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Last Updated: Aug 02, 2026
No. of Questions: 65 Questions & Answers with Testing Engine
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IBM C1000-136 exam covers a wide range of topics related to Cloud Pak for Data v4.x, including data integration, data governance, machine learning, and analytics. It is a comprehensive exam that tests an individual's knowledge and skills in various areas related to IBM Cloud Pak for Data. Passing C1000-136 exam not only validates an individual's expertise in Cloud Pak for Data solutions but also demonstrates their commitment to ongoing learning and professional growth in the field of data and AI.
Reference: https://www.ibm.com/training/certification/C0004601
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IBM C1000-136 exam is designed for IT professionals who want to demonstrate their knowledge and skills in IBM Cloud Pak for Data v4.x Solution Architecture. IBM Cloud Pak for Data v4.x Solution Architecture certification exam is intended to validate the candidate's understanding of the key concepts, tools, and technologies used in designing, deploying, and managing IBM Cloud Pak for Data solutions.
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| Section | Weight | Objectives |
|---|---|---|
| Machine Learning Operations | 16% | - Monitor deployed models inside Cloud Pak for Data - Key considerations when selecting a platform for model deployment - Monitor machine learning models running on an external platform - Workflow of deploying and monitoring models - Manage risk and regulatory compliance using OpenPages |
| Data Governance | 22% | - Leverage the platform to understand data flow and usage - Define the Governance structure - Capabilities of a data fabric topology - Use smart ingestion for auto cataloging - Explain the use of Guardium in auditing and monitoring data - Understand how workflow is used in Cloud Pak for Data - Map business opportunities into a data governance use case - Explain the concepts of Knowledge Accelerators |
| Integration, Implementation, Deployment, and Scaling | 10% | - Integrate Business Applications using Cloud Pak for Data - Develop a strategy to monitor the Data and AI platform - Develop process to take a Data and AI solution from inception to production - Accelerate the solution using Industry Accelerators and External Data sets |
| Build Data Science Algorithms | 14% | - Differences between traditional programming and machine learning - Build and deploy ML/AI algorithms in Cloud Pak for Data - Process and tools in Cloud Pak for Data to build, deploy, and monitor ML/AI algorithms - Map business opportunities into a Data Science use case - Collect, explore, and prepare data for ML/AI algorithms |
| Analytics | 17% | - Capabilities of business intelligence in Cloud Pak for Data - Difference between Descriptive, Prescriptive, Predictive, Diagnostic, and Cognitive Analytics - Capabilities of AI for financial operations in Cloud Pak for Data - Map business opportunities into an Analytics use case |
| Cloud Pak for Data Architecture | 21% | - Understand Cloud Pak for Data reference architecture - Understand sizing and deployment options - Understand Cloud Pak for Data reliability options - Understand the underlying infrastructure and installation - Secure the solution and client data |
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