Google Cloud Digital Leader study guide

How to use this guide

Know these cold:

• What actually drives a move to cloud, and the business risks of standing still
• Cloud versus on-premises economics — capex to opex, and where savings really come from
• Common transformation blockers: culture, skills, legacy dependencies, data gravity
• How Google Cloud frames the transformation journey and its stages

Know these cold:

• Why data value decays with time-to-insight, and what that implies for architecture
• The Google Cloud data portfolio by job — BigQuery, Cloud SQL, Spanner, Bigtable, Firestore
• Streaming versus batch, and where Pub/Sub and Dataflow fit
• Turning data into decisions: Looker, dashboards, and the cost of data silos

Know these cold:

• The difference between AI, machine learning and generative AI in business terms
• Pre-trained APIs versus AutoML versus custom models — the build-versus-buy decision
• Vertex AI's role as the managed platform, and what MLOps is for
• Responsible AI: bias, transparency, and why data quality bounds model quality

Know these cold:

• Migration paths — lift and shift, move and improve, rebuild — and when each is right
• Compute options by need: Compute Engine, GKE, Cloud Run, Cloud Functions
• Containers, microservices and APIs explained as business capability, not syntax
• Hybrid and multicloud strategy, and the role of Anthos / GKE Enterprise

Know these cold:

• The shared responsibility model and Google's shared fate stance
• Core threats — phishing, ransomware, insider risk — and the controls that address them
• IAM principles, least privilege, and organisation-level policy
• Encryption at rest and in transit, key management, and how compliance is evidenced

Know these cold:

• Total cost of ownership, and the levers that actually move a cloud bill
• Cloud financial governance: budgets, committed use discounts, resource hierarchy
• Reliability as a practice — SRE, SLOs, error budgets, and what they trade against
• Sustainability, carbon reporting, and the operational monitoring toolchain

Common mistakes

• ✕Studying it like an engineering exam. Deep product configuration is not tested; being unable to say what BigQuery is for, in one sentence, is.
• ✕Blurring the data products together — BigQuery, Cloud SQL, Spanner, Bigtable and Firestore each answer a different question, and the exam tests exactly that distinction.
• ✕Treating AI and ML as one topic. Pre-trained API versus AutoML versus custom model is a recurring decision point.
• ✕Skipping the operations and cost domain because it sounds like finance. It is 17% of the exam and the vocabulary is specific.
• ✕Answering with the most powerful option rather than the most appropriate one — the business framing usually rules out the heavyweight choice.

Exam-day tips

• ✓Anchor on the business outcome named in the stem. The right answer serves that outcome, not the most sophisticated technology available.
• ✓Multiple-select questions state how many to pick — count them and pick exactly that many.
• ✓When two products could both work, choose the more managed one; the exam consistently favours less operational overhead.
• ✓Watch for the actor in the scenario: what a CFO needs and what a platform team needs point at different answers to the same situation.
• ✓Do not overthink. This exam rewards a clear first read far more than a long reconsideration.

How to know you’re ready

One good practice score can be luck. The signal that holds up: consistently at or above the real pass mark (Not published by Google) across multiple full-length sets, with no single domain dragging far below the rest. Kwizza tracks your per-domain readiness automatically as you practice.