IBM Cloud Pak for Data v4.x Solution Architect Practice Exam 2025: Latest Questions

A solution architect is designing IBM Cloud Pak for Data on Red Hat OpenShift for multiple internal teams. The teams want isolated workspaces but shared platform services and centralized administration. Which design best meets this requirement?

A data engineering team needs to ingest data from an on-premises relational database into a governed analytics environment in Cloud Pak for Data. They require bulk loads and periodic incremental updates with minimal custom code. Which approach is most appropriate?

A data science team wants to train and deploy models and expose them to applications through a managed REST endpoint within Cloud Pak for Data. Which service provides this capability?

An organization must ensure only authorized users can access sensitive datasets in Cloud Pak for Data, and they need to control access based on user roles. Which capability directly addresses this requirement?

A company is planning a Cloud Pak for Data deployment and expects high concurrency for notebooks, model training, and scheduled ETL jobs. What is the best architectural practice to handle variable workload demands while maintaining cluster stability?

A data engineer reports that a DataStage job can read from a source system but fails when writing to the target database with an authentication error. Other users can write to the same target from their tools. What is the MOST likely cause?

A team wants to ensure that datasets used for model training are discoverable, have business definitions, and can be requested/approved before use. Which combination of capabilities best supports this governed workflow in Cloud Pak for Data?

A data scientist needs to compare multiple experiments and track parameters, metrics, and artifacts for reproducibility across model iterations. Which approach within Cloud Pak for Data is most appropriate?

A regulated enterprise must ensure that only approved container images are executed for analytics workloads in Cloud Pak for Data, and that any deviation is prevented at runtime. Which solution best addresses this requirement on OpenShift?

An architect must design Cloud Pak for Data so that critical metadata and governance services remain available during node failures and planned maintenance. Which design choice most directly improves availability of these platform components?

A solution architect is designing a new IBM Cloud Pak for Data deployment on OpenShift. The customer requires that platform services and user workloads be isolated to reduce noisy-neighbor impact and simplify operational governance. Which approach best meets this requirement?

A data engineer needs to ingest data nightly from an on-premises Db2 database into Cloud Pak for Data, apply transformations, and publish a curated table for analytics. The team prefers a visual, scheduled pipeline with minimal code. Which service is the best fit?

A data steward wants to ensure business users can easily understand the meaning of data assets and find approved datasets for reporting. Which capability directly supports this goal?

A team is building an analytical solution where sensitive columns (e.g., SSN) must be protected so that only a small compliance group can view them, while analysts can still query the remaining columns. The data must remain in the same table for performance reasons. Which design is most appropriate in Cloud Pak for Data?

A solution uses Watson Machine Learning to deploy an inference service. During load testing, latency spikes occur when traffic suddenly increases, but average latency is acceptable. Which architecture change best addresses this behavior?

A customer wants to allow analysts to query data stored in an external data warehouse without copying it into Cloud Pak for Data. They also want the dataset to appear in the catalog and be governed like other assets. Which approach best meets these requirements?

A governance team wants automated detection of sensitive data (e.g., personal identifiers) across newly added datasets and wants those findings to drive policy enforcement and stewardship workflows. Which capability is most directly aligned to this requirement?

A data scientist wants to standardize and track experiments across multiple model iterations, including parameters, metrics, and artifacts, so that teams can reproduce results and promote the best model to deployment. Which Cloud Pak for Data capability best supports this lifecycle practice?

A regulated enterprise requires that platform administrators cannot access user data in projects, but still must manage the cluster and Cloud Pak for Data services. Which design best satisfies separation-of-duties?

After deploying Cloud Pak for Data, users report that catalog search is slow and sometimes returns incomplete results for recently added assets. Administrators confirm that the underlying data sources are reachable and responsive. What is the most likely component area to investigate first?

A solution architect is planning a Cloud Pak for Data deployment on OpenShift where different business units will share the same cluster. The requirement is to strongly isolate workloads and resources between business units while allowing platform services to be centrally managed. Which approach best meets this requirement?

A data engineer needs to build a repeatable ingestion pipeline that loads daily files from object storage, performs data quality checks, and writes curated data to a governed catalog. The team wants an approach that is orchestrated, supports parameterization, and can be monitored. Which Cloud Pak for Data capability best fits this use case?

A data scientist trained a model in Cloud Pak for Data and deployed it for online scoring. After deployment, business stakeholders report that predictions are becoming less accurate over time. The architect wants to detect drift and monitor model quality in production with minimal custom code. What is the recommended capability to use?

A regulated customer requires that only approved datasets can be used in analytic projects and that access to sensitive columns is controlled consistently across multiple tools. They want lineage and auditability for data usage. Which combination best satisfies these governance requirements in Cloud Pak for Data?

A team is troubleshooting intermittent failures when multiple users run heavy Spark workloads simultaneously from analytics projects. The cluster nodes show CPU and memory pressure, and other platform services become slow. The architect wants to prevent noisy-neighbor impacts while still allowing shared cluster usage. What is the best corrective action?