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Machine learning Operations 16%
Data Governance 22%
Integration, Implementation, Deployment, and Scaling 10%
Build Data Science algorithms 14%
Analystics 17%
Cloud Pak for Data Artechitecure 21%
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Reference: https://www.ibm.com/training/certification/C0004601
| Section | Weight | Objectives |
|---|---|---|
| Machine Learning Operations | 16% | - Monitor deployed models inside Cloud Pak for Data - Manage risk and regulatory compliance using OpenPages - Monitor machine learning models running on an external platform - Key considerations when selecting a platform for model deployment - Workflow of deploying and monitoring models |
| Build Data Science Algorithms | 14% | - Differences between traditional programming and machine learning - Process and tools in Cloud Pak for Data to build, deploy, and monitor ML/AI algorithms - Collect, explore, and prepare data for ML/AI algorithms - Map business opportunities into a Data Science use case - Build and deploy ML/AI algorithms in Cloud Pak for Data |
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| Data Governance | 22% | - 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 - Use smart ingestion for auto cataloging - Capabilities of a data fabric topology - Define the Governance structure - Leverage the platform to understand data flow and usage - Explain the use of Guardium in auditing and monitoring data |
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| Analytics | 17% | - Map business opportunities into an Analytics use case - 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 |
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