C1000-173 Reliable Exam Prep, New C1000-173 Test Question
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IBM Cloud Pak for Data v4.7 Architect Sample Questions (Q183-Q188):
NEW QUESTION # 183
What is the significance of using platform connections in Data Replication on Cloud Pak for Data?
- A. Complicates data integration processes
- B. Decreases connectivity with data sources and targets
- C. Enhances interoperability and ease of configuration
- D. Isolates data environments
Answer: C
NEW QUESTION # 184
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 queue until one of the other concurrent jobs completes.
- B. The sixth job will start if resources are available and will automatic.
- C. The sixth job will fail and will need to be restarted.
Answer: A
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 # 185
What is the purpose of the IBM Data Replication service?
- A. To monitor database activity.
- B. To provide a unified view of disparate data sources.
- C. To integrate and synchronize data.
- D. To transform and validate data moving from source to target.
Answer: C
Explanation:
The IBM Data Replication service in Cloud Pak for Data is designed to integrate and synchronize data between various systems, ensuring that data in target systems is kept up-to-date with the source systems. It supports near real-time replication and change data capture (CDC) mechanisms, making it ideal for analytics environments that require continuous synchronization. The service is not a tool for database activity monitoring (A), creating unified virtual views (B), or performing heavy data transformations (D).
NEW QUESTION # 186
When selecting storage classes for Match 360, what factors should be considered?
- A. The storage class with the most attractive name
- B. None, as all storage classes are the same
- C. Compatibility with data structure and access patterns
- D. Only the name of the storage class
Answer: C
NEW QUESTION # 187
Which Db2 Big SQL component uses system resources efficiently to maximize throughput and minimize response time?
- A. Analyzer
- B. Scheduler
- C. Hive
- D. StreamThrough
Answer: D
Explanation:
StreamThrough is a high-performance component used in Db2 Big SQL within IBM Cloud Pak for Data that is optimized to manage data streams and queries efficiently. It is designed to maximize throughput and minimize query response times by optimizing memory usage, resource allocation, and processing logic.
Unlike Hive or Analyzer, which are used for query execution and analysis, StreamThrough enables efficient pipeline execution by streamlining data handling. Scheduler is used for job timing but does not influence runtime efficiency directly. StreamThrough is purpose-built to enhance performance through optimal resource usage.
NEW QUESTION # 188
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