IBM Cloud Pak for Data v4.x Solution Architecture: C1000-136 Exam

"IBM Cloud Pak for Data v4.x Solution Architecture", also known as C1000-136 exam, is a IBM Certification. With the complete collection of questions and answers, UpdateDumps has assembled to take you through 65 Q&As to your C1000-136 Exam preparation. In the C1000-136 exam resources, you will cover every field and category in IBM Certification Certification helping to ready you for your successful IBM Certification.

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  • Updated on: Sep 14, 2026
  • No. of Questions: 65 Questions & Answers

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  • Updated on: Sep 14, 2026
  • No. of Questions: 65 Questions & Answers

IBM C1000-136 Exam Topics

  • Data Governance 22%

  • Integration, Implementation, Deployment, and Scaling 10%

  • Cloud Pak for Data Artechitecure 21%

  • Machine learning Operations 16%

  • Analystics 17%

  • Build Data Science algorithms 14%

Reference: https://www.ibm.com/training/certification/C0004601

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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:

SectionWeightObjectives
Cloud Pak for Data Architecture21%- Understand sizing and deployment options
- Understand Cloud Pak for Data reference architecture
- Secure the solution and client data
- Understand the underlying infrastructure and installation
- Understand Cloud Pak for Data reliability options
Build Data Science Algorithms14%- Build and deploy ML/AI algorithms in Cloud Pak for Data
- 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
Machine Learning Operations16%- Manage risk and regulatory compliance using OpenPages
- Workflow of deploying and monitoring models
- Key considerations when selecting a platform for model deployment
- Monitor deployed models inside Cloud Pak for Data
- Monitor machine learning models running on an external platform
Integration, Implementation, Deployment, and Scaling10%- Develop process to take a Data and AI solution from inception to production
- 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
Data Governance22%- Capabilities of a data fabric topology
- Leverage the platform to understand data flow and usage
- Use smart ingestion for auto cataloging
- Map business opportunities into a data governance use case
- Explain the use of Guardium in auditing and monitoring data
- Explain the concepts of Knowledge Accelerators
- Understand how workflow is used in Cloud Pak for Data
- Define the Governance structure
Analytics17%- Capabilities of AI for financial operations in Cloud Pak for Data
- Difference between Descriptive, Prescriptive, Predictive, Diagnostic, and Cognitive Analytics
- Map business opportunities into an Analytics use case
- Capabilities of business intelligence in Cloud Pak for Data

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