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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.
The C1000-136 exam has complex programming concepts, such as pointers, arrays, structures and functions. The student needs to learn how to use these concepts in order to deal with the complexity of the IBM C1000-136 exam.
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
Machine learning Operations 16%
Build Data Science algorithms 14%
Integration, Implementation, Deployment, and Scaling 10%
Data Governance 22%
Cloud Pak for Data Artechitecure 21%
Analystics 17%
Reference: https://www.ibm.com/training/certification/C0004601
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You need to know the basics of Data v4.x Solution Architecture
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IBM C1000-136 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Machine Learning Operations | 16% | - Key considerations when selecting a platform for model deployment - Manage risk and regulatory compliance using OpenPages - Workflow of deploying and monitoring models - Monitor deployed models inside Cloud Pak for Data - Monitor machine learning models running on an external platform |
| 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 |
| 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 |
| Data Governance | 22% | - Explain the use of Guardium in auditing and monitoring data - Map business opportunities into a data governance use case - Explain the concepts of Knowledge Accelerators - 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 - Define the Governance structure |
| Build Data Science Algorithms | 14% | - Differences between traditional programming and machine learning - 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 - Map business opportunities into a Data Science use case - Build and deploy ML/AI algorithms in Cloud Pak for Data |
| 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 |
