Integrated repository for analytics and ML: The highest level of availability and performance within a single region is ideal for compute, analytics, and machine learning workloads in a particular region. Cloud Storage is also strongly consistent, giving you confidence and accuracy in analytics workloads.
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https://cloud.google.com/storage/docs/storage-classes

Creating a machine learning (ML) model to analyze the demographics and behaviors of customers who have previously closed their accounts is an effective approach to identify at-risk customers. This model can predict which current customers are most likely to leave, allowing the organization to proactively offer discounts or improved services to retain them.
Option D: Create a ML model based on the demographics and activities of previous customers that exited is correct because it uses data-driven insights and AI to identify patterns and predict future customer behavior.
Reference:
Google Cloud AI Platform: Building Machine Learning Models
Google Cloud Customer Retention Solutions: Using AI and Machine Learning

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A relational database offers the functionality of storing transactional data, which can then be accessed electronically. Relational databases store structured data that can be organized in tables with defined relationships between them. This makes them well-suited for transactional data, such as inventory data, that needs to be accessed and updated frequently.