IBM Certified Solution Advisor - Cloud Pak for Data v4.x Intermediate Practice Exam: Medium Difficulty 2025

A financial services company is deploying Cloud Pak for Data and needs to ensure that their architecture can handle workload isolation for different business units while maintaining centralized governance. Which architectural approach best addresses this requirement?

An organization wants to implement a data fabric architecture using Cloud Pak for Data. They have data sources across multiple clouds and on-premises systems. Which combination of components is essential for establishing the data fabric foundation?

A data science team is experiencing performance issues when running multiple machine learning experiments simultaneously in Watson Studio. The platform administrator needs to optimize resource allocation. What is the most effective approach?

A retail company wants to implement data governance policies that automatically classify personally identifiable information (PII) across their data assets in Cloud Pak for Data. What is the correct sequence of steps to achieve this?

A manufacturing company is building a predictive maintenance solution using Watson Machine Learning. They need to deploy a model that was trained in Watson Studio to a production environment where it will score data in real-time. Which deployment approach should they use?

An enterprise client has concerns about AI model bias in their loan approval system built on Cloud Pak for Data. As a solution advisor, which combination of capabilities would you recommend to address model fairness and transparency?

A client is designing a Cloud Pak for Data solution that requires integration with their existing enterprise data lake on AWS S3 and a legacy Oracle database. They want to avoid data duplication while enabling data scientists to access both sources seamlessly. What architectural pattern should you recommend?

A healthcare organization needs to enforce data masking policies on patient records in their analytics environment. Some users need to see full data while others should see masked data based on their roles. How should this be implemented in Cloud Pak for Data?

A client wants to operationalize their data science workflows by automating the process from data preparation through model deployment. They need to ensure models are automatically retrained when data drift is detected. Which Cloud Pak for Data capabilities should be combined to achieve this?

During a client engagement, you discover that their data engineering team uses Apache Spark for processing, their analysts use SQL for reporting, and their data scientists prefer Python notebooks. They want to consolidate these workflows on Cloud Pak for Data. What should you emphasize in your solution design?