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What is the IBM C1000-136 Exam?
IBM C1000-136 Exam is a certification exam that tests your knowledge of the IBM C1000-136 exam. The IBM Cloudpak for Data v4.x Solution Architecture certification exam is designed for those who have at least four years of experience with the IBM Cloudpak for Data v4.x solution architecture framework and who want to deepen their understanding of the subject matter. IBM C1000-136 exam dumps are designed to provide you with the knowledge you need to pass the IBM C1000-136 exam on the first try.
The IBM C1000-136 exam covers all of the material covered in the IBM cloudpak for data v4.x solution architecture framework course as well as additional topics such as how to manage a large scale project, how to use agile techniques, how to use a DevOps approach, how to collaborate with other teams, how to manage security risks, etc.
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 the underlying infrastructure and installation - Understand Cloud Pak for Data reliability options - Secure the solution and client data - Understand Cloud Pak for Data reference architecture - Understand sizing and deployment options |
| Topic 2: Data Governance | 22% | - Explain the concepts of Knowledge Accelerators - Explain the use of Guardium in auditing and monitoring data - Map business opportunities into a data governance use case - Leverage the platform to understand data flow and usage - Use smart ingestion for auto cataloging - Define the Governance structure - Capabilities of a data fabric topology - Understand how workflow is used in Cloud Pak for Data |
| Topic 3: Integration, Implementation, Deployment, and Scaling | 10% | - Develop process to take a Data and AI solution from inception to production - Integrate Business Applications using Cloud Pak for Data - Develop a strategy to monitor the Data and AI platform - Accelerate the solution using Industry Accelerators and External Data sets |
| Topic 4: Build Data Science Algorithms | 14% | - Build and deploy ML/AI algorithms in Cloud Pak for Data - Differences between traditional programming and machine learning - 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 - Collect, explore, and prepare data for ML/AI algorithms |
| Topic 5: Machine Learning Operations | 16% | - Monitor machine learning models running on an external platform - Key considerations when selecting a platform for model deployment - Workflow of deploying and monitoring models - Monitor deployed models inside Cloud Pak for Data - Manage risk and regulatory compliance using OpenPages |
| Topic 6: Analytics | 17% | - 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 - Map business opportunities into an Analytics use case |
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