G O O G L E C E R T I F I C A T I O N
Professional Machine Learning Engineer Practice Exam
Exam Number: 1011 | Last updated April 21, 2026 | 1246+ questions across 5 vendor-aligned objectives
Earning the Professional Machine Learning Engineer certification signals that you your ability to apply Google Cloud to real business problems. It is built for ML engineers and data scientists who productionize models and pipelines on Vertex AI and the broader Google Cloud ML stack. A passing score proves you can map platform capabilities to outcomes and make defensible technical choices under time pressure.
Heavy-weighted areas define where study time pays back fastest: 20% targets Automating and Orchestrating ML Pipelines (Vertex AI Pipelines, Kubeflow components, CI/CD for models, monitoring drift); 18% targets Scaling Prototypes into ML Models (custom training, distributed training, hyperparameter tuning); 18% targets Serving and Scaling Models (Vertex AI endpoints, batch prediction, online serving, model optimization).
Supporting domains fill out the blueprint: 16% covers Collaborating Within and Across Teams to Manage Data and Models (feature stores, metadata management, model registry, experiment tracking); 16% covers Monitoring ML Solutions (skew detection, explainability, responsible AI, retraining triggers); 12% covers Architecting Low-Code ML Solutions (AutoML, BigQuery ML, pre-trained APIs, Vertex AI Agent Builder). Each still appears on the exam, so none can be safely skipped.
Every answer links to the source. Each explanation below includes a hyperlink to the exact Google documentation page the question was derived from. PowerKram is the only practice platform with source-verified explanations. Learn about our methodology →
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Question #1 - Automating and Orchestrating ML Pipelines
An ML team at a subscription streaming service needs to rerun an entire training and deployment workflow every week, with reproducible steps, versioned artifacts, and metadata lineage.
Which Google Cloud service is purpose-built for this requirement?
A) Cloud Scheduler calling a shell script
B) Vertex AI Pipelines
C) Compute Engine cron jobs on a VM
D) A manual Jupyter notebook run weekly
Show solution
Correct answers: B – Explanation:
Vertex AI Pipelines orchestrates ML workflows with versioned artifacts and ML Metadata for lineage. Cloud Scheduler shell scripts and Compute Engine cron do not track ML lineage. Manual notebooks are not reproducible or automated. Source: Check Source
Question #2 - Serving and Scaling Models
A retailer needs to serve a trained recommendation model with sub-100ms online latency and autoscaling that reacts to traffic.
Which Vertex AI capability fits best?
A) A single Compute Engine VM with the model loaded
B) Cloud Storage signed URLs to a SavedModel file
C) Vertex AI online endpoints with autoscaling
D) Batch prediction jobs triggered per request
Show solution
Correct answers: C – Explanation:
Vertex AI online endpoints serve models with autoscaling and low-latency online inference, matching the requirement. A single VM does not autoscale. Signed URLs to a SavedModel are not inference. Batch prediction is throughput-oriented, not sub-100ms per request. Source: Check Source
Question #3 - Scaling Prototypes into ML Models
A data scientist has a working PyTorch prototype on a single GPU and needs to scale training to multiple machines without refactoring to a proprietary framework.
Which Vertex AI capability fits?
A) Vertex AI custom training with distributed training on a worker pool
B) AutoML tabular only
C) BigQuery ML linear regression
D) A Cloud Function per epoch
Show solution
Correct answers: A – Explanation:
Vertex AI custom training supports distributed PyTorch training across worker pools while keeping the framework. AutoML and BigQuery ML are different abstractions. A Cloud Function per epoch would not scale or converge as expected. Source: Check Source
Question #4 - Architecting Low-Code ML Solutions
A marketing analyst already works in BigQuery and wants to build a logistic regression for lead scoring with minimal MLOps overhead.
Which Google Cloud option best matches the simplicity goal?
A) Distributed TensorFlow on GKE
B) A self-hosted MLflow server on Compute Engine
C) A bespoke PyTorch training pipeline on Vertex AI
D) BigQuery ML CREATE MODEL
Show solution
Correct answers: D – Explanation:
BigQuery ML lets analysts train directly with SQL on the data they already have, which is the simplest managed option. Distributed TensorFlow, self-hosted MLflow, and bespoke PyTorch all add operational overhead that contradicts the minimal-MLOps goal. Source: Check Source
Question #5 - Monitoring ML Solutions
An ML engineer wants automatic alerts when the distribution of a served model’s inputs drifts from training data.
Which Vertex AI feature provides that?
A) Cloud DNS monitoring
B) Vertex AI Model Monitoring for feature skew and drift
C) Cloud Storage object versioning alone
D) Artifact Registry scanning
Show solution
Correct answers: B – Explanation:
Vertex AI Model Monitoring detects feature skew and drift on deployed models and raises alerts. DNS monitoring, object versioning, and container scanning are not ML observability. Source: Check Source
Question #6 - Collaborating Within and Across Teams to Manage Data and Models
Multiple ML teams are redefining the same ‘customer 30-day spend’ feature in slightly different ways and drifting apart.
Which Google Cloud capability addresses that governance problem?
A) Vertex AI Feature Store with shared feature definitions
B) A Google Sheet of feature names circulated weekly
C) Cloud Logging alerts on feature names
D) Disabling feature engineering by policy
Show solution
Correct answers: A – Explanation:
Vertex AI Feature Store is a shared, governed repository of reusable features that eliminates silent drift. A shared spreadsheet is not enforced. Logging alerts do not govern definitions. Banning feature engineering is impractical. Source: Check Source
Question #7 - Serving and Scaling Models
A churn model needs to score 50 million customers once per night and write results to BigQuery. Online latency is not required.
Which serving mode fits best?
A) Vertex AI online endpoint sized for peak RPS
B) A Cloud Function called 50 million times in a loop
C) Vertex AI batch prediction with BigQuery destination
D) A persistent Compute Engine VM polling every second
Show solution
Correct answers: C – Explanation:
Batch prediction processes large volumes efficiently and writes to BigQuery natively, matching the nightly scoring pattern. Online endpoints for batch is wasteful. Cloud Function loops and polling VMs are slow and costly. Source: Check Source
Question #8 - Automating and Orchestrating ML Pipelines
An MLOps lead wants trained models to be promoted only after passing evaluation thresholds, and to be rolled back automatically on regressions.
Which Vertex AI component tracks model versions and supports that workflow?
A) Cloud DNS zone changes
B) Cloud Scheduler alone
C) Cloud Storage objects with random names
D) Vertex AI Model Registry integrated with pipelines
Show solution
Correct answers: D – Explanation:
Vertex AI Model Registry stores model versions with metadata that pipelines can gate releases on. DNS changes, Scheduler alone, and randomly named objects do not model model-version lifecycle. Source: Check Source
Question #9 - Monitoring ML Solutions
A credit risk team must explain to regulators why a specific applicant was rejected by the model.
Which Vertex AI capability provides per-prediction feature attributions?
A) Vertex Explainable AI
B) Cloud Armor WAF rules
C) BigQuery BI Engine
D) Cloud Build triggers
Show solution
Correct answers: A – Explanation:
Vertex Explainable AI produces feature attributions per prediction, which is required for this kind of regulatory explanation. Cloud Armor, BI Engine, and Build triggers do not produce ML explanations. Source: Check Source
Question #10 - Collaborating Within and Across Teams to Manage Data and Models
A research team wants to compare 40 training runs with different hyperparameters and reproducibly share results across the team.
Which Vertex AI capability is purpose-built for this?
A) Emailing CSVs between teammates
B) Vertex AI Experiments and ML Metadata
C) A single shared Google Doc
D) Deleting older runs to save space
Show solution
Correct answers: B – Explanation:
Vertex AI Experiments with ML Metadata captures runs, parameters, and metrics for comparison and sharing. Emailing CSVs and Google Docs are not reproducible experiment tracking. Deleting runs loses information. Source: Check Source
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Exam mode & learn mode · Score by objective · Updated April 21, 2026
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What the Machine Learning Engineer exam measures
- Architecting Low-Code ML Solutions (12%): Apply Google Cloud practices to AutoML, BigQuery ML, pre-trained APIs, Vertex AI Agent Builder.
- Collaborating Within and Across Teams to Manage Data and Models (16%): Apply Google Cloud practices to feature stores, metadata management, model registry, experiment tracking.
- Scaling Prototypes into ML Models (18%): Apply Google Cloud practices to custom training, distributed training, hyperparameter tuning.
- Serving and Scaling Models (18%): Apply Google Cloud practices to Vertex AI endpoints, batch prediction, online serving, model optimization.
- Automating and Orchestrating ML Pipelines (20%): Apply Google Cloud practices to Vertex AI Pipelines, Kubeflow components, CI/CD for models, monitoring drift.
- Monitoring ML Solutions (16%): Apply Google Cloud practices to skew detection, explainability, responsible AI, retraining triggers.
How to prepare for this exam
- Review the Professional Machine Learning Engineer official exam guide end to end before you commit a study plan, so every later hour is spent against the published blueprint.
- Complete the relevant Google Cloud Skills Boost learning path and treat its labs as non-optional rather than extra credit.
- Get hands-on practice in Qwiklabs sandbox, repeating the same tasks from memory until configuration feels routine.
- Apply what you learn in real-world project experience — your day job, a volunteer project, or an open-source contribution — so the concepts stick.
- Master one objective at a time, starting with the highest-weighted domain on the blueprint and moving down from there.
- Use PowerKram learn mode with feedback and sourced links to close gaps while the answer rationale is still fresh.
- Finish with PowerKram exam mode across all objectives under realistic time pressure before you book the real exam.
Career paths and salary outlook
Holding the Professional Machine Learning Engineer certification typically supports roles such as:
- Machine Learning Engineer: roughly $ 140,000 to $200,000 USD per year in the US market (range varies by region, years of experience, and specialization). See current data on Glassdoor.
- MLOps Engineer: roughly $ 135,000 to $190,000 USD per year in the US market (range varies by region, years of experience, and specialization). See current data on Levels.fyi.
- Applied Scientist (GCP): roughly $ 160,000 to $230,000 USD per year in the US market (range varies by region, years of experience, and specialization). See current data on Payscale.
Official resources
Work directly from Google’s own preparation resources and treat third-party content as a supplement: