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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 Data Governance Services: This section evaluates skills of a Data Governance Specialist, covering the architecture of knowledge catalogs to enable data discovery and management. Data Privacy architectures ensure compliance with regulatory requirements around data protection. Knowledge Accelerators involve designing solutions that speed up data governance processes through predefined policies and templates.
Topic 3
  • 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.
Topic 4
  • 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 5
  • 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.

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IBM Cloud Pak for Data v4.7 Architect Sample Questions (Q180-Q185):

NEW QUESTION # 180
Which two of the following can be used with Watson Pipelines?

Answer: A,D

Explanation:
Watson Pipelines in Cloud Pak for Data support orchestration of diverse workload types including notebooks (Python or similar interactive environments) and scripts such as Bash. These pipeline components allow integration of notebook cells or shell scripts as tasks. There is no built#in support for executing PowerShell tasks directly (unless wrapped in Bash-like containers), and Postgres is used as a data source-not a pipeline component type. While Db2 Big SQL can be invoked within a notebook or script, it is not itself a pipeline component. Therefore the supported types in pipelines are notebooks and Bash scripts.


NEW QUESTION # 181
How are Knowledge Accelerators deployed?

Answer: D

Explanation:
Knowledge Accelerators are a part of IBM Knowledge Catalog in IBM Cloud Pak for Data and provide predefined industry-specific business terms and relationships. These accelerators are not included by default with sample assets nor automatically deployed through the Cloud Pak for Data marketplace. Instead, they are imported using dedicated API endpoints provided by IBM for Knowledge Accelerators. Deployment involves uploading the accelerator assets using the IBM Knowledge Accelerator API, and once imported, they can be customized and published within the governance framework. This approach provides the flexibility required for various enterprise governance models.


NEW QUESTION # 182
How does watsonx.data provide data sharing between Db2 Warehouse, Netezza, and any other data management solution?

Answer: D

Explanation:
watsonx.data uses Apache Iceberg tables as the open table format for data sharing across platforms like Db2 Warehouse, Netezza, and other compatible data management solutions. Iceberg provides a transactional and schema-evolution-friendly table layer, allowing multiple engines to read and write data concurrently. This approach avoids proprietary loaders or simple file transfers and ensures efficient interoperability between different systems.


NEW QUESTION # 183
What are the key benefits of utilizing Watson Pipelines in data processing? (Select two)

Answer: A,B


NEW QUESTION # 184
What registry permissions does OpenShift cluster node require?

Answer: B

Explanation:
In an OpenShift environment that hosts IBM Cloud Pak for Data, all cluster nodes-including master and worker nodes-must have access to the container registry to pull required images during deployment and runtime. In scenarios involving custom images, some nodes may also need to push to the registry. While the bastion node may initiate the setup or mirror images, it is not the only node involved. Therefore, all nodes should be configured with both pull and, where applicable, push access to the registry to ensure consistent deployment and operations.


NEW QUESTION # 185
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