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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
Real Exam Qty:	60-70
Exam Price:	USD 200 (varies by region)
Exam Format:	Multiple response, Multiple choice
Exam Duration:	90 minutes
Available Languages:	English
Related Certifications:	IBM Cloud Pak for Data Administrator IBM Cloud Pak for Data Data Engineer
Certificate Validity Period:	3 years
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	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 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 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.
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 Cloud Pak for Data v4.7 Architect Sample Questions (Q52-Q57):

NEW QUESTION # 52
How are caches defined in IBM Data Virtualization?

A. Caches are suggested based on statistics collected during data source configuration.
B. They are created automatically based on usage patterns.
C. DataStage flows are created in a project for maintaining caches.
D. Caches are manually defined based on recommendations and monitoring data.

Answer: D

Explanation:
In IBM Data Virtualization, caches must be manually defined by administrators. While monitoring and query performance statistics can guide where caching would be beneficial, the creation and configuration of caches (e.g., refresh schedules and scope) are manual tasks. There is no automated cache creation mechanism (A), nor are DataStage flows used for cache maintenance (B). Suggestions based on statistics (D) may assist administrators, but they do not automatically create the caches.

NEW QUESTION # 53
How do storage classes affect Watson Discovery's performance?

A. By changing the language of stored data
B. They modify the user interface theme
C. They have no impact on performance
D. By influencing indexing speed and query performance

Answer: D

NEW QUESTION # 54
What are two considerations when choosing the type of storage for Cloud Pak for Data?

A. The storage data has a throughput minimum of 16 MB/s per disk.
B. NFS storage supports all Cloud Pak for Data services.
C. The storage provides sufficient I/O performance.
D. The storage supports the services that will be installed.
E. The storage network must support a minimum transmission speed of 4 Gbps.

Answer: C,D

Explanation:
When selecting storage for Cloud Pak for Data, two critical considerations are:
Ensuring the storage supports the specific services being deployed. Not all services are compatible with every storage type (e.g., some services require block storage, others may support NFS). The storage must provide sufficient I/O performance to meet the operational demands of the workloads. Transmission speeds and throughput metrics are useful but not directly required as standalone criteria. Additionally, NFS is not universally supported by all services; some services specifically require RWO (ReadWriteOnce) or RWX (ReadWriteMany) access modes.

NEW QUESTION # 55
What is the purpose of configuring access to a Git repository associated with a project in Cloud Pak for Data?

A. To enhance data visualization in JupyterLab or RStudio.
B. To collaborate with others, manage file versions, and enable branching.
C. To delete the repository and create a new one.
D. To manage the deployment of a model to a project space.

Answer: B

Explanation:
Configuring access to a Git repository in Cloud Pak for Data projects allows teams to collaborate on code, notebooks, and assets while benefiting from version control and branching. This setup ensures that all project files can be tracked, reverted, or merged, enabling collaborative development and continuous integration workflows. It is not used for model deployment management (B) or visualization enhancements (C). Option D is unrelated to the actual purpose of Git integration.

NEW QUESTION # 56
What is the significance of planning an environment for multiple Cloud Pak deployments?

A. Ensuring a one-size-fits-all approach to resource allocation
B. Customizing each deployment based on tenant needs and usage patterns
C. To reduce the operational complexity and management overhead
D. Avoiding the use of projects and namespaces for simplicity

Answer: B

NEW QUESTION # 57
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