Google Cloud Certified — Cloud Digital Leader

Google Cloud Digital Leader Study Guide

Master every domain of the Cloud Digital Leader certification. Six comprehensive study guides with interactive notebooks cover cloud fundamentals, data transformation, AI/ML, infrastructure modernization, security, and operations.

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6
Study Guides
6
Exam Sections
6
Notebooks
~100%
Exam Coverage
Study Guides & Notebooks

Each section maps to a domain on the CDL exam. Work through them in order or jump to the areas you need most.

Section 1 — ~17% of exam
01 Digital Transformation with Google Cloud Cloud computing fundamentals, IaaS/PaaS/SaaS models, shared responsibility, total cost of ownership, and Google Cloud infrastructure.
Cloud Models IaaS/PaaS/SaaS TCO Shared Responsibility
Section 2 — ~16% of exam
02 Exploring Data Transformation with Google Cloud Data value chain, BigQuery, Cloud Storage, Pub/Sub, Dataflow, Dataproc, Looker, and real-time analytics pipelines.
BigQuery Cloud Storage Pub/Sub Dataflow Looker
Section 3 — ~16% of exam
03 Innovating with Google Cloud AI AI/ML fundamentals, pre-trained APIs, AutoML, BigQuery ML, Vertex AI, TensorFlow, TPUs, and responsible AI practices.
Vertex AI AutoML BigQuery ML Pre-trained APIs TPU
Section 4 — ~17% of exam
04 Modernize Infrastructure and Applications Cloud migration paths, Compute Engine, GKE, Cloud Run, App Engine, serverless, containers, APIs, Apigee, and hybrid/multicloud with Anthos.
Compute Engine GKE Cloud Run Anthos Containers
Section 5 — ~17% of exam
05 Trust and Security with Google Cloud Cybersecurity threats, shared responsibility, encryption at rest and in transit, IAM, Cloud Armor, Security Command Center, and compliance frameworks.
IAM Encryption Cloud Armor SecOps Compliance
Section 6 — ~17% of exam
06 Scaling with Google Cloud Operations Financial governance, billing management, resource hierarchy, SRE principles, DevOps practices, Cloud Monitoring, and Google's sustainability commitments.
Billing SRE DevOps Monitoring Sustainability
Key Glossary

Essential terms you must know for the CDL exam.

IaaS
Infrastructure as a Service. Rent raw compute, storage, and networking (e.g., Compute Engine). You manage OS, middleware, apps.
PaaS
Platform as a Service. Managed runtime for apps (e.g., App Engine). Google manages OS and runtime; you deploy code.
SaaS
Software as a Service. Fully managed applications (e.g., Google Workspace). Users consume functionality through a browser.
BigQuery
Google's serverless, petabyte-scale data warehouse. Supports SQL analytics, ML (BQML), streaming ingestion, and built-in BI.
Vertex AI
Unified ML platform for building, deploying, and scaling AI models. Includes AutoML, custom training, Model Garden, and prediction endpoints.
GKE
Google Kubernetes Engine. Managed Kubernetes for deploying, managing, and scaling containerized applications.
Cloud Run
Fully managed serverless platform for running containers. Scales to zero, bills per request, supports any language.
IAM
Identity and Access Management. Controls who (identity) can do what (role) on which resource. Follows least-privilege principle.
SRE
Site Reliability Engineering. Google's approach to operations: SLIs, SLOs, error budgets, blameless postmortems, and toil reduction.
TCO
Total Cost of Ownership. Full cost of a system including hardware, software, operations, migration, training, and opportunity costs.
Pub/Sub
Managed messaging service for asynchronous event-driven architectures. Decouples producers and consumers with at-least-once delivery.
Dataflow
Fully managed stream and batch data processing service based on Apache Beam. Auto-scales workers for ETL pipelines.
Cloud Armor
DDoS protection and Web Application Firewall (WAF) for Google Cloud load balancers. Supports geo-based and rate-limiting rules.
Anthos
Hybrid/multicloud platform for managing Kubernetes clusters across GCP, on-premises, and other clouds with a consistent control plane.
Cloud Storage
Object storage with four classes (Standard, Nearline, Coldline, Archive) for data of any size with 11 nines durability.
AutoML
Automated model training on Vertex AI. Provide labeled data and Google handles architecture search, tuning, and deployment.