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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.
Reference: https://www.ibm.com/training/certification/C0004601
What is the target audience of the IBM C1000-136 Exam
The target audience of the IBM C1000-136 exam is mainly composed of IT professionals and IT managers who want to know more about the cloud computing. They can also be applied in any industry that is related to cloud computing.
The IBM C1000-136 exam is designed for individuals who have some knowledge about cloud computing but are not yet qualified enough to enter the industry. The test will help them get a better understanding of what it takes for them to become successful in this field. It also helps them see if they have the right skills and qualifications needed in order to take up a new career path. IBM C1000-136 exam dumps exam study guide provides you with the best way to prepare for the exam.
The IBM C1000-136 exam has two different tracks, which are called “Cloud Platform Administration” and “Cloud Service Administration”. The first track focuses on how to manage cloud platforms while the second track focuses on how to manage services offered by clouds. Both tracks contain questions that will help you determine whether or not you have what it takes so that you can decide whether or not you want to pursue further studies in this area or not.
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IBM C1000-136 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Build Data Science Algorithms | 14% | - Collect, explore, and prepare data for ML/AI algorithms - 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 - Build and deploy ML/AI algorithms in Cloud Pak for Data |
| Integration, Implementation, Deployment, and Scaling | 10% | - Integrate Business Applications using Cloud Pak for Data - 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 |
| Machine Learning Operations | 16% | - Monitor deployed models inside Cloud Pak for Data - Workflow of deploying and monitoring models - Key considerations when selecting a platform for model deployment - Manage risk and regulatory compliance using OpenPages - Monitor machine learning models running on an external platform |
| Data Governance | 22% | - Understand how workflow is used in Cloud Pak for Data - Explain the use of Guardium in auditing and monitoring data - Map business opportunities into a data governance use case - Define the Governance structure - Capabilities of a data fabric topology - Explain the concepts of Knowledge Accelerators - Leverage the platform to understand data flow and usage - Use smart ingestion for auto cataloging |
| 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 |
| 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 sizing and deployment options - Understand Cloud Pak for Data reference architecture |
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