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IBM Cloud Pak for Data v4.7 Architect Sample Questions (Q119-Q124):

NEW QUESTION # 119
How are caches defined in IBM Data Virtualization?

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

Answer: C

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 # 120
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. Watson Machine Learning for Cloud Pak for Data
C. Watson Machine Learning Accelerator on Cloud Pak for Data
D. IBM Data Catalog for 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 # 121
Are there any special considerations for the client to migrate existing server jobs to DataStage in Cloud Pak for Data?

A. Server jobs are automatically migrated to Watson Pipeline flows.
B. Server jobs migrate with no changes.
C. Server jobs migrate directly to parallel jobs that always require modification.
D. Server jobs can be converted to parallel jobs prior to migration using MettleCI.

Answer: D

Explanation:
Legacy DataStage server jobs are not automatically compatible with DataStage on Cloud Pak for Data, which uses a parallel engine architecture. MettleCI is the recommended tool to convert server jobs into parallel jobs before migration. This conversion allows reusability and ensures the migrated jobs can run efficiently in the CP4D environment. Direct migration without modification (option D) is not possible, and they do not migrate to Watson Pipelines (option A).

NEW QUESTION # 122
For achieving optimal performance in IBM Cloud Pak for Data, which storage solution should be utilized for analytical workloads requiring high IOPS?

A. Object storage
B. Block storage
C. SSD-based storage
D. File storage

Answer: C

NEW QUESTION # 123
What are the critical metrics to understand in a Disaster Recovery plan?

A. Maximum Tolerable Downtime (MTD) and Minimum Necessary Downtime (MND)
B. System Downtime Duration (SDD) and Data Access Time (DAT)
C. Peak Load Time (PLT) and Off-Peak Recovery Time (OPRT)
D. Recovery Time Objective (RTO) and Recovery Point Objective (RPO)

Answer: D

NEW QUESTION # 124
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