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IBM C1000-173 Popular Exams - C1000-173 Exams

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IBM C1000-173 Exam Syllabus Topics:

Topic
Details

Topic 1

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 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

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 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 5

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.

>> IBM C1000-173 Popular Exams <<

C1000-173 Exams & Valid Test C1000-173 Format

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IBM Cloud Pak for Data v4.7 Architect Sample Questions (Q186-Q191):

NEW QUESTION # 186
Which of the following best defines Disaster Recovery in IT systems?

A. A plan to restore normal business operations after a disaster
B. A process to increase data vulnerability
C. A method to ensure continuous system overload
D. A strategy to maintain high system latency

Answer: A

NEW QUESTION # 187
Which of the following are components of identity management systems? (Select two)

A. Risk analysis
B. User provisioning
C. Authentication
D. Tokenization

Answer: B,C

NEW QUESTION # 188
How does watsonx.data provide data sharing between Db2 Warehouse, Netezza, and any other data management solution?

A. JSON file format
B. watsonx.data proprietary fast loader
C. Iceberg tables
D. Secure File Transfer Protocol (SFTP)

Answer: C

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 # 189
Which component must be enabled in order to render business lineage when installing IBM Knowledge Catalog?

A. Automated metadata lineage
B. Knowledge graph
C. Data quality
D. Data graph

Answer: B

Explanation:
Business Lineage and Knowledge Graph:IBM Knowledge Catalog leverages a Knowledge Graph to store and visualize the relationships between various assets, including data assets, governance artifacts (like business terms), and the flow of data. Business lineage, which shows the end-to-end journey of data in business terms, relies heavily on these interconnected relationships within the Knowledge Graph.
Documentation Confirmation:IBM's documentation explicitly states: "To view lineage, you can have any role in a catalog.Optional This feature is not available by default. Knowledge graph must be installed with IBM Knowledge Catalog, IBM Knowledge Catalog Premium, or IBM Knowledge Catalog Standard.
For information on installing knowledge graph, see Specifying additional installation options in the IBM Software Hub documentation." (Source: IBM Documentation on Lineage). It further clarifies, "Enable knowledge graph to gain access to the lineage feature, business-term relationship search, and the relationship explorer."

NEW QUESTION # 190
Which two Cloud Pak for Data services support the multi-tenancy mechanism of installing the service once and provisioning the workloads in tethered projects?

A. Watson Pipelines
B. Db2
C. Cognos Analytics
D. IBM Data Virtualization
E. Analytics Engine Powered by Apache Spark

Answer: D,E

Explanation:
Cloud Pak for Data supports service-level multi-tenancy by enabling some services to be installed centrally and then provisioned into user "tethered" namespaces (projects). In version 4.7, Analytics Engine (Apache Spark) and IBM Data Virtualization services support this tethered#project model: they can be installed once and then instantiated per project across multiple tenants. Core services like Db2, Cognos Analytics, and Watson Pipelines in 4.7 require installation per instance and do not yet support tethered#namespace provisioning via multi#tenant model.

NEW QUESTION # 191
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