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IBM C1000-173 Exam Overview:

Certification Vendor:	IBM
Exam Name:	IBM Cloud Pak for Data v4.7 Architect
Exam Number:	C1000-173
Exam Price:	USD 200 (varies by region)
Real Exam Qty:	60-70
Exam Duration:	90 minutes
Exam Format:	Multiple choice, Multiple response
Available Languages:	English
Certificate Validity Period:	3 years
Related Certifications:	IBM Cloud Pak for Data Data Engineer IBM Cloud Pak for Data Administrator
Recommended Training:	IBM Cloud Pak for Data Learning Resources
Exam Registration:	IBM Certification Portal
Sample Questions:	IBM C1000-173 Sample Questions
Exam Way:	Online proctored exam (remote) or authorized testing center
Pre Condition:	No strict mandatory prerequisites, but strong familiarity with IBM Cloud Pak for Data architecture and services is recommended.
Official Syllabus URL:	https://www.ibm.com/training/certification

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IBM C1000-173 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 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.

IBM Cloud Pak for Data v4.7 Architect Sample Questions (Q51-Q56):

NEW QUESTION # 51
How does the integration of security with the platform benefit Data Replication on Cloud Pak for Data?

A. Enhances the risk of data breaches
B. Ensures secure data transfer and access
C. Reduces compliance with industry standards
D. Compromises data integrity

Answer: B

NEW QUESTION # 52
In which situation would you specifically label nodes for DB2 workloads?

A. When node differentiation is unnecessary
B. When all nodes are utilized for identical tasks
C. To distinguish between different data or workload types
D. For reducing overall system performance

Answer: C

NEW QUESTION # 53
What can be used to deliver business-ready data to feed AI and analytics projects?

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

Answer: B

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 # 54
Which plug-in is used by the Cloud Pak for Data Audit Logging service to forward audit records to a SIEM system?

A. Fluentd output
B. Logstash output
C. OSS/J
D. Apache Kafka output

Answer: A

Explanation:
The Audit Logging service in IBM Cloud Pak for Data uses Fluentd as the core log forwarding mechanism. Fluentd output plug-ins are configured to route audit logs to external SIEM systems such as Splunk or QRadar. These plug-ins are versatile and support multiple formats and transport protocols. Other options listed--like Logstash, OSS/J, or Kafka--are not the designated default forwarding mechanisms used within the CP4D Audit Logging architecture.

NEW QUESTION # 55
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 fail and will need to be restarted.
B. The sixth job will start if resources are available and will automatic.
C. The sixth job will queue until one of the other concurrent jobs completes.

Answer: C

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 # 56
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