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IBM A2090-303 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Integration and Processing | - Data Sources and Targets
|
| Performance Tuning | - Optimization Techniques
|
| DataStage Fundamentals | - Architecture Overview
|
| Job Control and Administration | - Scheduling and Monitoring
|
| Job Design and Development | - Parallel and Server Jobs
|
IBM Assessment: IBM InfoSphere DataStage v9.1 Assessment Sample Questions:
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.
Correct Answer: C 🗳️
What is used to configure the DataStage QualityStage Operations Console?
- A. The DSODBCConfig.cfg file
- B. The DSAppWatcher.sh script
- C. Information Server Web Console
- D. DataStage Administrator
Correct Answer: A 🗳️
Identify two different types of custom stages you can create to extend the Parallel job syntax? (Choose two.)
- A. Basic stage
- B. Group stage
- C. Input stage
- D. Wrapped stage
- E. Custom stage
Correct Answer: D,E 🗳️
Which two commands start a DataStage job named procRows in a project named DSProject. It also passes the value 200 to the numRows parameter. (Choose two.)
- A. dsjob -run -paramfile numRows=200 DSProject procRows
- B. dsjob -run -mode normal -paramfile numRows=200 DSProject procRows
- C. dsjob -run -mode validate -param numRows=200 DSProject procRows
- D. dsjob -run -mode normal -param numRows=200 DSProject procRows
- E. dsjob -run -queue HighPriority -param numRows=200 DSProject procRows
Correct Answer: D,E 🗳️
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. Unset the Preserve Partitioning flag on the output of each parallel dataset.
- B. Explicitly specify hash partitioning and sorting on each input to the Join stage.
- C. Set the environment variable $APT_SORT_INSERTION_CHECK_ONLY.
- D. Add a parallel sort stage before each Join input, specifying the "Don't Sort, Previously Grouped" sort key mode for each key.
- E. Disable job monitoring.
Correct Answer: C,D 🗳️
