Last Updated: Jul 25, 2026
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InfoSphere DataStage v11.3 C2090-424 Exam is related to IBM Certified Solution Developer Certification. This IBM C2090-424 Exam validates and measures the candidate knowledge of proper selection of database stages, default type conversions, Join, Lookup, Merge stages, shared containers, parallel job score, shared containers, DataStage Designer Repository and message handlers, It also deals with the ability to properly configure DataStage. IBM Solution Developer and IBM Advanced Developer usually hold or pursue this certification and you can expect the same job roles after completion of this certification.
Reference: https://www.ibm.com/certify/exam?id=C2090-424
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| Topic | Details | Weights |
|---|---|---|
| Configuration | -Describe how to properly configure DataStage. -Identify tasks required to create and configure a project to be used for jobs. -Given a configuration file, identify its components and its overall intended purpose. -Demonstrate proper use of node pools. | 6% |
| Data Transformation | -Demonstrate knowledge of default type conversions, output mappings, and associated warnings. -Demonstrate proper selections of Transformer stage vs. other stages. -Describe Transformer stage capabilities. -Demonstrate the use of Transformer stage variables. -Identify process to add functionality not provided by existing DataStage stages. -Demonstrate proper use of SCD stage. -Demonstrate job design knowledge of using runtime column propagation (RCP). -Demonstrate knowledge of Transformer stage input and output loop processing. | 12% |
| Persistent Storage | -Explain the process of importing/exporting data to/from framework. -Demonstrate proper use of a Sequential File stage. -Demonstrate proper usage of Complex Flat File stage. -Demonstrate proper usage of FileSets and DataSets. -Demonstrate use of FTP stage for remote data. -Demonstrate use of restructure stages. -Identify importing/exporting of XML data. - Knowledge of balanced optimization for Hadoop and integration of oozie workflows. -Demonstrate proper use of File Connector stage. -Demonstrate use of DataStage to handle various types of data including unstructured, hierarchical, Cloud, and Hadoop. | 15% |
| Parallel Architecture | -Demonstrate proper use of data partitioning and collecting. -Demonstrate knowledge of parallel execution. | 9% |
| Job Components | -Demonstrate knowledge of Join, Lookup and Merge stages. -Demonstrate knowledge of Sort stage. -Demonstrate understanding of Aggregator stage. -Describe proper usage of change capture/change apply. -Demonstrate knowledge of real-time components. | 8% |
| Databases | -Demonstrate proper selection of database stages and database specific stage properties. -Identify source database options. -Demonstrate knowledge of target database options. -Demonstrate knowledge of the different SQL input/creation options and when to use each. | 6% |
| Metadata | -Demonstrate knowledge of framework schema. -Identify the method of importing, sharing, and managing metadata. -Demonstrate knowledge of runtime column propagation (RCP). | 5% |
| Job Management and Deployment | -Demonstrate knowledge of DataStage Designer Repository utilities such as advanced find, impact analysis, and job compare. -Articulate the change control process. -Knowledge of Source Code Control Integration. -Demonstrate the ability to define packages, import, and export using the ISTool utility. -Demonstrate the ability to perform admin tasks with tools such as Directory Admin. | 8% |
| Monitor and Troubleshoot | -Demonstrate knowledge of parallel job score. -Identify and define environment variables that control DataStage with regard to added functionality and reporting. -Given a process list, identify conductor, section leader, and player process. -Identify areas that may improve performance. -Demonstrate knowledge of runtime metadata analysis and performance monitoring. -Ability to monitor DataStage jobs using the Job Log and Operations Console. | 9% |
| Job Design | -Demonstrate knowledge of shared containers. -Describe how to minimize Sorts and repartitions. -Demonstrate knowledge of creating restart points and methodologies. -Explain the process necessary to run multiple copies of the source. -Knowledge of creating DataStage jobs that can be used as a service. -Knowledge of balanced optimization. -Describe the purpose and uses of parameter sets and how they compare with other approaches for parameterizing jobs. -Demonstrate the ability to create and use Data Rules using the Data Rules stage to measure the quality of data. -Demonstrate various methods of using DataStage to handle encrypted data. | 14% |
| Job Control and Runtime Management | -Demonstrate knowledge of message handlers. -Demonstrate the ability to use the dsjob command line utility. -Demonstrate ability to use job sequencers. -Create and manage encrypted passwords and credentials files. | 8% |
| Section | Objectives |
|---|---|
| Troubleshooting and Maintenance | - Logging and diagnostics - Common runtime errors - Debugging DataStage jobs |
| Data Integration and Connectivity | - File stages and external data sources - Database connectors - Metadata management |
| Job Development | - Data transformations and derivations - Stages and links configuration - Designing DataStage jobs |
| Job Sequencing and Control | - Control flow and conditional execution - Job sequences design |
| DataStage Architecture | - Parallel processing architecture - Server vs Parallel jobs concepts - Runtime environment and components |
| Performance Tuning and Optimization | - Resource optimization - Job monitoring and performance analysis - Parallelism tuning techniques |
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