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

An enterprise is designing a multi-tenant Cloud Pak for Data deployment where different business units require strict data isolation, custom resource quotas, and separate authentication policies. The infrastructure team wants to minimize operational overhead while ensuring complete tenant separation. Which architectural approach best addresses these requirements?

During a Cloud Pak for Data installation on a restricted network OpenShift cluster, the installation fails with image pull errors for specific Watson services despite the air-gapped registry being properly configured. The core platform installed successfully, but Watson Discovery and Watson Assistant services cannot pull images. Logs show 'manifest unknown' errors for these specific services. What is the most likely root cause and resolution?

A financial services organization needs to implement comprehensive data lineage tracking across multiple data sources including DB2, Oracle databases, and S3 object storage, while ensuring that personally identifiable information (PII) is automatically classified and masked before data scientists can access it in their analytics workflows. The solution must maintain audit trails for compliance. Which combination of Cloud Pak for Data components and configuration provides the most complete solution?

An architect is designing a high-availability Cloud Pak for Data deployment where Watson Machine Learning models must achieve 99.9% uptime and handle failover within 30 seconds. The deployment spans three availability zones in a cloud environment. Model serving requests must be distributed efficiently, and in-flight requests should not be lost during pod failures. Which architectural pattern best meets these requirements?

A Cloud Pak for Data environment experiences intermittent performance degradation during peak hours. Analysis shows that Data Virtualization queries are timing out, Watson Studio notebook kernels are being terminated, and the Zen operator pod is repeatedly restarting. CPU and memory metrics show the cluster is at 70% utilization. What is the most likely root cause and appropriate remediation strategy?

An organization needs to implement a data governance framework where data stewards can define business glossary terms that automatically apply data quality rules and access policies to physical assets. When a dataset is tagged with 'Customer PII', it should automatically trigger quality checks for completeness, apply column-level masking rules, and restrict access to specific user groups. The solution must work across virtualized and physical data sources. How should this be architected in Cloud Pak for Data?

A machine learning team has deployed multiple models in Watson Machine Learning that exhibit bias in predictions affecting protected demographic groups. The compliance team requires continuous monitoring, automatic detection of bias drift, and the ability to explain individual predictions to end users. The models include both structured data models (credit scoring) and unstructured data models (resume screening). What is the most effective implementation approach?

A global organization is implementing Cloud Pak for Data with Data Virtualization to federate queries across on-premises Oracle databases in Europe, DB2 databases in North America, and cloud-based data lakes in Asia. Users report that queries involving all three sources take over 5 minutes to complete, while single-source queries perform acceptably. Network latency between regions averages 150-200ms. Which optimization strategy will provide the most significant performance improvement?

An architect must design a Cloud Pak for Data deployment that complies with financial regulations requiring encryption at rest, encryption in transit, key rotation every 90 days, and cryptographic separation of data belonging to different subsidiaries. The solution must support FIPS 140-2 compliance and provide auditable key management. Which encryption architecture best meets these requirements?

A data science team using Watson Studio and Watson Machine Learning reports that their AutoAI experiments are failing with 'insufficient resources' errors, while manually created notebooks run successfully. The failures occur during the model training phase of AutoAI pipelines that involve datasets larger than 5GB. Cluster monitoring shows memory utilization at 60% and CPU at 45%. What is the most likely cause and resolution?