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

A large financial institution is implementing Cloud Pak for Data across multiple OpenShift clusters in different geographic regions. They require unified governance policies, centralized metadata management, and the ability to query data across all regions without data movement. Their compliance team mandates that sensitive data must remain in its source region. Which architectural pattern best addresses these requirements?

An enterprise has deployed Cloud Pak for Data with multiple services including Watson Studio, Watson Machine Learning, and DataStage. They are experiencing intermittent performance degradation affecting ML model training jobs. Monitoring shows that CPU and memory utilization are within acceptable ranges, but certain pods are being evicted. Network latency to persistent storage is normal. What is the most likely root cause and solution?

A data governance team needs to implement automated data quality monitoring and policy enforcement across 500+ data assets spanning on-premises databases, cloud object storage, and SaaS applications. They require automatic detection of PII, data quality scoring, and the ability to quarantine non-compliant data before it reaches production catalogs. Which combination of Cloud Pak for Data capabilities should be architected?

A retail organization has implemented a data fabric architecture using Cloud Pak for Data. They need to provide business analysts with self-service access to customer data while ensuring that: (1) analysts only see data for their assigned regions, (2) PII is automatically masked based on user roles, (3) all data access is logged for compliance, and (4) performance remains acceptable for interactive queries across 50TB of data in multiple source systems. What is the optimal technical approach?

An insurance company needs to establish a comprehensive data lineage solution that traces data flow from source systems through multiple transformation layers (DataStage jobs, Python notebooks in Watson Studio, and Watson Machine Learning models) to final business reports. They require impact analysis capabilities to assess the downstream effects of schema changes. What is the most effective implementation strategy?

A healthcare provider is deploying multiple AI models in production using Watson Machine Learning. They need to monitor models for drift, bias, and explainability while ensuring compliance with healthcare regulations. Model predictions influence treatment decisions. After deployment, they discover that a credit risk model is inadvertently being used alongside healthcare models. The operations team reports that Watson OpenScale monitoring shows degraded performance for several models but cannot determine root causes. What architectural issue needs to be addressed first?

A global manufacturing company needs to deploy federated learning capabilities where AI models are trained on sensitive operational data distributed across 20 factory locations worldwide. Local regulations prohibit transferring raw production data outside each country. They need centralized model aggregation, version control, and the ability to deploy the globally trained model back to all locations. Which Cloud Pak for Data architecture pattern best supports this requirement?

A financial services client is experiencing issues with their Cloud Pak for Data implementation where data scientists can successfully train models in Watson Studio but model deployment to Watson Machine Learning fails intermittently with timeout errors. The Watson Machine Learning service shows as healthy, and small test models deploy successfully. Large deep learning models consistently fail. Resource quotas are sufficient, and network connectivity tests pass. What is the most likely cause and resolution?

An enterprise client with Cloud Pak for Data needs to design a solution for a business unit that requires: real-time fraud detection on transaction streams (10,000 transactions/second), batch scoring for customer segmentation (50 million customers daily), interactive model development and experimentation, and the ability to explain individual predictions for regulatory compliance. They have limited ML operations expertise. What combination of services and architecture should be recommended?

A client is migrating from a legacy analytics platform to Cloud Pak for Data. They have 200+ DataStage jobs, 50 Cognos reports, multiple SPSS models in production, and custom Python analytics applications. They need a phased migration approach that minimizes business disruption, maintains regulatory compliance during transition, and enables knowledge transfer to their team. The migration must complete within 9 months. What migration strategy should be recommended?