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Google Cloud Professional ML Engineer

Google Cloud Professional Machine Learning Engineer covers building ML solutions on Google Cloud. Six domains: architecting low-code ML solutions, collaborating with stakeholders, scaling prototypes, serving and scaling models, automating and orchestrating ML pipelines, monitoring AI solutions. 50 to 60 questions in 2 hours.

Offered in: English, Japanese.

Owner: Google Cloud · Read: · Source: Professional Machine Learning Engineer certification

Exam fee at last check: $200, per the vendor. The official objectives are at cloud.google.com/learn/certification/machine-learning-engineer. Verified 2026-10-25.

Practice is free. The full analysis adds the right answer, the explanation and the source for every question, readiness by domain and a study plan, for one payment.

Answer the first question

Timed mock · Where you stand

How the Google Cloud Professional ML Engineer exam is weighted
  1. Architecting low-code AI solutions~13%
  2. Collaborating within and across teams to manage data and models~16%
  3. Scaling prototypes into ML models~21%
  4. Serving and scaling models~20%
  5. Automating and orchestrating ML pipelines~18%
  6. Monitoring AI solutions~13%

Source: Vendor exam objectives. Checked 2026-10-25. Bar length is relative to the heaviest domain; the figure beside each name is the published weight.

What's on the exam

Architecting low-code AI solutions ~13%

BigQuery ML, AutoML, Vertex AI Workbench.

  • BigQuery ML: in-warehouse model training and inference
  • Vertex AI AutoML for tabular, image, text, video
  • When to use low-code vs custom training

Primary sources: Google Cloud Professional ML Engineer exam guide

Collaborating within and across teams to manage data and models ~16%

Exploring and preprocessing data, prototyping in notebooks, and tracking experiments.

  • Choosing a preprocessing tool by scale and complexity (BigQuery SQL, Dataflow, Apache Spark, in-memory Python)
  • Feature creation and consolidation in a feature store
  • Data privacy and handling PII
  • Notebook prototyping with PyTorch, scikit-learn and JAX, and notebook collaboration and security practice
  • Tracking and comparing experiments, model artifacts, versions and lineage; evaluating predictive and generative solutions, including LLM-as-a-judge

Primary sources: Google Cloud Professional ML Engineer exam guide as of June 1, 2026

Scaling prototypes into ML models ~21%

Vertex AI Training, custom containers, hyperparameter tuning.

  • Vertex AI custom training with Python packages or custom containers
  • Vertex AI Vizier for hyperparameter tuning
  • Distributed training: data parallel, model parallel

Primary sources: Vertex AI documentation

Serving and scaling models ~20%

Vertex AI Prediction endpoints, online and batch.

  • Online prediction endpoints with autoscaling
  • Batch prediction jobs
  • Private endpoints, traffic splitting for A/B testing
  • GPU and TPU acceleration

Primary sources: Vertex AI Prediction docs

Automating and orchestrating ML pipelines ~18%

Vertex AI Pipelines on Kubeflow, CI/CD for ML.

  • Vertex AI Pipelines with KFP SDK
  • Pipeline components, parameters, artifacts
  • Vertex AI Model Registry and aliasing
  • Cloud Build for CI/CD

Primary sources: Vertex AI Pipelines

Monitoring AI solutions ~13%

Vertex AI Model Monitoring, drift detection, performance metrics.

  • Feature attribution drift, training-serving skew
  • Model performance monitoring
  • Alerting via Cloud Monitoring
  • Explainable AI: Vertex AI feature attribution

Primary sources: Vertex AI Model Monitoring

Exam-day strategy

Identify the Vertex AI sub-product first. Most questions resolve by mapping the use case to Pipelines, Endpoints, Training, Vizier, Model Monitoring, or Workbench.

Watch for distractors that propose BigQuery ML when the use case clearly needs custom training, or vice versa.

On serving questions, distinguish online prediction (low latency) from batch prediction (cost-efficient bulk) from streaming.

Pipelines questions usually have one canonical KFP-based answer; eliminate options that recommend cron + scripts.

Pace at roughly 2 minutes per question across a 120-minute exam. Flag and return on multi-service architecture questions.

How to prepare for Google Cloud Professional ML Engineer

  • EVIDENCE-BACKEDPractice in the same format as the exam. How well practice matches the final test shapes how much it helps. (Yang et al., 2021)
  • EVIDENCE-BACKEDStart practice questions in week one, even on topics you have not studied. Failed attempts before seeing the answer still helped learning. (Kornell et al., 2009 and others)
  • EVIDENCE-BACKEDSpace your reviews over weeks instead of cramming the week before. (Cepeda et al., 2006 and others)
  • PLAUSIBLEDivide the minutes by the questions before you start, and keep a margin for a second pass if the exam allows one. (Sound arithmetic; no study compared time budgets on a licensing exam.)
Why this works

EVIDENCE-BACKED At least one peer-reviewed study or official source found the effect or states the fact. A study can be small.

PLAUSIBLE Reasonable and partly studied, but not tested on exams, or the results are mixed.

Practice that matches the final test format works better than practice that does not.

Failed attempts before seeing the answer still improved later learning of the material asked about; the benefit for material not asked about was virtually nil.

Spacing beat massing across hundreds of experiments and beat cramming with flashcards; combined with retrieval it raised scores on a real high-stakes exam.

Sound arithmetic; no study compared time budgets on a licensing exam.

Sources

  • Yang, C., Luo, L., Vadillo, M. A., Yu, R., & Shanks, D. R. (2021). Testing (quizzing) boosts classroom learning: A systematic and meta-analytic review. Psychological Bulletin, 147(4), 399-435. doi.org/10.1037/bul0000309
  • Kornell, N., Hays, M. J., & Bjork, R. A. (2009). Unsuccessful retrieval attempts enhance subsequent learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(4), 989-998. doi.org/10.1037/a0015729
  • Richland, L. E., Kornell, N., & Kao, L. S. (2009). The pretesting effect: Do unsuccessful retrieval attempts enhance learning? Journal of Experimental Psychology: Applied, 15(3), 243-257. doi.org/10.1037/a0016496
  • St. Hilaire, K. J., Chan, J. C. K., & Ahn, D. (2024). Guessing as a learning intervention: A meta-analytic review of the prequestion effect. Psychonomic Bulletin & Review, 31(2), 411-441. doi.org/10.3758/s13423-023-02353-8
  • Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 132(3), 354-380. doi.org/10.1037/0033-2909.132.3.354
  • Kornell, N. (2009). Optimising learning using flashcards: Spacing is more effective than cramming. Applied Cognitive Psychology, 23(9), 1297-1317. doi.org/10.1002/acp.1537
  • Kang, S. H. K. (2016). Spaced repetition promotes efficient and effective learning: Policy implications for instruction. Policy Insights from the Behavioral and Brain Sciences, 3(1), 12-19. doi.org/10.1177/2372732215624708
  • Janes, J. L., Dunlosky, J., Rawson, K. A., & Jasnow, A. (2020). Successive relearning improves performance on a high-stakes exam in a difficult biopsychology course. Applied Cognitive Psychology, 34(5), 1118-1132. doi.org/10.1002/acp.3699

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