IBM Cloud Pak for Data V4.7 Architect C1000-173 Exam Questions

Answer : C

Data Privacy Masking flows in IBM Cloud Pak for Data utilize Apache Spark as the underlying data processing engine. Spark enables large-scale, distributed data masking operations for structured data, supporting high-performance transformations and compliance with privacy regulations. While DataStage can perform similar operations, the default and recommended engine for Data Privacy flows in CP4D is Spark. dbt and Presto are not used for this masking functionality.

Answer : B

In IBM 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.

With auto-scaling disabled, the system does not add or remove DataStage engine pods dynamically to handle workload changes.

The maximum concurrent jobs setting 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) will not fail immediately; instead, these jobs are placed in a queue.

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

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

Answer : B

Within IBM Knowledge Catalog as part of IBM Cloud Pak for Data, governing data lakes is enabled via integration with Data Virtualization. This approach supports automated data discovery, cataloging, tagging, and virtualization, allowing users to access enterprise data virtually---without physical movement. Policies and governance metadata are applied automatically to virtualized assets, enabling secure and efficient data consumption. Manual processes are not required for discovery, and data is masked selectively based on policies---not completely masked without user intervention. Thus automation and virtualization are central, making statement B correct.

Answer : A, D

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.

Answer : C

In Watson Pipelines, the component that enables users to define and assign values that can be referenced later in the pipeline---including by downstream components like DataStage---is Set User Variables. This component allows the user to create name-value pairs and store them as environment variables, which are accessible to DataStage and other execution blocks. This ensures dynamic parameter passing and enhances pipeline reusability. The other options listed do not correspond to valid Watson Pipeline components as defined in the official Cloud Pak for Data 4.7 release.

Answer : C

watsonx.data uses Apache Iceberg tables as the open table format for data sharing across platforms like Db2 Warehouse, Netezza, and other compatible data management solutions. Iceberg provides a transactional and schema-evolution-friendly table layer, allowing multiple engines to read and write data concurrently. This approach avoids proprietary loaders or simple file transfers and ensures efficient interoperability between different systems.

Answer : C

After importing IBM Knowledge Accelerator (KA) assets using an API (or other methods), those assets --- such as categories, terms, and relationships --- are part of governance artifacts in Cloud Pak for Data. However, to make them usable by specific users, you must assign collaborators to the relevant categories.

This ensures users have the appropriate permissions (e.g., to view, curate, or manage terms) within the Information Governance Catalog (IGC) or Watson Knowledge Catalog.