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The IBM C1000-136 exam is a difficult exam. The exam is not only testing the student's knowledge about the subject, but also testing their skills and abilities. It is a mixture of multiple-choice questions and C++ programming questions.
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The IBM C1000-136 certification exams are difficult because there are no shortcuts or tricks that can be used by the student during this process. It is important for the students to practice as much as possible before taking the test so that they will be able to manage their time well during this process. Students should visit various websites that provide free practice questions and answers so that they can prepare themselves better before taking their exams. IBM C1000-136 exam dumps is one of the most popular online resources that can be used by students.
IBM C1000-136 Exam Topics
Integration, Implementation, Deployment, and Scaling 10%
Data Governance 22%
Build Data Science algorithms 14%
Cloud Pak for Data Artechitecure 21%
Analystics 17%
Machine learning Operations 16%
Reference: https://www.ibm.com/training/certification/C0004601
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IBM C1000-136 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Governance | 22% | - Capabilities of a data fabric topology - Define the Governance structure - Understand how workflow is used in Cloud Pak for Data - Explain the concepts of Knowledge Accelerators - Map business opportunities into a data governance use case - 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 2: Cloud Pak for Data Architecture | 21% | - Secure the solution and client data - Understand Cloud Pak for Data reliability options - Understand the underlying infrastructure and installation - Understand sizing and deployment options - Understand Cloud Pak for Data reference architecture |
| Topic 3: Build Data Science Algorithms | 14% | - Collect, explore, and prepare data for ML/AI algorithms - Build and deploy ML/AI algorithms in Cloud Pak for Data - Differences between traditional programming and machine learning - 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 |
| Topic 4: Machine Learning Operations | 16% | - 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 - Key considerations when selecting a platform for model deployment |
| Topic 5: Analytics | 17% | - 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 - Capabilities of business intelligence in Cloud Pak for Data |
| Topic 6: Integration, Implementation, Deployment, and Scaling | 10% | - 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 - Develop process to take a Data and AI solution from inception to production |
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