IBM Cloud Pak for Data v4.x Administrator Advanced Practice Exam: Hard Questions 2025

An enterprise is upgrading Cloud Pak for Data from v4.0 to v4.6 in an air-gapped OpenShift environment with multiple data services including Watson Studio, DataStage, and Db2 Warehouse. During the upgrade, the cpd-cli upgrade command fails with an error indicating that certain custom resource definitions (CRDs) are in a conflicting state. What is the MOST appropriate sequence of actions to resolve this issue while minimizing service downtime?

A Cloud Pak for Data administrator notices that the zen-watcher pods are consuming excessive CPU resources (>80%) and causing performance degradation across the platform. Investigation reveals that there are over 5,000 user accounts synchronized from LDAP, and the watcher is continuously reconciling user permissions. Which combination of actions would BEST optimize this situation without compromising security?

During a multi-zone OpenShift cluster deployment of Cloud Pak for Data, the Db2 Warehouse service fails to start properly. Logs indicate that persistent volume claims (PVCs) are in a Pending state due to pod affinity rules conflicting with storage topology constraints. The storage class uses WaitForFirstConsumer binding mode with zone-specific provisioning. What is the MOST effective solution to resolve this architectural conflict?

An organization's Cloud Pak for Data environment experiences intermittent authentication failures affecting approximately 30% of users during peak hours. The platform uses an external LDAP directory with TLS encryption. Packet captures show successful LDAP binds, but the platform logs indicate 'authentication cache inconsistency' warnings. The redis-sentinel pods show frequent leader elections. What is the MOST likely root cause and appropriate remediation?

A data governance team needs to implement column-level access control across multiple data sources (Db2, external Hive, and object storage) accessed through Watson Query in Cloud Pak for Data. Some users should see masked values for sensitive columns while others see actual values based on their role. The solution must be centrally managed and enforce policies consistently. What architectural approach BEST meets these requirements?

After a routine backup, a Cloud Pak for Data administrator needs to test disaster recovery procedures by restoring to a new OpenShift cluster in a different region. The backup includes the control plane, all service instances, and user data. During restoration, several DataStage jobs fail with errors indicating missing connection credentials and runtime configurations. The backup was taken using the cpd-cli backup command with default options. What is the PRIMARY reason for this failure?

A Cloud Pak for Data installation on OpenShift is experiencing cascading pod failures across multiple namespaces. The usermgmt pods are in CrashLoopBackOff, zen-core pods show OOMKilled status, and the operator logs indicate 'unable to sync resources: etcd client timeout'. The OpenShift cluster has 6 control plane nodes and 20 worker nodes. Checking etcd metrics reveals average response times of 500ms for read operations. What is the MOST critical underlying issue that needs immediate attention?

An organization wants to implement a multi-tenant Cloud Pak for Data deployment where different business units have isolated environments but share common data catalogs for cross-organization collaboration. Each business unit requires separate Watson Studio instances, compute quotas, and user management, but must be able to discover and request access to datasets published by other units. What architectural pattern BEST achieves these requirements?

A DataStage job processes 500GB of data daily by reading from multiple database sources, performing complex transformations, and writing to a data warehouse. Performance has degraded by 60% after migrating to Cloud Pak for Data. Analysis shows that the DataStage pods have sufficient CPU and memory, but disk I/O wait times are high. The persistent volumes use the default OpenShift storage class with RWO access mode. What combination of optimizations would provide the MOST significant performance improvement?

A Cloud Pak for Data environment with Watson OpenScale monitoring multiple deployed models suddenly shows degraded model performance metrics and fairness violations that weren't present previously. The models themselves haven't changed, but a recent platform update modified the way inference requests are routed through the service mesh. OpenScale payload logging shows that feature values in the requests appear truncated. What is the MOST likely cause and appropriate solution?