Compute Engine, Cloud Run, GKE, Cloud Storage, IAM, VPC, BigQuery, Pub/Sub, Monitoring.
Before starting, we run a 1-minute tech check — microphone, ambient noise, connection. If your setup isn't good enough, the test is fully refunded.
Prove in 15 minutes that you actually know GCP — Cloud Run, GKE, BigQuery, IAM and the full stack — not just what's on your LinkedIn profile.
The Plume Google Cloud badge certifies your ability to design, deploy and operate cloud architectures on GCP. The 15-minute AI-led oral exam covers the core services: Compute Engine, Cloud Run, GKE, Cloud Storage, IAM, VPC, BigQuery, Pub/Sub and Cloud Monitoring. The AI examiner asks you to justify real architecture decisions, diagnose production incidents and explain how you secure a multi-team project with workload identity and least privilege.
Unlike a multiple-choice certification, a Plume oral can't be crammed. The model pushes on your concrete decisions: why Cloud Run instead of GKE? How did you size your BigQuery slots? What would you do differently in your last Terraform deployment? A second AI model reads the full transcript and produces a score from 0 to 100, a proficiency level (Novice / Proficient / Advanced / Expert), and a detailed report of your strengths and areas for improvement.
This badge is built for cloud engineers, DevOps and SRE professionals using GCP in production who want credible proof to show a recruiter or a client. It's equally relevant for data engineers working on BigQuery and Dataflow, and for solutions architects who need to stand out in a market where everyone lists "GCP" on their CV without being able to talk about it seriously.
Here are the concrete dimensions the AI examines during the 15-minute oral.
Ability to design and compare Cloud Run vs GKE architectures: runtime choice, cold starts, autoscaling, pay-per-use vs persistent nodes costs, and stateful workload management strategies.
Mastery of the Organization > Folders > Projects hierarchy, service accounts, workload identity federation, predefined vs custom roles, and best practices to prevent privilege escalation and creep.
Ability to diagnose slow or expensive queries: date or column partitioning, clustering, materialized views, reserved vs on-demand slots, and quota management to avoid billing surprises.
Integration of Terraform, Cloud Build or GitHub Actions, Artifact Registry, and automated deployments to GKE or Cloud Run, with secrets management via Secret Manager across dev/staging/prod environments.
Hands-on use of Cloud Monitoring (metrics, alerts, SLO/SLI), Cloud Logging (log-based metrics, BigQuery exports), and Error Reporting to detect, investigate and resolve production incidents end to end.
Designing GCP network topologies: Shared VPC, Cloud NAT, Private Google Access, VPC Service Controls, peering and interconnects — with the ability to explain the security implications of each architectural choice.
Integrating Pub/Sub into event-driven architectures: topics, push vs pull subscriptions, dead-letter topics, at-least-once delivery guarantees, and orchestration with Cloud Scheduler or Eventarc.
Ability to recommend or advise against GCP, or a specific service like GKE Autopilot, depending on context: team size, budget, lock-in constraints, and concrete comparisons with AWS and Azure.
Final scoring is performed by Claude (Anthropic), which reads back the full transcript and applies this weighted criteria grid.
The candidate references real parameters, limits and behaviors of GCP services (e.g., Cloud Run cold start behavior, BigQuery slot limits, GKE cluster autoscaler mechanics). Answers go beyond the official documentation summary.
The candidate justifies architectural choices with concrete criteria (cost, scalability, operability, security) and honestly acknowledges the downsides of each decision, without hiding behind vague generalities.
Examples cited (incidents, deployments, optimizations) are specific and internally consistent enough to be believable: real numbers, services involved, timeline, and genuine resolution of an actual problem encountered.
The candidate naturally integrates IAM, Secret Manager, VPC Service Controls and audit logging into their answers without needing specific prompting on those topics.
Familiarity with recent GCP developments (GKE Autopilot, Vertex AI / Gemini, Cloud Run jobs, Duet AI) and ability to position GCP against market trends in comparison with AWS and Azure.
A Plume session takes about 20 minutes, from tech check to badge delivery.
The AI verifies your mic is working, audio is clear and your connection is stable. No Google Cloud account or screen sharing required — the entire exam is conversational.
The AI examiner invites you to introduce your most recent or most significant GCP use case — project context, services involved, scale. This phase calibrates the difficulty of the questions that follow.
The core of the exam: the AI digs into 3 to 4 themes from the 8 calibrated topics — Cloud Run or GKE architecture, IAM and security, BigQuery, Terraform CI/CD, observability, Pub/Sub. It follows up on your answers, pushes for specifics and asks you to justify your technical decisions.
The examiner asks a positioning question: when would you advise against GCP? What would you do differently in a recent project? This phase assesses your technical maturity and ability to reflect critically on your own practice.
Claude Opus analyzes the full transcript and produces your score (0-100), your proficiency level (Novice to Expert), a detailed report and a shareable badge link. Everything lands in your inbox within 10 minutes.
Your score out of 100 translates into a level a recruiter can grasp at a glance.
You've explored GCP through tutorials or isolated POCs. You can spin up a Compute Engine VM or create a Cloud Storage bucket, but you haven't managed a production project or structured IAM beyond basic predefined roles.
You work with GCP regularly in a professional context. You deploy applications on Cloud Run or GKE, configure IAM for a team, query BigQuery and use Cloud Monitoring to watch your services. You handle common incidents autonomously.
You design complex GCP architectures in production: Shared VPC, workload identity, full Terraform CI/CD pipelines, BigQuery optimization at scale, multi-region strategies. You mentor other engineers and contribute to your organization's infrastructure decisions.
You're a GCP reference in your organization or your field. You've designed mission-critical architectures (high availability, multi-cloud, regulatory compliance), you command advanced services (Vertex AI, Dataflow, Cloud Spanner, Anthos) and can benchmark GCP against AWS and Azure with precise, experience-based criteria.
No degree or years of experience required to take the badge. Here are the profiles it makes the most sense for.
You work with GCP every day and want verifiable proof of your skills beyond a multiple-choice certification. The Plume badge shows you can talk through real scenarios in an interview, not just tick boxes.
You spend your days in BigQuery, Dataflow or Pub/Sub and want to surface that hands-on expertise to recruiters who can't tell the difference between someone who's "used BigQuery" and someone who genuinely optimizes it.
You advise clients and CTOs on GCP adoption. A credible oral badge on your profile strengthens your legitimacy when recommending high-stakes architectures.
Your engagements are short and clients don't have time to vet your background. A shareable badge with a score and level lets you present objective proof from the very first conversation.
You're coming from dev or sysadmin and actively upskilling on GCP. The badge lets you signal your actual level before you have a long GCP work history to point to on your CV.
Where and how your Google Cloud (GCP) badge will help you day to day.
A recruiter receives 40 resumes all listing "GCP" as a skill. Your profile stands out with a Plume Advanced badge and a score of 78/100 — they know exactly what you can do without running a separate technical screen.
You're pitching for a GCP architecture contract. You include your badge link in the proposal. The client sees your score and level in 10 seconds, cutting the back-and-forth qualification process significantly.
You add the shareable badge to your LinkedIn certifications section. Unlike official Google Cloud certifications that test memorization, the Plume badge attests to your ability to reason out loud on real-world cases.
During a compensation review, you present your Expert GCP badge to back your raise request. The detailed report shows precisely which dimensions you excel in, making the conversation more factual and less subjective.
You sit the badge for the first time and score 54/100 at Proficient level. The report flags IAM and VPC security as your weak spots. You train specifically on those areas and retake the exam three months later aiming for Advanced.
Your manager wants to confirm you're ready to lead the GCP migration for a strategic client. The badge provides an objective baseline for a targeted upskilling plan before the engagement kicks off.
A few minutes to check you have everything you need.
At the end of your session you don't just get a score — here's everything that awaits you.
You get a precise score and a proficiency level (Novice, Proficient, Advanced, Expert) calibrated against real market expectations for a GCP production profile.
Structured feedback on your strengths and weaknesses across each evaluated dimension: architecture, IAM, BigQuery, CI/CD, observability. You know exactly what to work on next.
Your session recording is stored privately and securely. You can replay it to analyze your own performance and identify how to sharpen your answers.
A public URL with your score, level and exam date — ready to share on LinkedIn, in a CV or in a client proposal with a single click.
Discover related skills you can validate with Plume.
A 15-min oral exam with an AI, a shareable badge for your recruiters.
Choose this badge · €19.99
