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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 Format:Multiple Response, Multiple Choice, Scenario-based
Passing Score:63%
Certificate Validity Period:3 Years
Available Languages:English
Exam Price:$250 USD
Exam Duration:90 minutes
Real Exam Qty:63
Related Certifications:IBM Certified Administrator - Cloud Pak for Data
IBM Certified Data Engineer - Cloud Pak for Data
IBM Certified Analyst - Cloud Pak for Data
Sample Questions:IBM C1000-173 Sample Questions
Exam Way:Online proctored or at authorized Pearson VUE test centers
Pre Condition:No formal prerequisites required, but recommend having prior experience with IBM Cloud Pak for Data administration, Red Hat OpenShift Container Platform, and general data architecture principles.
Official Syllabus URL:https://www.ibm.com/certify/exam?id=173

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IBM C1000-173 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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 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.

IBM Cloud Pak for Data v4.7 Architect Sample Questions (Q164-Q169):

NEW QUESTION # 164
What is the significance of understanding storage classes in Watson OpenScale?

Answer: D


NEW QUESTION # 165
In the context of data replication, what is a key benefit of minimizing the impact of outages?

Answer: B


NEW QUESTION # 166
Which two Cloud Pak for Data services implement data masking to support secure data sharing?

Answer: B,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 # 167
What is a Data Refinery flow in Cloud Pak for Data?

Answer: C

Explanation:
A Data Refinery flow in Cloud Pak for Data is an ordered set of data operations (transformations) that are applied to tabular data. It is used to cleanse, shape, and prepare data for analysis or machine learning. Users can apply filters, joins, aggregations, and custom expressions. It is not a storage location (A), ML model (B), or a visualization tool (D), though visual previews of transformed data are available.


NEW QUESTION # 168
When evaluating Cloud Pak for Data, what are the potential advantages of choosing a SaaS model over a non-SaaS model?

Answer: A,B,C


NEW QUESTION # 169
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