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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
Get to know about the Key Features of the IBM Cloud Pak for Data v4.x Solution Architecture Certification
The IBM Cloud Pak for Data v4.x Solution Architecture certification helps you to understand the key features of the IBM Cloud Pak for Data v4.x solution architecture. With this certification, you will be able to understand how the solution architecture works and what are its key components.
The IBM Cloud Pak for Data v4.x solution is a complete data lake that provides a single source of truth for all analytics applications and data. It is designed to meet the needs of business users who want to run their own analytics or use pre-built analytic tools within their own environments, whether they're on premises or in the cloud. IBM C1000-136 exam dumps can also be used by students who want to enter the IT industry.
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IBM C1000-136 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Build Data Science Algorithms | 14% | - 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 - Differences between traditional programming and machine learning - Collect, explore, and prepare data for ML/AI algorithms |
| Cloud Pak for Data Architecture | 21% | - Secure the solution and client data - Understand the underlying infrastructure and installation - Understand Cloud Pak for Data reliability options - Understand sizing and deployment options - Understand Cloud Pak for Data reference architecture |
| Machine Learning Operations | 16% | - Monitor machine learning models running on an external platform - Monitor deployed models inside Cloud Pak for Data - Workflow of deploying and monitoring models - Manage risk and regulatory compliance using OpenPages - Key considerations when selecting a platform for model deployment |
| Analytics | 17% | - Capabilities of business intelligence in Cloud Pak for Data - Map business opportunities into an Analytics use case - Difference between Descriptive, Prescriptive, Predictive, Diagnostic, and Cognitive Analytics - Capabilities of AI for financial operations in Cloud Pak for Data |
| Integration, Implementation, Deployment, and Scaling | 10% | - Develop process to take a Data and AI solution from inception to production - Develop a strategy to monitor the Data and AI platform - Accelerate the solution using Industry Accelerators and External Data sets - Integrate Business Applications using Cloud Pak for Data |
| Data Governance | 22% | - Explain the use of Guardium in auditing and monitoring data - Define the Governance structure - Capabilities of a data fabric topology - Use smart ingestion for auto cataloging - Understand how workflow is used in Cloud Pak for Data - Leverage the platform to understand data flow and usage - Explain the concepts of Knowledge Accelerators - Map business opportunities into a data governance use case |
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