Google Cloud Digital Leader
225 practice questions
Last reviewed: April 2026
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The Google Cloud Digital Leader (CDL) is the foundational, non-technical entry point into the Google Cloud certification track. It validates a working understanding of how Google Cloud helps organizations digitally transform — covering data, AI, infrastructure modernization, security, and operations — without requiring hands-on engineering experience. The exam targets business decision-makers, sales engineers, project managers, and technical leaders who need to speak credibly about Google Cloud capabilities, value propositions, and core service families. Expect conceptual scenario questions about matching cloud solutions to business outcomes, with no command-line, configuration, or coding content. It is roughly comparable in scope and difficulty to AWS Cloud Practitioner and Azure Fundamentals.
Why companies move to the cloud: business drivers, TCO, agility, the four cloud deployment models. Lightest domain at 10% but sets the framing for the whole exam.
BigQuery, Looker, Dataflow, Pub/Sub at the conceptual level. Data lifecycle, structured vs. unstructured data, data warehouse vs. data lake distinctions. Around 15% of questions.
Vertex AI, Gemini for Google Cloud, AutoML, pre-trained APIs (Vision, Speech, Translation), and the responsible-AI framework. The ML/AI lifecycle is conceptual, not hands-on.
Largest domain at 25%. Compute Engine, GKE, Cloud Run, App Engine; lift-and-shift vs. refactor vs. rebuild migration paths; hybrid and multi-cloud with Anthos.
Shared-responsibility model, IAM basics, encryption defaults, the Cloud Adoption Framework, and Google Cloud compliance offerings (ISO, SOC, FedRAMP).
Financial governance (Active Assist, billing reports), Cloud Operations suite (Logging, Monitoring, Trace), and the Customer Care support tiers. 20% — second-largest domain.
Services you'll encounter on the exam and why each one matters.
Virtual machine service spanning general-purpose, compute-optimized, memory-optimized, accelerator, and Tau Arm instance families with on-demand, committed-use, and spot pricing.
Why it's on the exam: The canonical IaaS surface — CDL questions in "Modernize Infrastructure and Applications" frame Compute Engine as the lift-and-shift baseline that PaaS and serverless options improve on.
Managed Kubernetes service with Standard and Autopilot modes, multi-cluster fleet management, and built-in autoscaling, security posture, and release channels.
Why it's on the exam: CDL frames GKE as the headline container platform for modernizing applications — a recurring "containers vs. VMs vs. serverless" comparison in Domain "Modernize Infrastructure and Applications".
Fully managed serverless container platform that scales to zero, billed per request and resource use, with native support for HTTP services and event-driven jobs.
Why it's on the exam: The reference answer when a question asks how to modernize a stateless workload without managing servers — central to "Modernize Infrastructure and Applications".
Object storage with Standard, Nearline, Coldline, and Archive classes, eleven 9s of durability, dual-region/multi-region replication, and Autoclass lifecycle automation.
Why it's on the exam: Underlies nearly every CDL data and modernization scenario; storage-class selection and lifecycle policies appear across "Exploring Data Transformation" and "Trust and Security".
Serverless multi-cloud data warehouse with separation of storage and compute, built-in ML (BigQuery ML), and federated queries across Sheets, Cloud Storage, and external lakes.
Why it's on the exam: "Exploring Data Transformation with Google Cloud" centers on BigQuery as the analytics flagship — expect questions on serverless economics, ML in SQL, and data-lake integration.
Managed relational database service for MySQL, PostgreSQL, and SQL Server with automated backups, high availability, and read replicas.
Why it's on the exam: The default answer when a CDL scenario asks how to lift-and-shift a transactional database without operating the engine yourself.
Globally distributed, strongly consistent relational database with horizontal scaling and 99.999% SLA, designed for mission-critical, high-throughput transactional workloads.
Why it's on the exam: Cited in "Modernize Infrastructure and Applications" as the answer when a business needs global consistency at scale — a differentiator vs. Cloud SQL.
Unified ML platform covering AutoML, custom training, Model Garden (Gemini and partner models), Vertex AI Agent Builder, and Vertex AI Studio for prompt iteration.
Why it's on the exam: "Innovating with Google Cloud Artificial Intelligence" is anchored on Vertex AI — questions distinguish AutoML, foundation models, and agentic patterns.
Event-driven serverless compute that runs single-purpose functions in response to HTTP, Pub/Sub, Cloud Storage, or Eventarc triggers, billed per invocation.
Why it's on the exam: Frequently contrasted with Cloud Run in "Modernize Infrastructure" questions on serverless granularity and event-driven integration.
Hybrid and multi-cloud application platform that extends GKE, service mesh, and policy management across on-premises, AWS, and Azure with a single control plane.
Why it's on the exam: The named answer in "Modernize Infrastructure and Applications" for scenarios that require consistent operations across on-prem and cloud — a CDL-level differentiator.
Global and regional load balancing for HTTP(S), TCP/SSL, and internal traffic with single anycast IP, integrated WAF, and autoscaling.
Why it's on the exam: Appears in scaling and global-availability scenarios under "Modernize Infrastructure and Applications" — usually contrasted with regional alternatives.
Globally distributed edge cache that fronts HTTP(S) Load Balancing, accelerating static and dynamic content delivery with cache invalidation and signed URLs.
Why it's on the exam: Cited whenever a CDL scenario asks how to reduce latency for global users or offload origin traffic — Domain "Modernize Infrastructure and Applications".
Managed asynchronous messaging service for event ingestion and decoupled microservices, with at-least-once delivery and global topic replication.
Why it's on the exam: "Exploring Data Transformation" includes streaming ingestion patterns — Pub/Sub is the default answer for high-throughput event pipelines feeding BigQuery or Dataflow.
Modern BI platform with a semantic modeling layer (LookML), embedded analytics, and Looker Studio for self-serve dashboards on BigQuery and other warehouses.
Why it's on the exam: CDL frames Looker as the visualization and decision-support tier of "Exploring Data Transformation" — bridging raw warehouse data to business outcomes.
Serverless Apache Beam runner for unified batch and streaming data processing, with autoscaling, exactly-once semantics, and templated pipelines.
Why it's on the exam: "Exploring Data Transformation" tests ETL/ELT patterns — Dataflow is the canonical answer for transforming Pub/Sub or Cloud Storage data into BigQuery.
Identity-as-a-service that manages users, groups, and devices across Google Cloud and Google Workspace, with SSO, MFA, and SCIM provisioning.
Why it's on the exam: Foundational for "Trust and Security with Google Cloud" — questions on workforce identity, SSO, and Workspace-to-Cloud integration all reference it.
Project- and resource-level access control: principals, roles (primitive, predefined, custom), conditions, and service accounts with workload identity federation.
Why it's on the exam: The headline "Trust and Security with Google Cloud" service — every CDL access-control scenario references IAM roles and least privilege.
Managed key service for symmetric and asymmetric encryption, key rotation, and customer-managed encryption keys (CMEK) integrated with Cloud Storage, BigQuery, and Compute Engine.
Why it's on the exam: Cited in "Trust and Security" whenever a question distinguishes Google-managed vs. customer-managed encryption keys for regulatory workloads.
Centralized security and risk-management platform that surfaces misconfigurations, vulnerabilities, and threat findings across Google Cloud assets.
Why it's on the exam: "Trust and Security with Google Cloud" expects Security Command Center as the named tool for posture management and continuous compliance visibility.
Operations suite (formerly Stackdriver) providing log aggregation, metrics, uptime checks, alerting, and dashboards across Google Cloud and hybrid workloads.
Why it's on the exam: Underpins ongoing operational scenarios across every CDL domain — the named answer for observability, incident response, and cost-and-performance visibility.
$75k–$115k–$175k USD annual
Range reflects US-based business and pre-sales roles where GCP fluency is required. The CDL alone does not justify a salary jump — it is a screening signal that complements existing domain experience. Google partner-sales roles trend higher; FAANG TPM and senior solutions consultant roles can clear $200k TC.
Source: levels.fyi 2025–2026 (Google L4 non-engineering, partner sales engineers), U.S. BLS OEWS May 2024 (13-1111 management analysts, 41-9031 sales engineers). Figures are approximate; actual compensation depends on role, region, and experience.
Google Cloud partners and resellers commonly require CDL within 60–90 days of hire for non-engineering staff, so demand for the credential tracks the GCP partner ecosystem. Inside enterprise customers, CDL turns up most often on profiles for cloud-program managers, FinOps analysts, and pre-sales solutions consultants. It does not unlock engineering interviews on its own — for those, the Associate Cloud Engineer (ACE) is the meaningful next step. As of 2026, GCP holds the #3 cloud-market position behind AWS and Azure, so absolute volume of CDL postings is lower, but the per-candidate hiring competition is correspondingly thinner.
There are no formal prerequisites. Google recommends three or more years of overall industry experience, including one or more years working with cloud technology in a business or technical role, but the exam itself is genuinely accessible to motivated beginners. The official Cloud Digital Leader Learning Path on Google Cloud Skills Boost (around 12–15 hours of video and reading) covers everything tested.
If you are coming from another cloud (AWS Cloud Practitioner, Azure Fundamentals), most of the conceptual material maps directly — you mostly need to relearn the Google service names. If you have no cloud background at all, expect to spend a few extra hours grounding yourself in basic concepts (virtualization, APIs, the shared-responsibility model) before the official path lands cleanly.
CDL is foundational and notably approachable. Plan on 25–40 hours of study over 3–5 weeks if you have no prior cloud experience, or 10–15 hours over 1–2 weeks if you already hold an AWS or Azure foundational cert. The exam is 50–60 multiple-choice / multiple-select questions in 90 minutes, delivered through Pearson VUE (testing-center or online-proctored) — Google migrated away from Kryterion / Webassessor in early 2026.
The most common stumbling block is keeping the Google service taxonomy straight: Compute Engine vs. GKE vs. Cloud Run vs. App Engine for compute; BigQuery vs. Cloud SQL vs. Spanner vs. Bigtable for data. The exam loves scenario phrasing where two services would technically work and you need to pick the most idiomatic Google answer. Google does not publish numeric scores — only pass/fail.
Current exam guide refreshed in August 2024 to add Gemini for Google Cloud and updated Vertex AI coverage. Generative-AI questions now appear across multiple domains.
Initial general availability replacing the older "Cloud Sales Credential" track. Established the six-domain structure still in use today.
CDL (Google Cloud Digital Leader) is a considered an entry-level exam testing breadth of conceptual understanding rather than hands-on depth Foundational-level exam. Most candidates need 30–80 hours of study spread over 3–6 weeks for foundational-level exams. Most candidates who score consistently above the passing threshold on practice exams pass on their first attempt.
Most candidates need 30–80 hours of study spread over 3–6 weeks for foundational-level exams. Time-to-pass varies widely by prior experience. Engineers with hands-on production experience in the underlying technology typically need less; candidates new to the platform should plan toward the upper end of that range.
CDL is a recognized credential in the GCP ecosystem and signals validated knowledge to employers, recruiters, and clients. Whether it is worth the time and fee for you depends on your role and goals — it tends to pay off most for cloud engineers, architects, and consultants who work with GCP day-to-day or want to move into roles that do.
The passing score for CDL is Not published. The exam contains 50 questions and lasts 1 hr 30 min.
The CDL exam fee is $99 USD. Fees are set by GCP and may vary by region; always confirm the current price on the official GCP certification page before booking.
Google Cloud Foundational and Associate certifications are valid for 3 years. Recertify by re-passing the current version of the exam.
Yes. You can take the exam online (proctored via the provider's secure browser, available 24/7 in most regions) or at an in-person Pearson VUE test center during business hours. Both formats use the same questions, time limit, and passing score.
CertLabPro provides 15 study modes across the practice question bank for CDL. The exam-simulation mode mirrors the real exam: 50 questions in 1 hr 30 min, with the same passing threshold of Not published. Browse mode lets you read every Q&A statically.