PDF Version DemoWhat 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.
Reference: https://www.ibm.com/training/certification/C0004601
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You need to know the basics of Data v4.x Solution Architecture
IBM Cloud Pak for Data v4.x Solution Architecture is a fully integrated software package that combines the IBM Cloud platform and IBM Analytics solutions. It is designed to help you manage your data throughout its lifecycle, from collection to analysis and analytics, in an agile manner.
The solution architecture provides a comprehensive view of your data through a single pane of glass, enabling you to view all aspects of your data as well as perform analytics on it in real time. The solution architecture also enables you to gain insights into what matters most by identifying patterns within your data that can be used to improve business decisions and operations. IBM C1000-136 exam dumps is designed to provide you with the knowledge you need to pass the IBM C1000-136 exam on the first try.
IBM C1000-136 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Cloud Pak for Data Architecture | 21% | - Understand sizing and deployment options - Understand Cloud Pak for Data reliability options - Understand Cloud Pak for Data reference architecture - Understand the underlying infrastructure and installation - Secure the solution and client data |
| Topic 2: Data Governance | 22% | - Explain the use of Guardium in auditing and monitoring data - Capabilities of a data fabric topology - Understand how workflow is used in Cloud Pak for Data - Use smart ingestion for auto cataloging - Define the Governance structure - Map business opportunities into a data governance use case - Explain the concepts of Knowledge Accelerators - Leverage the platform to understand data flow and usage |
| Topic 3: Machine Learning Operations | 16% | - 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 - Manage risk and regulatory compliance using OpenPages - Workflow of deploying and monitoring models |
| Topic 4: Integration, Implementation, Deployment, and Scaling | 10% | - 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 - Accelerate the solution using Industry Accelerators and External Data sets |
| Topic 5: Analytics | 17% | - Capabilities of AI for financial operations in Cloud Pak for Data - Map business opportunities into an Analytics use case - Difference between Descriptive, Prescriptive, Predictive, Diagnostic, and Cognitive Analytics - Capabilities of business intelligence in Cloud Pak for Data |
| Topic 6: 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 - Map business opportunities into a Data Science use case - 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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