Free IBM C1000-173 Practice Exam with Questions & Answers | Set: 2

To virtualize files that reside in shared directories (e.g., NFS, SMB, or other on-premises sources), IBM Data Virtualization uses a remote connector agent. This remote connector is installed and executed on the source server to enable secure access and metadata extraction. The service does not scan networks automatically nor rely on FTP. Directly adding file shares via the UI is not sufficient without the backend connector in place, which acts as a secure communication bridge.

You can use the IBM Software Hub monitoring and alerting framework to monitor the state of the platform. You can set up events to alert when action is needed, based on thresholds that you define.

By default, IBM Software Hub is initialized with one monitor that runs every ten minutes. The diagnostic monitor records the status of deployments, StatefulSets, and persistent volume claims. It also tracks your system usage of virtual processors (vCPUs) and memory. The data that is collected can be used for analysis and to alert customers in a production environment based on set alert rules.

MPP, or Massively Parallel Processing, is a database architecture model where data is divided and processed across multiple compute nodes in parallel. Each node works independently on a portion of the data, dramatically improving query performance and throughput for analytics workloads. This model is ideal for big data and analytical queries, not transactional workloads. It differs from shared-disk models or replication strategies like two-phase commit. The correct definition involves distributed data and parallel query execution, as described in option C.

In on-premises deployments of IBM Cloud Pak for Data:

Administrators have full control over the number of OpenShift nodes, unlike cloud-managed environments where node scaling may be automatic or abstracted.

Persistent storage is always required and configured by the infrastructure team to meet service requirements and ensure data availability.

Auto-scaling of compute and automatic updates of services are not handled by IBM in on-prem setups.

Network security responsibilities also lie with the deploying organization, not IBM, in an on-premises model.

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.

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

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).

As mentioned previously, IBM Data Virtualization provides a single governed access point for data residing across heterogeneous systems. It reduces data movement by creating virtualized views, enabling organizations to query multiple sources seamlessly while adhering to centralized security and governance policies. This capability supports analytics and reporting without duplicating or moving data.