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IBM C1000-173 Exam Syllabus Topics:

Section	Weight	Objectives
Installation and Configuration	20%	- Install Cloud Pak for Data on Red Hat OpenShift 1. Manage certificates and security settings 2. Configure the ZenService and internal authentication 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. Use OpenShift monitoring tools and CP4D logging 2. Troubleshoot common pod and service failures - User and access management 1. Manage users, groups, and role-based access control (RBAC) 2. Integrate with external identity providers (LDAP/SAML)
Security	15%	- Implement security best practices 1. Manage secrets and encryption at rest 2. Understand compliance and auditing requirements 3. Configure security context constraints (SCCs)
Planning and Designing a Cloud Pak for Data Solution	20%	- 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) - Size the environment 1. Select appropriate OpenShift cluster configurations 2. Calculate resource requirements based on user load and services
Data Virtualization, Governance, and Analytic Integration	10%	- Data Governance and Quality 1. Understand the purpose of data governance (Steward, Catalog) 2. Integrate with data fabric and knowledge graph concepts - Manage data sources and virtualization 1. Create and manage virtualized tables 2. Configure connections to databases and data warehouses
Cloud Pak for Data Architecture	20%	- Explain the Cloud Pak for Data architecture, key components, and services 1. Describe the purpose of Zen Core, Redis, and Postgres 2. Understand how services are deployed as pods in the OpenShift cluster 3. Explain the integration with Red Hat OpenShift Container Platform - Describe deployment models and licensing 1. Understand subscription models and capacity requirements 2. Differentiate between self-managed and cloud-managed deployments

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IBM Cloud Pak for Data v4.7 Architect Sample Questions (Q33-Q38):

NEW QUESTION # 33
What is a primary advantage of using Watson Pipelines in AI solution architecture?

A. Providing end-to-end data processing automation
B. Isolating data silos
C. Decreasing the transparency of data processing
D. Reducing data integrity

Answer: A

NEW QUESTION # 34
When planning the implementation of Cloud Pak for Data, which of the following services might be considered essential based on the business requirements?

A. Data governance
B. AI workflows
C. Storage optimization
D. Network monitoring

Answer: A,B

NEW QUESTION # 35
After importing IBM Knowledge Accelerator assets using an API endpoint, what change must be made before the assets can be used by the appropriate users?

A. Add collaborators to the Knowledge Accelerators categories.
B. Ensure a shared credential has been configured.
C. Ensure the user permission role is active.
D. Add users to the Knowledge Accelerators security group.

Answer: A

Explanation:
After importing IBM Knowledge Accelerator (KA) assets using an API (or other methods), those assets -- such as categories, terms, and relationships -- are part of governance artifacts in Cloud Pak for Data. However, to make them usable by specific users, you must assign collaborators to the relevant categories.
This ensures users have the appropriate permissions (e.g., to view, curate, or manage terms) within the Information Governance Catalog (IGC) or Watson Knowledge Catalog.

NEW QUESTION # 36
When creating a Db2 Big SQL service instance, which two service resource items should be taken into account when sizing the cluster?

A. Number of physical cores
B. Number of workers
C. Number of virtual cores
D. Maximum expected throughput
E. Amount of memory

Answer: B,E

Explanation:
When provisioning a Db2 Big SQL service instance in IBM Cloud Pak for Data, key considerations include the amount of memory available per worker node and the total number of worker nodes. These factors directly affect query execution parallelism and system capacity. The system does not distinguish between physical and virtual cores at the configuration level.
Throughput is an outcome of sizing, not a direct sizing parameter. Therefore, memory and number of workers are the two most critical sizing metrics.

NEW QUESTION # 37
Which statement describes MPP (Massively Parallel Processing) Database architecture?

A. An analytics environment that improves performance by dividing the data across many nodes.
B. A data warehouse that needs all compute nodes to access a shared data store simultaneously.
C. A transactional system which uses multiple nodes for maximum availability.
D. Two or more databases kept in sync via two-phase commit.

Answer: A

Explanation:
MPP, or Massively Parallel Processing, is a database architecture model where data is divided and processed across multiple compute nodes in parallel. Each node works independently on a portion of the data, dramatically improving query performance and throughput for analytics workloads. This model is ideal for big data and analytical queries, not transactional workloads. It differs from shared-disk models or replication strategies like two-phase commit. The correct definition involves distributed data and parallel query execution, as described in option C.

NEW QUESTION # 38
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