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IBM A2090-303 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Performance Tuning and Optimization | - Parallel job tuning - Memory and partitioning strategies |
| Topic 2: Administration and Deployment | - Security and user management - Job scheduling and monitoring |
| Topic 3: DataStage Architecture and Concepts | - Project and environment structure - Parallel processing architecture - InfoSphere DataStage components |
| Topic 4: Data Integration and Transformation | - Data transformation logic - Lookup and join strategies - Data extraction and loading techniques |
| Topic 5: Job Design and Development | - Data flow design principles - Job types (parallel, server, sequence) - Stages and transformers |
IBM Assessment: IBM InfoSphere DataStage v9.1 Assessment Sample Questions:
Question 1
Configuring the weighting column of an Aggregator stage affects which two options. (Choose two.)
A. Sum
B. Uncorrected Sum of Squares
C. Maximum Value
D. Average of Weights
E. Coefficient of Variation
Question 2
Identify two items that are created as a result of running a Balanced Optimization on a job that accesses a Hadoop distributed file system as a source? (Choose two.)
A. A Balanced Optimization Shared Container is found in the project.
B. A Big Data File stage is found in the optimized job results.
C. A MapReduce Transformer stage is found in the optimized job result.
D. A Balanced Optimization parameter set is found in the project
E. A JAQL stage is found in the optimized job result.
Question 3
Indicate two possible job design topologies that Information Services Director (ISD) parallel jobs can conform to. (Choose two.)
A. A job with a single ISD Transformation stage.
B. A job with greater then one ISD input stage and one ISD Output stage.
C. A job with one ISD Input stage and no ISD Output stage.
D. A job with one ISD Input stage and greater then one ISD Output stage.
E. A job with no ISD Input stage and with one ISD Output stage.
Question 4
A job design reads from a complex flat file, performs some transformations on the data, and outputs the results to a ISD output stage. How can the parameter values can be passed to this job at run-time?
A. Change the properties of the information provider.
B. Pass the parameter values at the time of the service request.
C. Include the parameter values in the data.
D. Execute a DSSetParam with the values at job execution time.
Question 5
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. Unset the Preserve Partitioning flag on the output of each parallel dataset.
C. Disable job monitoring.
D. Add a parallel sort stage before each Join input, specifying the "Don't Sort, Previously Grouped" sort key mode for each key.
E. Set the environment variable $APT_SORT_INSERTION_CHECK_ONLY.
Solutions:
| Question 1 Answer: A,B | Question 2 Answer: D,E | Question 3 Answer: C,E | Question 4 Answer: B | Question 5 Answer: D,E |

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