Last Updated: Aug 22, 2026
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Complex Jobs & Real-Time | 10% | - Real-time integration patterns - Job sequences and dependencies - Information Services Director (ISD) |
| Topic 2: Metadata | 6% | - Metadata sharing and reuse - Schema management and import - Runtime Column Propagation (RCP) |
| Topic 3: Performance Tuning | 10% | - Environment variable tuning - Sorting and partitioning optimization - Identifying bottlenecks |
| Topic 4: Parallel Architecture | 7% | - Environment variables for parallel jobs - Partitioning and collecting methods - Parallel execution framework |
| Topic 5: Persistent Storage | 15% | - File format and record structure - Sequential File and Data Set stages - Data import/export operations |
| Topic 6: Database Connectivity | 7% | - SQL pushdown and data type handling - Connection configuration and optimization - Database Connector stages |
| Topic 7: Data Transformation | 14% | - Change Capture and Apply stages - Slowly Changing Dimension (SCD) stage - Type conversion and derivations - Transformer stage functionality |
| Topic 8: Deployment & Source Control | 7% | - Job comparison and impact analysis - Export/import of jobs and projects - Source control integration |
| Topic 9: Configuration | 6% | - Configuration file structure - Project and environment setup - Node pools and parallel environment design |
| Topic 10: Job Control & Runtime Management | 6% | - Log analysis and troubleshooting - Monitoring execution status - Error handling and message handlers |
| Topic 11: Job Design Principles | 12% | - Shared containers and reusable components - Performance-oriented job design - Minimizing sorts and repartitions - Restart points and checkpoint design |
1. Which step is required to change from a normal lookup to a sparse lookup in an ODBC Connector stage?
A) Change the lookup option in the stage properties to "Sparse".
B) Replace columns at the beginning of a SELECT statement with a wildcard asterisk (*).
C) Sort the data on the reference link.
D) Change the partitioning to hash.
2. How is DataStage table metadata shared among DataStage projects?
A) Export DataStage table definitions from one project and importing them into another project.
B) Import another copy of the table metadata into the project where it is required.
C) Use the InfoSphere Metadata Asset Manager (IMAM) to move the DataStage table definition to the projects where it is needed.
D) Use the "Shared Table Creation Wizard" to place the table in the shared repository.
3. You would like to pass values into parameters that will be used in a variety of downstream activity stages within a job sequence. What are two valid ways to do this? (Choose two.)
A) Check the "Propagate Parameters" checkbox in the Sequence Job properties.
B) Add a Transformer stage variable to the job sequence canvas.
C) Use local parameters.
D) Use the UserVariablesActivity stage to populate the local parameters from an outside source such as a file.
E) Place a parameter set stage on the job sequence.
4. What two computer system resources on the DataStage engine are monitored in the Operations Console? (Choose two.)
A) XMETA database
B) Disk
C) Scratch disk
D) CPU
E) Memory
5. What two binding types are supported by Information Services Director (ISD) for a parallel job that is designed to be used as a service? (Choose two.)
A) SOAP
B) EJB
C) HDFS
D) STREAMS
E) SQL
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: D | Question # 3 Answer: C,D | Question # 4 Answer: D,E | Question # 5 Answer: A,B |
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