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Reference: https://www.ibm.com/training/certification/C0004601
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IBM C1000-136 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Cloud Pak for Data Architecture | 21% | - Understand sizing and deployment options - Secure the solution and client data - Understand Cloud Pak for Data reliability options - Understand Cloud Pak for Data reference architecture - Understand the underlying infrastructure and installation |
| Topic 2: Machine Learning Operations | 16% | - Workflow of deploying and monitoring models - Key considerations when selecting a platform for model deployment - Monitor deployed models inside Cloud Pak for Data - Monitor machine learning models running on an external platform - Manage risk and regulatory compliance using OpenPages |
| Topic 3: Data Governance | 22% | - Capabilities of a data fabric topology - Leverage the platform to understand data flow and usage - Map business opportunities into a data governance use case - Define the Governance structure - Understand how workflow is used in Cloud Pak for Data - Explain the use of Guardium in auditing and monitoring data - Use smart ingestion for auto cataloging - Explain the concepts of Knowledge Accelerators |
| Topic 4: Integration, Implementation, Deployment, and Scaling | 10% | - Integrate Business Applications using Cloud Pak for Data - Accelerate the solution using Industry Accelerators and External Data sets - Develop process to take a Data and AI solution from inception to production - Develop a strategy to monitor the Data and AI platform |
| Topic 5: Analytics | 17% | - Map business opportunities into an Analytics use case - Capabilities of AI for financial operations in Cloud Pak for Data - Capabilities of business intelligence in Cloud Pak for Data - Difference between Descriptive, Prescriptive, Predictive, Diagnostic, and Cognitive Analytics |
| Topic 6: Build Data Science Algorithms | 14% | - Map business opportunities into a Data Science use case - Process and tools in Cloud Pak for Data to build, deploy, and monitor ML/AI algorithms - Differences between traditional programming and machine learning - Build and deploy ML/AI algorithms in Cloud Pak for Data - Collect, explore, and prepare data for ML/AI algorithms |
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