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IBM C1000-173 Exam Syllabus Topics:

Topic	Details
Topic 1	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.
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	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.

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IBM Cloud Pak for Data v4.7 Architect Sample Questions (Q48-Q53):

NEW QUESTION # 48
Which type of search allows Watson Discovery to find the most relevant material in documents?

A. Total
B. Keyword
C. Faceted
D. Link

Answer: B

Explanation:
Watson Discovery employs advanced natural language processing and ranking algorithms that begin with keyword-based search. This type of search enables the tool to locate and return the most relevant document passages based on the presence and context of keywords in user queries. While it may also use metadata filters (facets), the core retrieval method remains keyword-driven to find relevance at both document and passage levels.

NEW QUESTION # 49
What can be used to deliver business-ready data to feed AI and analytics projects?

A. IBM Knowledge Catalog for Cloud Pak for Data
B. IBM Data Catalog for Cloud Pak for Data
C. Watson Machine Learning for Cloud Pak for Data
D. Watson Machine Learning Accelerator on Cloud Pak for Data

Answer: A

Explanation:
IBM Knowledge Catalog is the core governance and cataloging service within Cloud Pak for Data that enables the delivery of trusted, business-ready data to AI and analytics pipelines. It provides data lineage, metadata management, access policies, and quality scores, ensuring data consumers use curated and compliant data. Watson Machine Learning and its accelerator are focused on model training and inference, while IBM Data Catalog is a former term replaced by Knowledge Catalog in recent versions.

NEW QUESTION # 50
Which statement is true about governing data lakes in IBM Knowledge Catalog?

A. It increases the time and effort required for data discovery and cataloging.
B. All data is masked with no user intervention.
C. It relies on manual processes for accessing enterprise data.
D. It automates data discovery and provides access to enterprise data through virtualization.

Answer: D

Explanation:
Within IBM Knowledge Catalog as part of IBM Cloud Pak for Data, governing data lakes is enabled via integration with Data Virtualization. This approach supports automated data discovery, cataloging, tagging, and virtualization, allowing users to access enterprise data virtually-without physical movement. Policies and governance metadata are applied automatically to virtualized assets, enabling secure and efficient data consumption. Manual processes are not required for discovery, and data is masked selectively based on policies-not completely masked without user intervention. Thus automation and virtualization are central, making statement B correct.

NEW QUESTION # 51
Why is tethering projects to the IBM Cloud Pak for Data control plane beneficial?

A. Disallows resource monitoring and management
B. It prevents any form of project organization
C. Enhances project isolation while enabling resource access control
D. It ensures all projects are untethered for greater flexibility

Answer: C

NEW QUESTION # 52
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 # 53
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