IBM C1000-084 certification exam consists of 60 multiple-choice questions that must be completed within 90 minutes. C1000-084 exam is available in English and Japanese languages. C1000-084 exam covers various topics such as IBM Cloud Pak for Data V3.x architecture, data virtualization, data governance, data science, and machine learning.
The C1000-084 exam covers a wide range of topics, including planning, deploying, and configuring IBM Cloud Pak for Data V3.x, designing and implementing data and AI workflows, securing IBM Cloud Pak for Data V3.x solutions, and integrating IBM Cloud Pak for Data V3.x with other technologies. C1000-084 exam also tests the ability to understand the business requirements of clients and translate them into technical solutions, as well as the ability to troubleshoot and optimize IBM Cloud Pak for Data V3.x solutions. Passing C1000-084 exam is an essential step towards becoming an IBM Cloud Pak for Data V3.x Associate Architect, and it demonstrates a high level of expertise in designing and deploying IBM Cloud Pak for Data V3.x solutions.
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IBM C1000-084 Exam Syllabus Topics:
| Section | Objectives |
| Cloud Pak for Data Architecture Overview | - Core architecture components
- 1. Data virtualization and integration layers
- 2. Platform services and microservices architecture
- Deployment models
- 1. On-premises deployment
- 2. Red Hat OpenShift-based deployment
|
| Data Management and Governance | - Data governance policies
- 1. Access control and compliance
- 2. Data stewardship roles
- Data catalog and metadata management
- 1. Data lineage and classification
- 2. IBM Watson Knowledge Catalog concepts
|
| Data Integration and Engineering | - Data transformation tools
- 1. Streaming data integration concepts
- 2. Data Refinery usage
- Data ingestion and pipelines
- 1. DataStage integration
- 2. ETL/ELT processes
|
| Security and Platform Administration | - Platform management
- 1. Resource management on OpenShift
- 2. Monitoring and logging
- Security model
- 1. Role-based access control (RBAC)
- 2. Authentication and authorization
|
| AI and Analytics Integration | - Machine learning lifecycle
- 1. Model training and deployment
- 2. AutoAI capabilities
- Analytics services
- 1. Visualization and reporting tools
- 2. Notebook environments
|