Cloud Digital Leader Advanced Practice Exam: Hard Questions 2025

A global retailer is modernizing its customer analytics platform. Today, it runs nightly batch ETL on an on-prem data warehouse. Leadership wants to enable near-real-time personalization (minutes), but the data governance team requires a single source of truth, strong data lineage, and consistent policy enforcement across multiple consuming teams. The solution must minimize operational overhead and avoid building custom streaming infrastructure. Which approach best meets these requirements on Google Cloud?

A healthcare provider is migrating to Google Cloud and must ensure that only approved regions are used and that all data at rest is encrypted with customer-managed keys (CMEK). Multiple teams create projects frequently, and the security team has limited staffing. They want guardrails that prevent noncompliant resources from being created, not just alerts after the fact. Which design best enforces these requirements at scale?

A media company runs a monolithic web application on-prem. During traffic spikes, the app suffers from resource contention and slow releases. The company wants to modernize with minimal rewrite while improving scalability and release velocity. They also want a path to break out features over time. Which approach is the best first step on Google Cloud?

A financial services firm wants to adopt generative AI to summarize customer interactions and suggest next-best actions. Data includes sensitive customer information, and regulators require clear controls over data access and strong auditability. The firm also wants to reduce the risk of inadvertently exposing sensitive data to a public model endpoint. Which solution is most appropriate?

A SaaS company operates a multi-tenant analytics platform on Google Cloud. A new requirement states that customers must be able to prove that their data cannot be accessed by other tenants, even in the event of misconfigured IAM in a single project. The company wants a defense-in-depth control to reduce the blast radius of credential misuse and prevent data exfiltration from protected services. Which control best addresses this requirement?

A logistics company needs to ingest IoT telemetry from 500,000 devices. Messages arrive continuously and must be processed in order per device, with late-arriving data up to 10 minutes. The company needs exactly-once-like outcomes in the analytics store (no duplicates in final aggregates), and a single pipeline that supports both real-time dashboards and replay for backfills. Which architecture is the best fit?

An enterprise is moving to a product-centric operating model. Multiple business units will share a common platform team that provides reusable capabilities (networking, identity, logging, CI/CD). The company expects rapid growth in projects and teams and wants consistent policy enforcement, billing separation, and delegated administration without central bottlenecks. Which Google Cloud structure best supports this operating model?

A company runs a latency-sensitive API used by mobile apps worldwide. The API is currently deployed in a single region on Compute Engine and uses a managed database. They need global traffic distribution, protection against regional failures, and the ability to roll out changes with minimal downtime. The team wants a managed approach that reduces operational complexity. Which solution best meets these needs?

A platform team enabled centralized logging but is now facing a surge in log volume and difficulty detecting actionable incidents. Engineers complain about alert fatigue, while security requires retention for investigations. The company wants to reduce noise, keep critical signals, and maintain compliance-ready access to historical logs. Which approach best balances these goals?

A retail company wants to build a governed self-service BI experience. Analysts need to explore data freely, but leadership requires consistent KPI definitions across departments and row-level access controls by region. The company also wants to minimize duplicated semantic logic across dashboards and avoid inconsistent calculations. Which approach best meets these requirements?