Google Cloud Digital Leader study guide

How to use this guide

What to focus on:

• Know what drives a move to cloud, and the business risks of standing still
• Compare cloud and on-premises economics: capex to opex, and where savings really come from
• Name the common transformation blockers: culture, skills, legacy dependencies, data gravity
• Describe how Google Cloud frames the transformation journey and its stages

What to focus on:

• Explain why data loses value as time-to-insight grows, and what that means for architecture
• Match the data products to the job: BigQuery, Cloud SQL, Spanner, Bigtable, Firestore
• Tell streaming from batch, and know where Pub/Sub and Dataflow fit
• Turn data into decisions with Looker and dashboards, and name the cost of data silos

What to focus on:

• Separate AI, machine learning and generative AI in business terms
• Make the build-versus-buy call between pre-trained APIs, AutoML and custom models
• Describe Vertex AI as the managed platform, and what MLOps is for
• Cover responsible AI: bias, transparency, and why data quality caps model quality

What to focus on:

• Choose a migration path: lift and shift, move and improve, or rebuild
• Pick compute by need: Compute Engine, GKE, Cloud Run, Cloud Functions
• Explain containers, microservices and APIs as business capability, not syntax
• Describe hybrid and multicloud strategy, and the role of Anthos / GKE Enterprise

What to focus on:

• Explain the shared responsibility model and Google's shared fate stance
• Match core threats (phishing, ransomware, insider risk) to the controls that address them
• Apply IAM principles, least privilege and organisation-level policy
• Cover encryption at rest and in transit, key management, and how compliance is evidenced

What to focus on:

• Work through total cost of ownership and the levers that actually move a cloud bill
• Apply cloud financial governance: budgets, committed use discounts, resource hierarchy
• Describe reliability as a practice: SRE, SLOs, error budgets, and what they trade against
• Cover 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.
• ✓Count the selections asked for. Multiple-select questions state how many to pick.
• ✓Choose the more managed product when two could both work. 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.
• ✓Trust your first read. This exam rewards a clear first pass far more than a long reconsideration.

How to know you’re ready

One good practice score can be luck. What counts is scoring at or above the real pass mark (Not published by Google) across several full-length sets in a row, with no single domain trailing far behind the others. Kwizza tracks your per-domain readiness automatically as you practice.