IBM Cloud Pak for Data V4.x Administrator Advanced Practice Exam: Hard Questions 2025

An enterprise is deploying Cloud Pak for Data on a multi-zone OpenShift cluster with strict high-availability requirements. During installation, the platform fails to provision storage for stateful services, and you discover that the StorageClass being used has volumeBindingMode set to 'Immediate'. The infrastructure team reports intermittent pod scheduling failures across zones. What is the MOST appropriate solution to resolve this architectural issue?

Your Cloud Pak for Data environment uses an external identity provider (LDAP) for authentication. Users report intermittent authentication failures, and investigation reveals that some users can authenticate while others with identical group memberships cannot. The usermgmt pods show 'context deadline exceeded' errors in logs. LDAP connectivity tests succeed, but you notice the LDAP directory contains over 50,000 users across 500+ groups with nested group structures. What is the MOST effective solution?

After deploying Watson Studio and Watson Machine Learning services, data scientists report that their Python notebook kernels crash with OOMKilled errors when processing datasets larger than 2GB. The nodes have sufficient memory (128GB RAM), and you verify that notebook pods are being scheduled with their default resource limits. What is the BEST approach to resolve this while maintaining multi-tenancy and preventing resource exhaustion?

During a Cloud Pak for Data upgrade from version 4.0 to 4.6, the zen-operator becomes stuck in a CrashLoopBackOff state after the control plane upgrade completes. Examining logs reveals 'failed to reconcile custom resource: admission webhook validation failed' errors. The admission webhook service exists but pods are not ready. You need to complete the upgrade with minimal risk. What is the MOST appropriate recovery action?

Your Cloud Pak for Data monitoring system shows that the elasticsearch pods supporting the audit logging service are experiencing severe performance degradation. The cluster has 3 data nodes, and you observe that shard allocation is uneven (node1: 45 shards, node2: 23 shards, node3: 12 shards). Index write performance has dropped by 70%, and search queries are timing out. The audit indices use a daily rotation policy, and 90 days of retention is required. What combination of actions will MOST effectively address this situation?

You are implementing a multi-tenant Cloud Pak for Data environment where each business unit requires strict data isolation and separate resource pools. Business units must not be able to view or access other units' projects, data assets, or compute resources. Some services like Watson Studio and DataStage will be shared. What architecture provides the STRONGEST isolation while maintaining operational efficiency?

An administrator discovers that several Watson Machine Learning deployment endpoints are returning 503 errors intermittently. Investigation reveals that model serving pods are being evicted with 'The node was low on resource: ephemeral-storage' messages. The models are large deep learning models (5-8GB each), and the serving pods download models from persistent storage at startup. Node ephemeral storage capacity is 100GB. What is the MOST sustainable solution?

During routine monitoring, you notice that the zen-metastoredb (PostgreSQL) database has grown to 85% of its allocated storage capacity. Query analysis shows that the activities, audit_trail, and notifications tables account for 70% of the database size. The system has been running for 18 months, and performance is degrading. What is the MOST comprehensive approach to address this issue while preserving compliance requirements?

Your organization requires that all user actions in Cloud Pak for Data be logged and retained for 7 years for regulatory compliance. The current audit logging service stores logs in elasticsearch with 90-day retention. The security team requires tamper-proof logs with cryptographic verification. What architecture BEST satisfies these requirements?

You are planning a backup strategy for a production Cloud Pak for Data environment with Watson Studio, Watson Machine Learning, and DataStage services. The RTO (Recovery Time Objective) is 4 hours and RPO (Recovery Point Objective) is 1 hour. The environment processes critical data pipelines continuously. What backup approach BEST meets these requirements?