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IBM C1000-173 Exam Syllabus Topics:

SectionWeightObjectives
Administration and Monitoring15%- Monitor and maintain the environment
  • 1. Use OpenShift monitoring tools and CP4D logging
  • 2. Troubleshoot common pod and service failures
- User and access management
  • 1. Integrate with external identity providers (LDAP/SAML)
  • 2. Manage users, groups, and role-based access control (RBAC)
Planning and Designing a Cloud Pak for Data Solution20%- Size the environment
  • 1. Calculate resource requirements based on user load and services
  • 2. Select appropriate OpenShift cluster configurations
- Plan network and storage infrastructure
  • 1. Plan for high availability and disaster recovery
  • 2. Design network policies and routes
  • 3. Determine storage class requirements (File, Block, Object)
Cloud Pak for Data Architecture20%- Explain the Cloud Pak for Data architecture, key components, and services
  • 1. Understand how services are deployed as pods in the OpenShift cluster
  • 2. Describe the purpose of Zen Core, Redis, and Postgres
  • 3. Explain the integration with Red Hat OpenShift Container Platform
- Describe deployment models and licensing
  • 1. Differentiate between self-managed and cloud-managed deployments
  • 2. Understand subscription models and capacity requirements
Installation and Configuration20%- Add and configure services
  • 1. Install and configure core add-on services (e.g., Data Virtualization, Watson Studio)
  • 2. Manage version upgrades of the platform and services
- Install Cloud Pak for Data on Red Hat OpenShift
  • 1. Configure the ZenService and internal authentication
  • 2. Use the IBM Cloud Pak for Data installer and operators
  • 3. Manage certificates and security settings
Security15%- Implement security best practices
  • 1. Manage secrets and encryption at rest
  • 2. Understand compliance and auditing requirements
  • 3. Configure security context constraints (SCCs)
Data Virtualization, Governance, and Analytic Integration10%- Manage data sources and virtualization
  • 1. Create and manage virtualized tables
  • 2. Configure connections to databases and data warehouses
- Data Governance and Quality
  • 1. Integrate with data fabric and knowledge graph concepts
  • 2. Understand the purpose of data governance (Steward, Catalog)

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IBM Cloud Pak for Data v4.7 Architect Sample Questions (Q146-Q151):

NEW QUESTION # 146
Which IBM Cloud Pak for Data service would you use for building AI models and running experiments?

Answer: D

Explanation:
Watson Studio is the primary service for building AI models, running experiments, and training machine learning models within IBM Cloud Pak for Data. It provides tools for data scientists to build, train, and deploy AI models.


NEW QUESTION # 147
Which two storage requirements are essential to achieve High Availability?

Answer: C,D


NEW QUESTION # 148
Which Watson Pipeline component manages pipeline errors, typically used with DataStage?

Answer: D

Explanation:
In Watson Pipelines within IBM Cloud Pak for Data, error management is handled by the Error Handling component. This feature allows developers and pipeline administrators to define how pipeline failures are processed-whether to stop execution, continue, or trigger alternate flows. It ensures controlled behavior in response to job failures, particularly in complex ETL pipelines like those built with DataStage. Error Handling is a configurable element of pipeline orchestration and is typically used to enhance fault tolerance and control error propagation in production workflows.


NEW QUESTION # 149
If a Cloud Pak for Data cluster is air-gapped, it is unable to reach the internet, and a private registry must be configured for installations and upgrades. Beyond container images, what other components need to be addressed?

Answer: A

Explanation:
In air-gapped (offline) environments, it's not just the container images that require preparation.
Package managers like pip (for Python) and other utilities commonly attempt to pull dependencies from public internet repositories. These must be redirected to internal mirrors or handled via offline bundles. If left unaddressed, certain operations within components like Jupyter notebooks, Watson Studio, or pipelines may fail. License validation and monitoring do not inherently require internet access, and data movement between nodes is part of normal cluster function--not something to be blocked.


NEW QUESTION # 150
What outcomes can be achieved from Match 360?

Answer: B

Explanation:
Match 360 is IBM's master data management (MDM) service integrated into Cloud Pak for Data. It produces master data views along with statistics, graphs, and insights that allow users to explore, analyze, and understand their master data entities (e.g., customers, products). While it does integrate data from disparate sources, its focus is on consolidating and providing master data analysis rather than virtual views (B) or conversational analytics (A).


NEW QUESTION # 151
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