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IBM C1000-173 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • Architect with Data Source Services: This domain measures expertise of a Data Integration Architect and includes architecting solutions with data replication technologies to synchronize data across systems. IBM Data Virtualization architecture enables access to and querying of disparate data sources without data movement. Architecting with watsonx.data focuses on building intelligent data lakes and query engines. Architecting with Db2-related services involves designing scalable data management and processing architectures using IBM's Db2 platforms.

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IBM Cloud Pak for Data v4.7 Architect Sample Questions (Q160-Q165):

NEW QUESTION # 160
In which situation would you specifically label nodes for DB2 workloads?

Answer: B


NEW QUESTION # 161
When migrating DataStage, what are key considerations? (Select two)

Answer: C,D


NEW QUESTION # 162
In the context of IBM Cloud Pak for Data, which statement best describes the purpose of using OpenShift Operators?

Answer: B


NEW QUESTION # 163
When creating a Db2 Big SQL service instance, which two service resource items should be taken into account when sizing the cluster?

Answer: A,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 # 164
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: B

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 # 165
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