)
- A. Before making a change to a job make a copy of the job in a different category folder.
- B. Import the original job from a .dsx export.
- C. Select the job, then right click Compare within.
- D. Select the job, then right click Cross Project Compare.
A DataStage job uses an Inner Join to combine data from two source parallel datasets that were written to disk in sort order based on the join key columns. Which two methods could be used to dramatically improve performance of this job? (Choose two.)
- A. Explicitly specify hash partitioning and sorting on each input to the Join stage.
- B. Add a parallel sort stage before each Join input, specifying the "Don't Sort, Previously Grouped" sort key mode for each key.
- C. Disable job monitoring.
- D. Set the environment variable $APT_SORT_INSERTION_CHECK_ONLY.
- E. Unset the Preserve Partitioning flag on the output of each parallel dataset.
Which two statements are true about the use of named node pools? (Choose two.)
- A. Clustered environments must have named node pools for data processing.
- B. Named node pools constraints will limit stages to be executed only on the nodes defined in the node pools.
- C. Using appropriately named node pools forces DataStage to use named pipes between stages.
- D. Named node pools can allow separation of buffering from sorting disks.
Which two statements are true about DataStage parallel routines? (Choose two.)
- A. Parallel routines are written in DataStage Basic.
- B. Parallel routines are coded within a DataStage Designer editor.
- C. Parallel routines can be written that are called from within the Transformer stage.
- D. Parallel routines can be written that are called before or after a stage runs.
- E. Parallel routines created as shared library functions are not supported.
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