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C1000-136 PDF Practice Q&A's
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How difficult is the IBM C1000-136 Exam?
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.
Reference: https://www.ibm.com/training/certification/C0004601
What are the prerequisites for IBM C1000-136 Exam
Candidates must pass IBM Cloud pak for data v4.x administration to get the certification.
The prerequisites for the IBM C1000-136 exam are as follows:
A certified IT professional with four to five years of experience.
A strong knowledge of system development and deployment methodologies.
Familiarity with basic Java programming and scripting language, such as JavaScript, JQuery or PHP.
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IBM C1000-136 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Analytics | 17% | - Capabilities of AI for financial operations in Cloud Pak for Data - 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 |
| Machine Learning Operations | 16% | - Workflow of deploying and monitoring models - Manage risk and regulatory compliance using OpenPages - Monitor machine learning models running on an external platform - Key considerations when selecting a platform for model deployment - Monitor deployed models inside Cloud Pak for Data |
| Cloud Pak for Data Architecture | 21% | - Understand Cloud Pak for Data reliability options - Understand the underlying infrastructure and installation - Secure the solution and client data - Understand Cloud Pak for Data reference architecture - Understand sizing and deployment options |
| Data Governance | 22% | - Leverage the platform to understand data flow and usage - Capabilities of a data fabric topology - Explain the use of Guardium in auditing and monitoring data - Use smart ingestion for auto cataloging - Understand how workflow is used in Cloud Pak for Data - Define the Governance structure - Explain the concepts of Knowledge Accelerators - Map business opportunities into a data governance use case |
| 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 |
| Integration, Implementation, Deployment, and Scaling | 10% | - Accelerate the solution using Industry Accelerators and External Data sets - Integrate Business Applications using Cloud Pak for Data - Develop process to take a Data and AI solution from inception to production - Develop a strategy to monitor the Data and AI platform |
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