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IBM Cloud Pak for Data v4.x Solution Architecture is a fully integrated software package that combines the IBM Cloud platform and IBM Analytics solutions. It is designed to help you manage your data throughout its lifecycle, from collection to analysis and analytics, in an agile manner.
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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 Cloud Pak for Data reference architecture - Understand the underlying infrastructure and installation - Understand Cloud Pak for Data reliability options - Secure the solution and client data - Understand sizing and deployment options |
| Topic 2: Data Governance | 22% | - Explain the concepts of Knowledge Accelerators - Understand how workflow is used in Cloud Pak for Data - Map business opportunities into a data governance use case - Capabilities of a data fabric topology - Define the Governance structure - Leverage the platform to understand data flow and usage - Use smart ingestion for auto cataloging - Explain the use of Guardium in auditing and monitoring data |
| Topic 3: Analytics | 17% | - Capabilities of business intelligence in Cloud Pak for Data - Difference between Descriptive, Prescriptive, Predictive, Diagnostic, and Cognitive Analytics - Map business opportunities into an Analytics use case - Capabilities of AI for financial operations in Cloud Pak for Data |
| Topic 4: Integration, Implementation, Deployment, and Scaling | 10% | - Accelerate the solution using Industry Accelerators and External Data sets - Develop a strategy to monitor the Data and AI platform - Integrate Business Applications using Cloud Pak for Data - Develop process to take a Data and AI solution from inception to production |
| Topic 5: Machine Learning Operations | 16% | - Key considerations when selecting a platform for model deployment - Workflow of deploying and monitoring models - Manage risk and regulatory compliance using OpenPages - Monitor deployed models inside Cloud Pak for Data - Monitor machine learning models running on an external platform |
| Topic 6: Build Data Science Algorithms | 14% | - Collect, explore, and prepare data for ML/AI algorithms - Differences between traditional programming and machine learning - Map business opportunities into a Data Science use case - 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 |
