Google Cloud Professional Machine Learning Engineer
Complete guide to passing the Google Cloud Professional Machine Learning Engineer exam on your first attempt.
$200
~40%
2 years
Global
$160k-$220k
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Complete Overview
The Google Cloud Professional Machine Learning Engineer certification is a two-hour, 200 USD exam for engineers who build, evaluate, productionise and optimise AI solutions on Google Cloud. Google sets the format at 50 to 60 multiple choice and multiple select questions, offers the exam in English and Japanese, and delivers it either online-proctored from a remote location or onsite at a Pearson test centre. There are no prerequisites, and Google's recommended profile is three or more years of industry experience including one or more years designing and managing solutions on Google Cloud.
The exam guide has been rewritten to reflect the transition from Vertex AI to the Gemini Enterprise Agent Platform, alongside updates to the Google Cloud data and analytics stack. Product names in the current guide read Agent Platform AutoML, Agent Platform Workbench, Agent Platform Pipelines, Agent Platform Feature Store, Agent Platform Model Registry and Model Monitoring on Gemini Enterprise Agent Platform. Study material written before that rebrand still describes the same services under Vertex AI names, and the exam prioritises Google Cloud native solutions.
The guide splits the exam six ways. Scaling prototypes into ML models is the largest at roughly 21 percent, followed by serving and scaling models at roughly 20 percent, automating and orchestrating ML pipelines at roughly 18 percent, collaborating within and across teams to manage data and models at roughly 16 percent, and architecting low-code AI solutions and monitoring AI solutions at roughly 13 percent each. Google publishes these as approximations preceded by a tilde rather than as exact figures.
Coding is not assessed directly. Google's exam guide states that with a minimum proficiency in Python and SQL you should be able to interpret any question containing a code snippet, which shifts the burden onto product selection, architecture judgement and operational reasoning rather than syntax recall.
Google does not publish a passing score and reports results as pass or fail only, with no numeric score. The company's stated reason is that the exam is designed to determine whether a candidate meets a minimum passing standard rather than to rank ability, so a number would be easy to misread. You see a provisional result on screen the moment you submit, and Google confirms the official result in 7 to 10 days after reviewing the exam record for compliance with the terms and conditions.
Professional certifications are valid for two years. The renewal window opens 60 days before the expiry date, and Google allows renewal up to 30 days after the certification's inactive date.
Why Get Google Cloud Professional Machine Learning Engineer Certified?
Google has rewritten the exam guide for the move from Vertex AI to Gemini Enterprise Agent Platform, so preparing for it walks you through the current product surface rather than retired naming.
Generative AI is named in four of the six sections: fine-tuning Gemini models using BigQuery and tuning Imagen and Veo in Section 1, Model Garden prototyping and LLM-as-a-judge evaluation in Section 2, fine-tuning foundational models in Section 3, and Model Armor with safety filters in Section 6.
Two hours for 50 to 60 questions gives roughly two minutes per item, and Google states the exam does not directly assess coding, so the time goes into scenario reading rather than mental parsing of code.
Section 3 and Section 4 together are about 41 percent of the exam and cover training and serving at scale, including data and model parallelism across GPUs and TPUs. That is the hardest content to learn on the job at most organisations.
At 200 USD the exam is half the price of an AWS professional or specialty exam, and Google charges the same fee whether you sit online or at a test centre.
Failing is survivable but expensive in time: 14 days before a second attempt, 60 days before a third, 365 days before a fourth, with four attempts allowed in any two-year period. That structure rewards preparing properly for the first sitting.
Google gives failed candidates a section-level score report under Exam History in the Candidate Portal, breaking performance down against the approximate section percentages, so a first failure produces a targeted plan.
Exam Format & Structure
Duration
Two hours
Questions
50 to 60 questions, per Google's exam page
Passing Score
Not published by Google. Google reports results as pass or fail only and states that numerical scores are not provided because the exam is designed to determine whether a candidate meets a minimum passing standard rather than to rank ability on a scale.
Question Types
- Multiple choice with one correct answer
- Multiple select with more than one correct answer
- Scenario questions that may contain Python or SQL snippets to interpret rather than write
Delivery Method
Delivered through Pearson: onsite at a Pearson test centre, or online-proctored from a remote location using Pearson's OnVUE service. Booking runs through a CM Connect account. The fee is the same for both methods, and you can switch between them by cancelling at least 24 hours ahead and re-registering.
Exam Domains & Topics
The largest section, covering how a notebook experiment becomes a trained model that fits cost, complexity, latency and scalability constraints. Google asks you to choose the model type, the product and the deployment strategy, then organise data and run the training. Hardware selection sits here too, including distributed training strategies across GPUs and TPUs.
Key Topics to Master:
- Choosing between model types such as ARIMA, DNN and LLM for a stated task
- Choosing between Agent Platform AutoML, BigQuery ML and Agent Platform Pipelines
- Modeling techniques where interpretability is a requirement
- Organising tabular, text, speech, image and video training data on Cloud Storage and BigQuery
- Training with Agent Platform custom training, Kubeflow on GKE, Agent Platform AutoML and Tabular Workflows
- Troubleshooting ML model training failures
- Hyperparameter tuning
- Fine-tuning foundational models from Agent Platform and Model Garden, and judging when tuning is warranted
- CPU, GPU and TPU selection, plus data parallelism and model parallelism for distributed training
Deployment and the throughput problem that follows it. Two subsections split the work: serving models across batch and online inference with the right packaging and versioning, then scaling online serving with features, endpoints, hardware and backend capacity. Rollout strategy questions turn on which technique lets you compare two versions safely.
Key Topics to Master:
- Batch and online inference on Agent Platform, Model Garden, Cloud Run and GKE
- Packaging and serving PyTorch and XGBoost models with prebuilt and custom containers
- Organising and versioning models in Gemini Enterprise Agent Platform Model Registry
- A/B testing and canary deployments to compare model versions
- Inference preprocessing and postprocessing solutions
- Managing and serving features from Agent Platform Feature Store
- Deploying to public and private endpoints
- Choosing CPU, GPU, TPU or edge hardware for serving
- Scaling the serving backend on throughput with Agent Platform Inference and containerized serving
End-to-end pipelines and automated retraining. Google separates pipeline construction from retraining policy, and the retraining subsection explicitly names CI/CD/CT, continuous training, as a distinct concern from continuous delivery. Consistency of preprocessing between training and serving appears here rather than in the data section, which is where training-serving skew is prevented.
Key Topics to Master:
- Validating data and models inside a pipeline
- Orchestrating with Agent Platform Pipelines, Managed Service for Apache Airflow and Ray on Gemini Enterprise Agent Platform
- Choosing between managed and unmanaged orchestration services
- Building from templates versus custom pipeline solutions
- Ensuring consistent data preprocessing between training and serving
- Determining an appropriate retraining policy
- Deploying models in CI/CD/CT pipelines with Cloud Build
Data exploration, notebook prototyping and experiment tracking, framed as team practice rather than solo work. Tool selection is scored on scale and complexity: BigQuery SQL, Dataflow, Apache Spark or in-memory Python each win under different conditions. Evaluation of generative solutions, including LLM-as-a-judge, is named here alongside classic model metrics.
Key Topics to Master:
- Organising and exploring tabular, text and image data for experimenting, training and serving
- Choosing preprocessing tools by scale: BigQuery SQL, Dataflow, Apache Spark, in-memory Python
- Creating and consolidating features in Gemini Enterprise Agent Platform Feature Store
- Handling personally identifiable information and data privacy
- Collaboration and security practices for Agent Platform Workbench and Colab Enterprise notebooks
- Developing models with PyTorch, sklearn and JAX in notebook environments
- Prototyping with foundational and open-source models from Model Garden
- Choosing between Experiments on Agent Platform, Agent Platform Pipelines and Kubeflow Pipelines
- Evaluating predictive and generative solutions, including LLM-as-a-judge, and tracking artifacts, versions and lineage with ML Metadata
The path that avoids custom training entirely: BigQuery ML and AutoML for structured problems, and pretrained APIs or foundation models for everything else. Google asks you to fine-tune Gemini models using BigQuery here, which places generative AI inside the low-code section rather than treating it as advanced work.
Key Topics to Master:
- Building classification, regression, forecasting and clustering models in BigQuery ML
- Training models with Agent Platform AutoML
- Feature engineering and selection using BigQuery ML
- Generating predictions from BigQuery ML
- Fine-tuning Gemini models using BigQuery
- Selecting a model for a task from Gemini Enterprise Agent Platform Model Garden
- Building with industry-specific APIs including Document AI, Vision and Translate
- Tuning Gemini, Imagen, Veo and models as a service for specific use cases
- Optimising Gemini-based applications for cost, latency and availability
Risk identification and production monitoring in two subsections. The risk half covers securing AI systems against data exfiltration and malicious prompting, responsible AI practice and explainability. The monitoring half covers continuous evaluation, the four named drift and skew failure modes, and evaluation of generative solutions in production.
Key Topics to Master:
- Protecting against data exfiltration, malicious prompting and sharing sensitive data with LLMs
- Choosing security tooling: regex filters, safety filters and Model Armor
- Aligning with responsible AI practices, including monitoring for bias
- Model explainability on Agent Platform
- Configuring Model Monitoring on Gemini Enterprise Agent Platform for continuous evaluation metrics
- Detecting training-serving skew
- Detecting data drift and concept drift
- Detecting feature attribution drift
- Monitoring, testing and evaluating generative AI solutions
Recommended Study Plan
- 1Download the current Professional Machine Learning Engineer exam guide and confirm it uses Gemini Enterprise Agent Platform naming rather than Vertex AI
- 2Build a checklist from all six sections and mark every named product you have never opened
- 3Work through Google's official sample questions to calibrate question style before you study anything
- 4Create your CM Connect account and confirm your legal first and last name matches your government-issued photo ID in Romanized characters
- 5Set up a Google Cloud project on the Free Tier and enable the Agent Platform, BigQuery and Cloud Storage APIs
- 1Train a classification model and a forecasting model in BigQuery ML using nothing but SQL
- 2Do the feature engineering for both inside BigQuery ML rather than in Python, since the guide names that explicitly
- 3Train the same problem with Agent Platform AutoML and compare cost and time against the BigQuery ML version
- 4Fine-tune a Gemini model using BigQuery and record what the workflow requires
- 5Call the Document AI, Vision and Translate APIs and note which business problems each solves without training
- 1Preprocess the same dataset four ways: BigQuery SQL, Dataflow, Apache Spark and in-memory pandas, and record the crossover points
- 2Create features in Agent Platform Feature Store and serve them to both a training job and an online request
- 3Set up an Agent Platform Workbench instance and a Colab Enterprise notebook and compare their security and sharing models
- 4Handle a dataset containing personally identifiable information and apply masking before it reaches a model
- 5Run and compare three experiments with Experiments on Agent Platform and inspect the lineage in ML Metadata
- 1Evaluate a classifier against precision, recall, F1 and AUC and justify which metric fits an imbalanced business case
- 2Set up an LLM-as-a-judge evaluation for a generative task and read the resulting scores critically
- 3Prototype with three different models from Model Garden and compare output quality and cost on the same prompt
- 4Track model artifacts, versions and lineage across all of the above and confirm you can reconstruct any run
- 5Earn a machine learning skill badge on Google Cloud Skills Boost to lock in the hands-on work
- 1Move a notebook model into Agent Platform custom training with a container and confirm it reproduces the same result
- 2Run the same training on Kubeflow on GKE and note the operational differences
- 3Ingest structured and unstructured data from Cloud Storage and BigQuery into a training pipeline
- 4Break a training job deliberately, out of memory and bad input schema, and practise reading the error
- 5Run hyperparameter tuning and compare the trial results against a single manual configuration
- 1Benchmark the same training run on CPU, GPU and TPU and record cost per epoch for each
- 2Implement data parallelism across multiple accelerators and measure the scaling efficiency
- 3Read Google Cloud documentation on model parallelism and identify when a model is too large for data parallelism alone
- 4Fine-tune a foundational model from Model Garden and write down the criteria that made tuning the right call
- 5Practise questions that trade off training time, cost and accuracy against a stated business deadline
- 1Deploy one model for batch inference and again for online inference and compare latency and cost
- 2Package a PyTorch model in a custom container and an XGBoost model in a prebuilt one
- 3Register three versions in Agent Platform Model Registry and run a canary deployment between two of them
- 4Deploy to a private endpoint inside a VPC and confirm the traffic path
- 5Load test an endpoint and scale the serving backend on throughput, then add preprocessing and postprocessing logic
- 1Build an end-to-end pipeline with Agent Platform Pipelines including data and model validation steps
- 2Rebuild the same flow on Managed Service for Apache Airflow and compare operational overhead
- 3Run a distributed workload with Ray on Gemini Enterprise Agent Platform
- 4Wire Cloud Build into a CI/CD/CT pipeline that retrains and redeploys automatically
- 5Write a retraining policy for a stated drift scenario and defend the trigger you chose
- 1Configure Model Monitoring on Gemini Enterprise Agent Platform with continuous evaluation metrics
- 2Induce training-serving skew deliberately by changing preprocessing on the serving side, and confirm the alert fires
- 3Distinguish data drift from concept drift on the same production model and describe how the remediation differs
- 4Configure feature attribution drift monitoring and interpret the output
- 5Apply Model Armor and safety filters to a generative endpoint and attempt a prompt injection against it
- 1Complete the Machine Learning Engineer learning path on Google Cloud Skills Boost and close any remaining gaps
- 2Take a full-length timed practice exam under two-hour conditions with no notes and no second monitor
- 3Score by section against the approximate weights and spend the remaining days only on the two weakest
- 4Register through CM Connect, choosing an onsite Pearson test centre or an online OnVUE session, and pay the 200 USD fee
- 5If you chose online delivery, run the OnVUE system test and download the application at least 24 hours before the appointment
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Best Study Resources
Professional Machine Learning Engineer exam guide
Official blueprint PDFGoogle's own guide with all six sections, approximate weights and named products. This is the document that tells you the exam now uses Gemini Enterprise Agent Platform naming rather than Vertex AI.
Free
Professional ML Engineer certification page
Official exam pageGoogle's page confirming the two-hour length, the 200 USD registration fee, English and Japanese availability, the 50 to 60 question format and the recommended three or more years of industry experience.
Free
Official sample questions
Practice questionsGoogle's own sample set, linked from step 3 of the exam overview. Written in the exact style of the real exam, which makes it more useful for calibration than any third-party bank.
Free
Machine Learning Engineer learning path on Google Cloud Skills Boost
Guided courses and hands-on labsGoogle's own preparation path, combining video courses with hands-on labs in real projects. Skill badges from this platform give you a verifiable record of the practical work.
Free tier plus paid subscription
Google Cloud documentation
Product documentationGoogle names this as step 4 of exam preparation. The Agent Platform, BigQuery ML and Model Garden documentation is where the current product naming and the exact service limits live.
Free
Official Google Cloud Certified Professional Machine Learning Engineer Study Guide
BookThe study guide Google links from its own exam page, using real-world scenarios across the platform, TensorFlow, Kubeflow and AutoML, including when to choose a pretrained model over a custom one.
Paid
Google Cloud Certification FAQs
Policy referenceThe authority on two-year validity, the 60-day renewal window for professional certifications, the 7 to 10 day official result confirmation and why Google reports pass or fail without a score.
Free
Important Program Policies
Policy referenceThe page setting the four-attempt limit over two years, the 14, 60 and 365 day waiting periods, the no-show forfeiture rule and the conditions under which Google invalidates an online proctored result.
Free
Online Proctored Exams help article
Policy referenceThe specific rules for an OnVUE sitting: no breaks, no scratch paper, no headphones, no external monitors, no reading aloud, and a launch window from 10 minutes before to 20 minutes after your slot.
Free
Google Cloud Free Tier
Cloud account creditGoogle recommends this in step 1 of its own exam overview. Enough to run BigQuery ML training, small AutoML jobs and endpoint experiments without committing budget up front.
Free within monthly limits
Common Mistakes to Avoid
Studying from material that still calls everything Vertex AI. The current exam guide uses Gemini Enterprise Agent Platform naming throughout: Agent Platform AutoML, Agent Platform Pipelines, Agent Platform Feature Store, Agent Platform Model Registry.
Read the current exam guide first and build a name mapping. The underlying services are largely the same, but an answer option that names the old product where the question names the new one is a deliberate trap.
Treating the exam as a coding test and grinding TensorFlow syntax. Google states outright that the exam does not directly assess coding skill.
Aim for enough Python and SQL to read a snippet and understand its intent, which is the standard Google sets. Put the recovered hours into product selection and operational judgement, which is what the six sections actually measure.
Skipping BigQuery ML because it looks like the beginner option. Architecting low-code AI solutions is roughly 13 percent of the exam and includes fine-tuning Gemini models using BigQuery.
Train real models in BigQuery ML with SQL alone, including feature engineering and prediction. The exam rewards recognising when the low-code route is the correct answer rather than the lazy one.
Confusing training-serving skew with data drift on monitoring questions. The exam guide lists them as separate failure modes alongside concept drift and feature attribution drift.
Learn each by its cause: skew comes from preprocessing differing between training and serving, data drift from input distributions shifting, concept drift from the input to output relationship changing, feature attribution drift from feature importance shifting.
Ignoring generative AI security. The guide names data exfiltration, malicious prompting and sharing sensitive data with LLMs, with regex, safety filters and Model Armor as the tooling.
Deploy a generative endpoint, apply safety filters and Model Armor, and attempt a prompt injection against it. This is one of the newest parts of the blueprint and it is the part most study material has yet to catch up on.
Booking an online proctored exam without checking the workspace rules. Google requires a completely clear desk with no papers, no writing instruments, no external monitors and no food or drink.
Run the OnVUE system test and clear the room at least 24 hours in advance. Google also requires the ability to conduct a room scan and states that no other person may enter the testing area at any point.
Planning to step away during the two hours. Google states no breaks are allowed and that taking any break cancels the session and causes the result to be rejected.
Prepare to sit for the full two hours without leaving camera view. If you need a break for a medical reason, request the accommodation from your CM Connect account before scheduling, since Google says accommodations take two to four weeks to arrange.
Registering with a name that does not match your identification. Google requires your legal first and last name in CM Connect to match your government-issued photo ID exactly, in Romanized characters.
Check and correct your CM Connect profile as soon as you decide to sit the exam. A mismatch on the day means you cannot test and your fee may be forfeited, and name changes have to go through Google support rather than being self-service.
Assuming you can retake quickly after a failure. Associate and professional exams allow four attempts in a two-year period with waits of 14 days, then 60 days, then 365 days.
Treat the first attempt as expensive. Use the section-level score report under Exam History in the Candidate Portal to target the weakest sections, and note that this report is not produced for beta or renewal exams.
Exam Day Tips
- 1
For an onsite sitting, arrive at least 15 minutes early and bring two forms of identification, one of which must be a government-issued photo ID from Google's list of acceptable forms.
- 2
Leave personal items behind for an onsite exam. Google warns that storage at test centres may be limited or absent, and no food or drink is permitted in the testing area.
- 3
For an online sitting, launch between 10 minutes before and 20 minutes after your scheduled time. Miss that window and the session is cancelled automatically with the fee forfeited.
- 4
Clear the desk completely before an online exam: no papers, no pens, no scratch paper, no smart devices, no wearables, no food or water, and no headphones or earbuds.
- 5
Disconnect any external monitor and close every application before check-in. Google lists an unauthorised second screen and an invalid keystroke such as print screen among the reasons a result is invalidated.
- 6
Do not read the questions out loud. Google names reading exam content aloud and talking during the session as security violations that can suspend the session and reject the result.
- 7
Plan for no breaks at all. Google states that any break during the exam cancels the session and causes the result to be rejected unless it was approved in advance through the accommodations process.
- 8
Expect a provisional pass or fail on screen immediately after you submit, but do not photograph it. Google specifically prohibits taking a picture of the provisional result while the webcam is still on.
- 9
Budget 5 to 15 minutes before the clock starts. Google states the online proctor verifies your identity and secures the testing environment before launching the exam, and the full two hours begins only once the exam launches.
- 10
Allow 7 to 10 days for Google to confirm the official result after reviewing your exam record for compliance with the terms and conditions.
Career Paths & Salary Ranges
Machine learning engineer
Google's own description of the role: builds, evaluates, productionises and optimises AI solutions using Google Cloud alongside conventional ML approaches, handling large complex datasets and writing repeatable, reusable code.
$160k-$220k
MLOps engineer
Owns pipelines, retraining and monitoring. Automating and orchestrating ML pipelines is roughly 18 percent of the exam and monitoring AI solutions roughly 13 percent, so nearly a third of the blueprint is this role's daily work.
$160k-$220k
AI platform engineer
Builds the serving layer other teams deploy onto: endpoints, feature stores, model registries, container images and accelerator capacity. Serving and scaling models is roughly 20 percent of the exam.
$160k-$220k
Applied data scientist on Google Cloud
Works from BigQuery and notebooks through to production models, including BigQuery ML and AutoML routes. The collaboration section at roughly 16 percent covers exactly this handover between exploration and engineering.
$160k-$220k
Generative AI engineer
Tunes and operates Gemini, Imagen and Veo based applications, selects from Model Garden, optimises for cost and latency, and defends against prompt injection with Model Armor and safety filters. All of this appears explicitly in the current exam guide.
$160k-$220k
Prerequisites & Requirements
- None. Google states there are currently no prerequisites to take any Google Cloud certification exam.
- You must be at least 18 years old to take a Google Cloud certification exam.
- Google's recommended experience is three or more years in industry, including one or more years designing and managing solutions using Google Cloud.
- Minimum proficiency in Python and SQL is expected, sufficient to interpret code snippets in questions. Google states the exam does not directly assess coding skill.
- You need a CM Connect account with a legal first and last name that matches your government-issued photo ID, entered in Romanized characters, before you can schedule with Pearson.
- You may not sit the same exam while currently certified unless you are inside the renewal eligibility period, which for professional certifications opens 60 days before expiry.
Frequently Asked Questions
How much does the Professional Machine Learning Engineer exam cost?
200 USD plus tax where applicable, and the fee is identical whether you sit online-proctored or at a Pearson test centre. Google charges per attempt, so a retake costs another 200 USD. If you switch delivery method after booking, any voucher you used remains valid for the new registration as long as you cancel at least 24 hours in advance.
How long is the certification valid and when can I renew?
Two years from the date you certify. All Google Cloud professional certifications carry a two-year validity, while foundational and associate certifications last three years. The renewal eligibility period for a professional certification opens 60 days before your expiration date. Google also allows renewal up to 30 days after the certification's inactive date.
What happens if I fail?
You can retake after 14 days. If you fail a second time you wait 60 days, and after a third failure you wait 365 days. Associate and professional exams allow a maximum of four attempts in any two-year period. Payment is required for each attempt. Attempts in different languages or through different delivery methods still count toward the total and still trigger the waiting periods.
What is the passing score?
Google does not publish one and does not give you a numeric score. Results are pass or fail only. Google's stated reason is that the exams are designed to determine whether a candidate meets a minimum passing standard rather than to rank ability on a scale, so a number would be easy to misinterpret and would not be a useful diagnostic.
How long do results take?
You get a provisional pass or fail on screen immediately after you submit. Google then evaluates your exam record, including verification that you complied with the terms and conditions, and asks you to allow 7 to 10 days for confirmation of the official result. The provisional result also appears in your CM Connect account after the session ends.
Do I get feedback on which sections I was weak in?
Yes, if you fail. Google provides a detailed score report under Exam History in the Candidate Portal, breaking the exam into sections with the approximate percentage each contributed. Google notes that this feedback is guidance rather than a prediction of a future attempt, and that section-level reports are not produced for beta or renewal exams.
What identification do I need?
For an onsite exam, two forms of identification, one of which must be a government-issued photo ID from Google's list of acceptable forms. For an online exam, a government-issued photo ID shown to the proctor at check-in. In both cases your legal first and last name in CM Connect must match the ID exactly, and the legal name field requires Romanized characters in order to schedule with Pearson.
What are the online proctoring rules?
Take the exam in a quiet, well-lit room where nobody can disturb you, be prepared to conduct a room scan, and remove everything from your desk except the computer. External monitors must be disconnected and all applications closed. Food, drink, headphones and earbuds are prohibited. Google may suspend or invalidate a session for poor video or audio, excessive background noise, an unauthorised person in the room, or an invalid keystroke such as print screen.
Can I use scratch paper, a calculator or notes?
No. Google states these are not open-book exams and that your testing space must be free of resources, naming external monitors, scratch paper, writing instruments, note-taking materials, smart devices, wearables, food and water. Using any unauthorised item is flagged as misconduct and can result in losing all your Google Cloud certifications and being barred from future exams.
Are breaks allowed during the two hours?
No. Google states that no breaks are allowed and that taking any break during the exam cancels the session and causes Google to reject the result. If a medical condition requires a break, you must request the accommodation before scheduling, and any approved break must be taken within view of the camera or proctor depending on the medical need.
How do I request testing accommodations?
Request them from within your CM Connect account before you schedule the exam. Google states that accommodations may take two to four weeks to arrange, so start well ahead of the date you have in mind. Requesting after booking is not the supported route, and Google directs candidates who require an accommodation to contact them before scheduling.
Can I cancel or reschedule?
Yes, up to 24 hours before your scheduled start time through the self-service interface in your CM Connect account. Inside 24 hours a late rescheduling or cancellation fee applies. A no-show forfeits the entire fee with no refund. Google reviews exceptional circumstances case by case and requires supporting documentation, with any refund granted at its discretion.
What does the exam cover now that Vertex AI has been renamed?
The same services under Gemini Enterprise Agent Platform naming. Google states the exam was updated to reflect the transition from Vertex AI to Gemini Enterprise Agent Platform, alongside updates to the Google Cloud data and analytics stack, and that it prioritises Google Cloud native solutions. The exam guide now names Agent Platform AutoML, Agent Platform Workbench, Agent Platform Pipelines, Agent Platform Feature Store, Agent Platform Model Registry, Agent Platform Inference and Model Monitoring on Gemini Enterprise Agent Platform.
Do the section weights add up to 100 percent?
Not exactly, because Google publishes them as approximations. The guide gives roughly 13, 16, 21, 20, 18 and 13 percent for the six sections, each preceded by a tilde. Treat them as a guide to study allocation rather than as an exact item count, and note that the number of questions per section varies between exam forms.
How much coding do I need?
Enough Python and SQL to read a snippet, no more. Google's exam guide states directly that the exam does not assess coding skills and that a minimum proficiency in Python and SQL is sufficient to interpret any question containing code. The judgement being tested is product selection, architecture and operations.
How does this compare with the AWS machine learning certifications?
Google's exam is two hours of 50 to 60 questions at 200 USD, valid two years, with no published passing score and pass or fail results only. AWS Certified Machine Learning Engineer - Associate is 130 minutes of 65 questions at 150 USD, valid three years, with a published minimum passing score of 720 on a 100 to 1,000 scale and a score report that may carry a section-level table of classifications. AWS retired its Machine Learning - Specialty exam on 31 March 2026.
Should I take the Professional Data Engineer certification first?
Not necessarily, since Google requires no prerequisites for any of its certifications. The overlap sits in Section 2, which covers exploring and preprocessing data across BigQuery, Dataflow and Spark. If your background is data engineering, that section is the one you can move through quickly, and Sections 3 and 4 on scaling and serving models at roughly 41 percent combined will need most of your time.
Can I take the exam more than once while I am still certified?
No, unless you are inside the renewal eligibility period, which opens 60 days before expiry for professional certifications. Google states that attempting the same exam outside that window can lead to your result being rejected, decertification from the Google Cloud Certification Program, a ban from taking Google exams, and termination of any applicable business relationship.
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