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What are the main benefits of IBM C1000-136 Certification
The IBM Cloud Pak for Data v4.x Solution Architecture course is a unique, end-to-end course that covers the entire lifecycle of developing and implementing a data lake solution on IBM Cloud Platform. It begins with an overview of the general concepts and terminates with an in-depth look at the architecture and design choices that are needed to create a successful data lake. IBM C1000-136 exam dumps exam study guide helps you prepare for the exam by providing you with the right information about the topics covered in the exam.
This course will provide you with the knowledge and skills you need to successfully develop and implement a data lake solution on IBM Cloud Platform. You'll learn how to bring together multiple existing tools, including Big Data tools such as Hadoop and Spark, along with NoSQL tools such as Cassandra, JSON databases such as MongoDB, and file storage systems such as S3. You'll also see how you can use these tools together to create a complete data lake solution.
IBM C1000-136 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Integration, Implementation, Deployment, and Scaling | 10% | - Develop a strategy to monitor the Data and AI platform - Integrate Business Applications using Cloud Pak for Data - Accelerate the solution using Industry Accelerators and External Data sets - Develop process to take a Data and AI solution from inception to production |
| Topic 2: Cloud Pak for Data Architecture | 21% | - Understand the underlying infrastructure and installation - Understand Cloud Pak for Data reliability options - Understand sizing and deployment options - Secure the solution and client data - Understand Cloud Pak for Data reference architecture |
| Topic 3: Analytics | 17% | - 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 - Map business opportunities into an Analytics use case |
| Topic 4: Machine Learning Operations | 16% | - Key considerations when selecting a platform for model deployment - Monitor machine learning models running on an external platform - Workflow of deploying and monitoring models - Monitor deployed models inside Cloud Pak for Data - Manage risk and regulatory compliance using OpenPages |
| Topic 5: Data Governance | 22% | - Define the Governance structure - Capabilities of a data fabric topology - Explain the concepts of Knowledge Accelerators - Use smart ingestion for auto cataloging - Map business opportunities into a data governance use case - Understand how workflow is used in Cloud Pak for Data - Leverage the platform to understand data flow and usage - Explain the use of Guardium in auditing and monitoring data |
| Topic 6: Build Data Science Algorithms | 14% | - Build and deploy ML/AI algorithms in Cloud Pak for Data - Map business opportunities into a Data Science use case - 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 - Differences between traditional programming and machine learning |
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