The Cloud Digital Leader exam is business-level: it asks why an organization moves to the cloud and which Google Cloud product family fits which goal. Nothing is configured hands-on. The outline follows the exam guide launched in August 2026, which covers both the standard and the shorter renewal exam. Tick topics off as you cover them. Your ticks are saved in this browser.
This outline follows the official CDL exam guide (PDF). Check it for the exact wording.
18%
Why the cloud changes businesses, the vocabulary of cloud models and networks, and what sets Google Cloud apart. About 18% of the exam.
Why and how the cloud is revolutionizing business Defining cloud, agentic AI, infrastructure, digital transformation, open source and open standards Business benefits: scalability, cost-effectiveness, agility, speed, flexibility, stronger security, global reach, high availability, data-driven insight, strategic focus What pushes organizations to transform, the hurdles they hit, and the risk of not adopting cloud Google Cloud differentiators: leading AI, openness and interoperability, AI Hypercomputer, AI-ready data platform, security, global network Cloud architectures Networking basics and the Google Cloud network Service models 18%
Data as the fuel of transformation: types of data, the data supply chain, governance, choosing a Google Cloud data product, and analytics. About 18% of the exam.
The role of data Why data is valuable: real-time insight, trends, decisions, fuel for AI Databases vs data warehouses vs data lakes First-, second- and third-party data; structured, semi-structured and unstructured data Data supply chain: genesis, collection, processing, storage, analysis, activation Data governance and why it matters Openness and interoperability to remove silos and avoid vendor lock-in Choosing data management products Cloud Storage, Spanner, Cloud SQL, AlloyDB, Bigtable, BigQuery and Firestore matched to use cases Relational, non-relational, object storage, SQL and NoSQL terms Cloud Storage classes: Standard, Nearline, Coldline, Archive and Autoclass Ways to migrate or modernize an existing database Analytics, BI and streaming
Innovating with AI 18% AI, ML and generative AI concepts, data quality and responsible AI, and how Google Cloud's AI offerings create business value. About 18% of the exam.
AI and ML fundamentals Definitions: AI, ML, generative AI, data analytics, business intelligence How agentic AI reshapes productivity, support, sales, product innovation, operations and research Key benefits of Google Cloud AI: AI infrastructure, AI-ready data cloud, first-party models, one developer platform, prebuilt agents and applications Business problems ML solves: replacing rule-based systems, insight from large data sets, scaling decisions Why data quality matters and its dimensions: completeness, uniqueness, timeliness, validity, accuracy, consistency Business implications of explainable and responsible AI Choosing Google Cloud AI solutions
Modernizing infrastructure and applications 18% Migration vocabulary, compute options, modern application development and APIs. About 18% of the exam.
Moving to the cloud Migration terms: workload, discovery and assessment, retire, retain, rehost, replatform, refactor, reimagine Compute terms: VMs, containers, microservices, serverless, Spot VMs, Kubernetes, autoscaling, load balancing, managed services Infrastructure offerings and their value APIs
Trust and security 18% Security concepts, Google's layered security and the products behind it, and how Google earns trust. About 18% of the exam.
Fundamental security concepts Threats: DDoS, ransomware, cryptomining, malware, phishing, misconfiguration, third-party risk, physical damage, LLM attacks Cloud security vs on-premises security Control, compliance, confidentiality, integrity and availability Key terms: data loss prevention, privileged access, least privilege, zero trust, security by default, posture, cyber resilience, firewall, encryption Encryption in use, in transit and at rest Authentication, authorization and auditing; MFA, 2SV, IAM SecOps terms: security posture, threat intelligence, threat response Google as part of your security team Security across the AI stack: infrastructure, data, models, platform, agents Google Threat Intelligence: Google visibility, Mandiant, VirusTotal Security Command Center for risks and misconfigurations Google Security Operations as a unified SecOps platform Secure-by-design infrastructure: custom hardware, data centers, networking AI-assisted and AI-focused security: Gemini in Security Operations, AI Protection, Model Armor Other offerings: VPC, Cloud VPN, Interconnect, firewalls, Cloud Armor, Cloud Logging, IAM, Sensitive Data Protection, Confidential Computing, Certificate Manager, Identity-Aware Proxy Earning trust: transparency reports, third-party audits, digital sovereignty, data residency, compliance resources
Scaling with Google Cloud operations 10% Controlling cost, the resource hierarchy, observability, reliability and SRE ideas. About 10% of the exam, the smallest section.
Controlling cloud costs CapEx to OpEx shift and its effect on total cost of ownership Recommended financial governance: who owns cost, cost management tools People, process and technology in cost control Resource hierarchy: resources, projects, folders, organization node; inheritance, access, compliance, visibility Controlling consumption: quotas, budgets and alerts, billing reports, Dynamic Workload Scheduler, Spot VMs Modern operations, reliability and resilience Observability: Cloud Monitoring, Logging, Trace, Profiler, Error Reporting Terms: operational excellence, reliability, high availability Resilient design: redundancy, replication, scalable infrastructure, backups Measuring performance: latency, traffic, saturation, errors DevOps and SRE: SLIs, SLOs and SLAs