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| Section | Weight | Objectives |
|---|
| Data Virtualization, Governance, and Analytic Integration | 10% | - Data Governance and Quality
- 1. Integrate with data fabric and knowledge graph concepts
- 2. Understand the purpose of data governance (Steward, Catalog)
- Manage data sources and virtualization
- 1. Configure connections to databases and data warehouses
- 2. Create and manage virtualized tables
|
| Planning and Designing a Cloud Pak for Data Solution | 20% | - 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. Design network policies and routes
- 2. Determine storage class requirements (File, Block, Object)
- 3. Plan for high availability and disaster recovery
|
| Security | 15% | - Implement security best practices
- 1. Configure security context constraints (SCCs)
- 2. Manage secrets and encryption at rest
- 3. Understand compliance and auditing requirements
|
| Installation and Configuration | 20% | - Install Cloud Pak for Data on Red Hat OpenShift
- 1. Configure the ZenService and internal authentication
- 2. Manage certificates and security settings
- 3. Use the IBM Cloud Pak for Data installer and operators
- 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
|
| Administration and Monitoring | 15% | - Monitor and maintain the environment
- 1. Troubleshoot common pod and service failures
- 2. Use OpenShift monitoring tools and CP4D logging
- User and access management
- 1. Manage users, groups, and role-based access control (RBAC)
- 2. Integrate with external identity providers (LDAP/SAML)
|
| Cloud Pak for Data Architecture | 20% | - Describe deployment models and licensing
- 1. Differentiate between self-managed and cloud-managed deployments
- 2. Understand subscription models and capacity requirements
- Explain the Cloud Pak for Data architecture, key components, and services
- 1. Understand how services are deployed as pods in the OpenShift cluster
- 2. Explain the integration with Red Hat OpenShift Container Platform
- 3. Describe the purpose of Zen Core, Redis, and Postgres
|
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IBM Cloud Pak for Data v4.7 Architect Sample Questions (Q93-Q98):
NEW QUESTION # 93
Insurance industry datasets frequently include personally identifiable information (PII) and many data analysts need access to datasets but not to PII.
Which Cloud Pak for Data services leverage Data Protection Rules?
- A. IBM Data Virtualization, Data Privacy, and IBM Knowledge Catalog
- B. DataStage, SPSS Modeler, and Python Notebooks
- C. Watson Discovery and Watson Assistant
- D. Data Refinery, Watson Pipeline, and Watson Studio
Answer: A
Explanation:
IBM Cloud Pak for Data includes built-in Data Protection Rules to enforce access control on sensitive data, such as PII. These rules are integrated directly into services like IBM Data Virtualization, Data Privacy, and IBM Knowledge Catalog. When analysts or applications access data through these services, the platform automatically masks, obfuscates, or restricts access to sensitive fields based on the defined policies. This ensures compliance with data privacy regulations and organizational security policies without manual intervention.
NEW QUESTION # 94
How does IBM Data Virtualization aid in the creation of a Data Fabric?
- A. It is a replication tool that brings data from all sources together in one location.
- B. It suspends access to a data source once a usage threshold is exceeded.
- C. It allows data engineers to customize access to data across multiple data sources, then publish the resulting objects in a catalog.
- D. It uses AI to predict where data will be used and relocated data for the best performance.
Answer: C
NEW QUESTION # 95
Which of the following are components of identity management systems? (Select two)
- A. Authentication
- B. User provisioning
- C. Tokenization
- D. Risk analysis
Answer: A,B
NEW QUESTION # 96
Why is it essential to integrate Watson OpenScale in an AI solution architecture?
- A. To diminish the role of data scientists
- B. To increase data encryption standards
- C. To reduce the computation power required
- D. To provide AI model fairness, explainability, and audit trails
Answer: D
NEW QUESTION # 97
What are two ways to customize Knowledge Accelerators to meet specific requirements?
- A. Change Knowledge Accelerator content inline.
- B. Place Knowledge Accelerator content in a "development" vocabulary separate from the main stream "enterprise vocabulary".
- C. Use a GitHub Repo to manage any changes.
- D. Create a separate project for any customized content.
- E. Customize the content in a separate namespace.
Answer: B,D
Explanation:
Customization of Knowledge Accelerators in IBM Cloud Pak for Data is a structured process to preserve the integrity of base content while allowing for extension. The recommended approaches include:
Creating a separate project for customizations, so that changes are isolated and easily managed without affecting the source accelerator.
Using a "development" vocabulary where custom terms and structures are created. This is separate from the "enterprise vocabulary," which contains the unmodified, original Knowledge Accelerator content. Inline editing of the original content is discouraged. Use of GitHub or namespaces is not part of the official customization workflow.
NEW QUESTION # 98
......
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