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IBM C1000-173 Exam Syllabus Topics:

TopicDetails
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
  • 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 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.

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IBM Cloud Pak for Data v4.7 Architect Sample Questions (Q157-Q162):

NEW QUESTION # 157
When planning the integration of IBM Cloud Pak for Data with existing enterprise systems, which aspect is crucial for ensuring seamless connectivity and data exchange? (Select two)

Answer: A,D


NEW QUESTION # 158
How can multi-tenancy be achieved with multiple instances of Cloud Pak for Data?

Answer: D


NEW QUESTION # 159
Which of the following is a key feature of IBM Cloud Pak for Data's architecture?

Answer: A

Explanation:
IBM Cloud Pak for Data is built on a containerized architecture, leveraging Kubernetes and OpenShift. This allows the platform to be flexible, scalable, and portable across various environments, including on-premises, public clouds, and hybrid clouds.


NEW QUESTION # 160
A customer wants to manage Cloud Pak for Data secrets via an existing supported vault system.
What is needed to integrate any supported vault systems into Cloud Pak for Data?

Answer: B

Explanation:
To integrate a supported vault system with IBM Cloud Pak for Data, the fully qualified URL of the external vault is a required component for establishing communication between Cloud Pak for Data and the vault. This is typically configured in the external secrets manager settings. While authentication credentials (like client certificates or keys) are also necessary depending on the authentication method used, the fully qualified URL is universally required to locate and connect to the vault.
IBM Cloud Pak for Data supports integration with vaults such as:
HashiCorp Vault
AWS Secrets Manager
Azure Key Vault
IBM Key Protect
For more details, refer to:
IBM Cloud Pak for Data: Using external secrets managers


NEW QUESTION # 161
Which two Cloud Pak for Data services implement data masking to support secure data sharing?

Answer: D,E

Explanation:
Data masking in IBM Cloud Pak for Data is primarily supported by IBM Knowledge Catalog and Data Privacy services. IBM Knowledge Catalog enforces masking through Data Protection Rules, which dynamically mask sensitive fields when data is accessed through virtualized connections.
Data Privacy allows creating masking flows and rules that transform datasets while maintaining usability for analytics, ensuring sensitive data is hidden or obfuscated. DataStage and Db2 Data Gate are ETL and data replication tools, respectively, and SPSS is an analytics tool, none of which natively implement comprehensive masking as a core capability.


NEW QUESTION # 162
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