Pass the IBM IBM Data and AI: Data and AI C1000-173 Questions and answers with CertsForce

Passthrough route configuration on OpenShift is the only route type that preserves the original TLS connection from the client to the backend service. This is essential for supporting client certificate authentication, as the certificate must be passed directly to the application without termination or inspection at the router level. Edge and Re-encrypt routes terminate the TLS connection at the OpenShift router or re-encrypt it with a new certificate, making them unsuitable for mutual TLS (mTLS) or client certificate scenarios.

DataStage promotes modularity and reusability through the use of components such as job stages, parameters, parameter sets, and environment variables. Parameters and parameter sets allow dynamic configuration of jobs, enabling reuse across environments and reducing hardcoding. Environment variables allow users to define global job behavior across projects. These elements significantly enhance development efficiency and pipeline portability. Other features like schema drift and ELT modes support execution flexibility, but they are not primarily focused on reusability.

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.

Collaborators in IBM Knowledge Catalog are granted access to data assets that have been properly governed and made available through connections. Once a connection is established by an administrator or asset owner, users with collaborator roles can access the data without needing to re-enter credentials. This simplifies secure data consumption and aligns with enterprise access control policies. They do not see underlying credentials, and access is not limited to document types like PDFs.

Customization of Knowledge Accelerators in IBM Cloud Pak for Data is a structured process to preserve the integrity of base content while allowing for extension. The recommended approaches include:

Creating a separate project for customizations, so that changes are isolated and easily managed without affecting the source accelerator.

Using a "development" vocabulary where custom terms and structures are created. This is separate from the "enterprise vocabulary," which contains the unmodified, original Knowledge Accelerator content.

Inline editing of the original content is discouraged. Use of GitHub or namespaces is not part of the official customization workflow.

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.

Applications interact with Db2 Big SQL using industry-standard protocols. The most common and supported interface is through a REST (Representative State Transfer) API endpoint. REST endpoints allow for external applications to query, manage, and manipulate data within Big SQL using simple HTTP calls. None of the other options—SLEEP, SNORE, or DREAM—are valid or recognized interfaces in IBM Cloud Pak for Data or Db2 Big SQL documentation.

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.

IBM Cloud Pak for Data supports NFS‑backed persistent volumes (RWX storage class) for services that require access to shared file storage. Among the available services, IBM Knowledge Catalog and Watson Discovery are known to support NFS storage classes in installation configuration (e.g. “managed‑nfs‑storage”) for persistent metadata and document storage. Other services like watsonx Assistant, Watson Knowledge Studio, and Planning Analytics use different storage mechanisms and do not necessarily support NFS shared storage in the standard CP4D 4.7 deployment.

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