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IBM C1000-173 Exam Syllabus Topics:

Topic	Details
Topic 1	Plan for a Cloud Pak for Data Implementation: This section of the exam measures the skills of an Implementation Consultant and covers determining which Cloud Pak for Data services to deploy based on organizational needs. It involves sizing the Kubernetes OpenShift cluster appropriately for workload demands and planning backup and restore strategies to ensure data protection. Planning for high availability and disaster recovery is essential to maintain uninterrupted service. Multi-tenancy requirements must be understood to support multiple user groups securely on shared infrastructure. Migration requirements need assessment to transition existing data and workloads smoothly.
Topic 2	Architect with AI Series: This section measures the skills of an AI Solution Architect and includes designing architectures for solutions involving various IBM Watson AI services. Architecting with Watson Assistant involves creating conversational AI interfaces. Watson Discovery solutions focus on building cognitive search and content analytics applications. Watson Pipelines solutions involve orchestrating data science workflows. Watson OpenScale architectures enable AI model monitoring and governance. Architecting with Match 360 supports personalized engagement by integrating multi-channel customer insights.
Topic 3	Architect with Analytic Services: This domain targets a Data Analytics Architect and focuses on designing solutions using analytic components of Cloud Pak for Data. Architecting with DataStage involves creating data integration and ETL pipelines. Data Refinery architectures prepare and cleanse data for analysis. Db2 Big SQL solutions address querying large distributed data sets with SQL, facilitating analytics across diverse data sources.
Topic 4	Security Requirements: This domain targets a Security Architect and focuses on planning security for a Cloud Pak for Data deployment. It includes managing certificates that secure communications, identity management systems, and access and authorization controls to enforce secure and compliant user and service interactions. The auditing features and their integration with enterprise audit systems are crucial to ensure traceability and accountability. Asset interchange security involves safeguarding data movement between services.

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IBM Cloud Pak for Data v4.7 Architect Sample Questions (Q139-Q144):

NEW QUESTION # 139
Which statement describes MPP (Massively Parallel Processing) Database architecture?

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

Answer: B

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 # 140
What advantage does data replication hold over ETL tools for multi-site data distribution?

A. Centralized data storage
B. Immediate data availability across sites
C. Increased data isolation
D. Slower data dissemination

Answer: B

NEW QUESTION # 141
How do Cloud Pak for Data administrators obtain access to Match 360?

A. Each user who is assigned to a service group has access.
B. Administrative users must belong to the appropriate service group.
C. Administrative users automatically have access.
D. Each user is automatically granted read access.

Answer: B

Explanation:
Access to Match 360 within IBM Cloud Pak for Data is role-based and governed by service groups. Even administrative users are not granted automatic access unless they are explicitly assigned to the appropriate Match 360 service group. This allows fine-grained control over who can access master data management capabilities. Service group membership defines the roles and privileges needed for interacting with Match 360 functionalities like entity resolution and golden record management.

NEW QUESTION # 142
How does the IBM Data Virtualization service virtualize files in shared directories?

A. Users add shared file systems in the UI.
B. It scans its network for open file shares.
C. A remote connector is installed and run on the source server.
D. Administrators configure FTP connections to the file source.

Answer: C

Explanation:
To virtualize files that reside in shared directories (e.g., NFS, SMB, or other on-premises sources), IBM Data Virtualization uses a remote connector agent. This remote connector is installed and executed on the source server to enable secure access and metadata extraction.
The service does not scan networks automatically nor rely on FTP. Directly adding file shares via the UI is not sufficient without the backend connector in place, which acts as a secure communication bridge.

NEW QUESTION # 143
An architect is working with a team to configure Dynamic Workload Management for a single DataStage instance on Cloud Pak for Data.
Auto-scaling has been disabled and the maximum concurrent jobs has been set to 5.
What will happen if a sixth concurrent job is executed?

A. The sixth job will start if resources are available and will automatic.
B. The sixth job will queue until one of the other concurrent jobs completes.
C. The sixth job will fail and will need to be restarted.

Answer: B

Explanation:
InIBM Cloud Pak for Data version 4.7, when configuring Dynamic Workload Management (DWM) for IBM DataStage, the system controls job concurrency based on the maximum concurrent jobs setting and auto- scaling configuration.
* Withauto-scaling disabled, the system does not add or remove DataStage engine pods dynamically to handle workload changes.
* Themaximum concurrent jobssetting limits the number of jobs that can run simultaneously on a single DataStage instance.
* If the number of concurrent jobs reaches the maximum limit (in this case, 5), any additional job requests (such as the sixth job) willnot fail immediately; instead, these jobs are placed in aqueue.
* The queued jobs remain pending until one of the running jobs completes, freeing up capacity for the next job to start.
This queuing behavior ensures workload stability and prevents resource exhaustion by enforcing the concurrency limit strictly when auto-scaling is turned off.
Exact extract from IBM Cloud Pak for Data 4.7 documentation:
"When auto-scaling is disabled, the maximum concurrency limit set on the DataStage instance controls how many jobs can run simultaneously. Jobs submitted beyond this limit are queued and wait for running jobs to complete before starting execution."
-IBM Cloud Pak for Data v4.7, DataStage Dynamic Workload Management section References:
IBM Cloud Pak for Data 4.7 Documentation - DataStage and Dynamic Workload Management IBM Knowledge Center for Cloud Pak for Data v4.7: https://www.ibm.com/docs/en/cloud-paks/cp-data/4.7?
topic=management-dynamic-workload

NEW QUESTION # 144
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