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

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).

Training Data Statistics:Watson OpenScale needs to understand the characteristics of the data the model was trained on. This includes things like the distribution of features, sensitive attributes (for fairness monitoring), and how the model performed on this initial data. These "training data statistics" are crucial for:

Fairness Configuration:Recommending fairness attributes, reference, and monitored groups.

Bias Detection:Calculating fairness metrics (like disparate impact) by comparing runtime behavior to the learned training data distribution.

Explainability:Generating explanations by understanding the distribution of values in the training data to create meaningful perturbations.

Drift Detection:Building a drift detection model that compares runtime data to the training data to identify shifts.

While Watson OpenScale also consumespayload data(the data sent to the deployed model for predictions) at runtime to calculate various metrics and perform monitoring, the initial setup and the ability to generate meaningfulstatisticsfor things like fairness and drift fundamentally rely on understanding thetraining data

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.

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.

In IBM Cloud Pak for Data, a "Connection" is a configuration object that defines how to access an external data source, such as Db2, Oracle, or an S3 bucket. It does not involve moving or copying data. Instead, it acts as a reference to the external data source environment, enabling users to browse, query, and analyze the data in place. This approach supports virtualized access and governance while avoiding data duplication and ensuring data stays within its source system, aligning with enterprise data security policies.

The primary goal of the data privacy capabilities in IBM Cloud Pak for Data is to restrict access to sensitive data such as personally identifiable information (PII), financial records, or protected health information. This is achieved using Data Protection Rules, dynamic masking, and policy enforcement across services like IBM Knowledge Catalog and Data Virtualization. It ensures compliance with data governance policies and external regulations. While trusted data and platform security are important, those are addressed by other services and features.

After importing IBMKnowledge Accelerator(KA) assets using an API (or other methods), those assets — such as categories, terms, and relationships — are part ofgovernance artifactsin Cloud Pak for Data. However,to make them usable by specific users, youmust assign collaboratorsto the relevantcategories.

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

In an OpenShift environment that hosts IBM Cloud Pak for Data, all cluster nodes—including master and worker nodes—must have access to the container registry to pull required images during deployment and runtime. In scenarios involving custom images, some nodes may also need to push to the registry. While the bastion node may initiate the setup or mirror images, it is not the only node involved. Therefore, all nodes should be configured with both pull and, where applicable, push access to the registry to ensure consistent deployment and operations.

What sets DataStage apart in the IBM Cloud Pak for Data ecosystem is its high-performance parallel execution engine. While other tools may offer graphical interfaces and transformation libraries, DataStage is designed for enterprise-grade ETL with scalable parallelism. It optimizes performance by processing large data volumes using parallel pipelines across multiple cores or nodes. This architecture ensures faster execution and is ideal for complex data integration tasks.

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.

When selecting storage for Cloud Pak for Data, two critical considerations are:

Ensuring the storage supports the specific services being deployed. Not all services are compatible with every storage type (e.g., some services require block storage, others may support NFS).

The storage must provide sufficient I/O performance to meet the operational demands of the workloads.

Transmission speeds and throughput metrics are useful but not directly required as standalone criteria. Additionally, NFS is not universally supported by all services; some services specifically require RWO (ReadWriteOnce) or RWX (ReadWriteMany) access modes.

The Data Engineer role in IBM Cloud Pak for Data includes permissions necessary for managing the data pipeline lifecycle. This includes the ability to create projects, manage data assets, and configure data protection rules, which are critical for ensuring data privacy and governance. The role does not include platform-level administrative privileges like managing the platform or monitoring all workloads. Workflow management is typically assigned to users with broader project or orchestrator roles.

A Data Refinery flow is an ordered sequence of data operations applied to tabular data for preparation, cleansing, and transformation. Users can create a series of steps such as filtering, joining, aggregating, and applying custom expressions. The flow is saved and can be rerun on updated datasets to ensure consistency in data preparation. It is not a storage system (A), an ML model (B), or a visualization tool (D).

Access to Match 360 within IBM Cloud Pak for Data is role-based and governed by service groups. Even administrative users are not granted automatic access unless they are explicitly assigned to the appropriate Match 360 service group. This allows fine-grained control over who can access master data management capabilities. Service group membership defines the roles and privileges needed for interacting with Match 360 functionalities like entity resolution and golden record management.

Configuring access to a Git repository in Cloud Pak for Data projects allows teams to collaborate on code, notebooks, and assets while benefiting from version control and branching. This setup ensures that all project files can be tracked, reverted, or merged, enabling collaborative development and continuous integration workflows. It is not used for model deployment management (B) or visualization enhancements (C). Option D is unrelated to the actual purpose of Git integration.