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

A data engineering team needs to ingest daily CSV files from an SFTP server into object storage and then load curated data into a data warehouse. They want a graphical pipeline that supports scheduling, parameterization, and basic transformations. Which Cloud Pak for Data capability best fits this requirement?

An organization wants to make multiple data sources (Db2, Oracle, and flat files) available for analytics without copying the data. They also want to control who can see which columns. Which approach best meets these requirements in Cloud Pak for Data?

A data steward needs to ensure that datasets in Cloud Pak for Data can be discovered by business users with consistent definitions for key terms like "Customer" and "Net Revenue." Which feature should be used?

A platform engineer must confirm that Cloud Pak for Data services can be deployed and scaled reliably on the underlying container platform. Which foundational component is Cloud Pak for Data installed on and managed through?

A team virtualizes several remote tables and reports that queries are slow due to repeated scans over a high-latency network. They can tolerate data being up to an hour stale for this use case. What is the best next step?

A data engineer needs to ensure that sensitive columns (for example, national ID and birth date) are masked for most users but still visible to a small compliance group across cataloged assets. Which governance capability should be implemented?

A Cloud Pak for Data deployment uses external object storage for project assets and persisted data. During peak hours, DataStage jobs intermittently fail with storage timeout errors. Which action is the most appropriate first step to troubleshoot?

A company wants to standardize how datasets are published for self-service analytics. They need datasets to be discoverable, carry business metadata, and be approved before becoming visible to a broad audience. Which approach best meets these requirements?

After enabling governance enforcement, some users report that queries against a virtualized view now fail with authorization errors, even though they have access to the catalog asset. The data steward confirms a masking rule exists for a column in the underlying table. What is the most likely cause?

A Cloud Pak for Data environment hosts multiple services. During a new workload rollout, several pods remain in Pending state. The OpenShift events show "Insufficient cpu" and "Insufficient memory". What is the best resolution aligned with platform best practices?

A project team wants to enforce that only approved, governed data assets are discoverable by most users, while stewards can still find and work with all assets. Which Cloud Pak for Data capability best supports this requirement?

A data engineer is asked to provide a single logical view that joins customer data from Db2 and order data from a remote Oracle database without moving the data. Users need to query it with SQL from within Cloud Pak for Data. Which approach best meets this requirement?

An administrator needs to confirm that a Cloud Pak for Data service pod is unable to start because required persistent storage cannot be mounted. Which Kubernetes-level evidence is most directly useful to validate this?

A data engineering team must standardize business definitions (e.g., 'Active Customer') and ensure those definitions are consistently applied and searchable across governed assets. Which feature best addresses this requirement?

A virtualized query that joins two remote data sources is slow due to repeated full scans over a high-latency network. The data changes only nightly. What is the best way to improve performance while keeping a virtualization-based access pattern?

A catalog contains sensitive datasets. The organization requires that only a specific group can see column-level profiles and sample data previews, while other approved users can still discover the asset metadata. What is the most appropriate control to implement?

A DataStage flow writes to a target table in Db2. Intermittently, the job fails with deadlock/timeout-related errors during peak hours. Which mitigation is most appropriate from a data engineering perspective?

A team needs to deploy Cloud Pak for Data in an environment with strict isolation requirements. They want to minimize cross-namespace access while still allowing platform services to function. Which Kubernetes/OpenShift design best supports this?

A governed data product must ensure that when a user queries a virtualized customer table, rows are filtered based on the user's region attribute from the enterprise identity provider. The policy should be centrally managed and consistently enforced. Which approach best meets this requirement?

After enabling Data Virtualization, a federated query that should push filters to the source instead retrieves far more data than expected, causing network saturation. Which action most directly helps the optimizer choose better pushdown plans?

A data engineer is asked to expose curated data to multiple analytics teams without copying data into new storage. The source tables remain in an external database, but users must see business-friendly names and only permitted columns/rows. Which approach best meets these requirements in Cloud Pak for Data?

A team wants to ensure that when data assets are added to a catalog, users can quickly discover and understand them using consistent business terminology and classifications. Which capability in Cloud Pak for Data is most appropriate to implement this?

A Cloud Pak for Data cluster shows intermittent failures when starting a service instance. The pods repeatedly restart, and logs indicate the container is being killed due to memory pressure. Which action is the BEST next step for troubleshooting and remediation?

A governed catalog contains sensitive customer data. The organization requires that access decisions be consistent and centrally managed across multiple services (for example, virtualized data access and curated data sets) based on user attributes and policies. Which approach in Cloud Pak for Data best supports this requirement?

A solution architecture requires that Cloud Pak for Data services run on OpenShift in one namespace, but data scientists must consume data from an enterprise LDAP/AD for authentication and use a corporate ingress with TLS termination. Which design choice is MOST aligned with Cloud Pak for Data architecture and best practices?