IBM C1000-136 exam covers various topics, including the architecture and components of IBM Cloud Pak for Data v4.x, deployment methodologies, security, and management of the platform. C1000-136 exam also tests the candidate's ability to integrate IBM Cloud Pak for Data v4.x with other IBM and third-party products to create comprehensive data and AI solutions.
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IBM C1000-136 (IBM Cloud Pak for Data v4.x Solution Architecture) certification exam is a valuable credential for technical professionals who want to prove their expertise in designing, architecting, and implementing IBM Cloud Pak for Data solutions. IBM Cloud Pak for Data v4.x Solution Architecture certification validates the candidate's technical skills and knowledge and can help them stand out in the job market. With the increasing demand for data and AI solutions, earning the IBM C1000-136 certification can open up new career opportunities for professionals.
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IBM Cloud Pak for Data solution helps organizations to automate their data-driven decision-making process. With this certification, individuals can demonstrate their proficiency in building, deploying, and managing IBM Cloud Pak for Data solutions. IBM Cloud Pak for Data v4.x Solution Architecture certification also helps individuals to enhance their career prospects as many organizations are actively looking for professionals with IBM Cloud Pak for Data certification.
Reference: https://www.ibm.com/training/certification/C0004601
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 Cloud Pak for Data reference architecture - Understand sizing and deployment options |
| Topic 2: Build Data Science Algorithms | 14% | - Process and tools in Cloud Pak for Data to build, deploy, and monitor 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 - Collect, explore, and prepare data for ML/AI algorithms |
| Topic 3: Analytics | 17% | - Map business opportunities into an Analytics use case - Difference between Descriptive, Prescriptive, Predictive, Diagnostic, and Cognitive Analytics - Capabilities of business intelligence in Cloud Pak for Data - 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 - Develop process to take a Data and AI solution from inception to production - Integrate Business Applications using Cloud Pak for Data |
| Topic 5: Machine Learning Operations | 16% | - Workflow of deploying and monitoring models - Monitor deployed models inside Cloud Pak for Data - Key considerations when selecting a platform for model deployment - Monitor machine learning models running on an external platform - Manage risk and regulatory compliance using OpenPages |
| Topic 6: Data Governance | 22% | - Use smart ingestion for auto cataloging - Capabilities of a data fabric topology - Explain the use of Guardium in auditing and monitoring data - Explain the concepts of Knowledge Accelerators - Understand how workflow is used in Cloud Pak for Data - Define the Governance structure - Leverage the platform to understand data flow and usage - Map business opportunities into a data governance use case |
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