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

During a Cloud Pak for Data upgrade assessment, you discover that a production environment has custom operators deployed in the same namespace as CP4D, multiple deprecated API versions in use, and persistent volumes using a storage class that will be retired. The cluster is running near capacity with limited node resources. What should be your PRIMARY concern when planning the upgrade path?

You are upgrading a multi-zone Cloud Pak for Data deployment with Watson services across three availability zones. During the upgrade, you need to maintain service availability for critical inference workloads while ensuring data consistency. The upgrade requires updating both the control plane and service instances. What is the correct upgrade sequence to minimize risk?

During a CP4D upgrade, the cpd-cli upgrade command fails with an error indicating that certain CustomResourceDefinitions (CRDs) cannot be updated due to schema conflicts. The logs show that existing custom resources have fields that violate the new CRD schema validation rules. What is the most appropriate recovery strategy?

After upgrading Cloud Pak for Data, you discover that Watson Machine Learning deployments are failing with authentication errors, while the WML service itself appears healthy. Investigation reveals that the service uses a custom certificate authority for internal communications, and the certificate rotation policy changed in the new version. Which approach correctly addresses this authentication issue?

You are performing a major version upgrade of Cloud Pak for Data that includes schema changes to the metadata database. The environment contains 500TB of data across multiple data virtualization sources, and the metadata database upgrade script is estimated to take 18 hours. Midway through the migration, the process fails due to a constraint violation on legacy metadata entries. What is the most effective recovery strategy?

During an upgrade, you need to migrate data from a legacy Db2 Warehouse instance to a new architecture that uses Db2 with different storage optimization features. The database contains partitioned tables with 2 billion rows, complex foreign key relationships, and materialized views that support real-time analytics dashboards. What migration strategy minimizes both downtime and risk of data inconsistency?

After upgrading Cloud Pak for Data, you need to migrate existing Watson Studio projects that contain notebooks with hardcoded connections to deprecated data source APIs. The projects also use custom libraries that were compiled against old Python versions. There are 200+ projects across 50 users. What is the most scalable approach to ensure continuity?

Post-upgrade, Watson OpenScale is experiencing intermittent timeout errors when evaluating fairness metrics on deployed models. Performance monitoring shows that the OpenScale pods have sufficient CPU and memory, but metric calculation jobs are queuing extensively. The underlying model serving endpoints respond normally to direct requests. What is the most likely root cause and solution?

Following a CP4D upgrade, users report that DataStage job performance has degraded by 40% compared to pre-upgrade benchmarks. The jobs involve complex transformations with multiple aggregations and lookups on large datasets. Kubernetes metrics show consistent resource usage patterns, and there are no error logs. Investigation reveals that the upgrade changed the default JVM heap settings for DataStage containers. How should you systematically optimize performance?

After upgrading a multi-tenant Cloud Pak for Data environment, one tenant's Watson Knowledge Catalog experiences significantly slower search performance compared to other tenants and pre-upgrade baselines. The catalog contains 500,000 assets with rich metadata. Elasticsearch cluster metrics show balanced shard distribution and normal resource utilization. Further investigation reveals that the upgrade modified the default index mapping for custom metadata fields. What optimization strategy addresses this issue?