CertLabProGoogle Cloud Professional Machine Learning Engineer
225 שאלות תרגול
נבדק לאחרונה: April 2026
הערות אישיות וקישורים למשאבים למסע הלמידה שלך
סנן לפי הסמכה
The Google Cloud Professional Machine Learning Engineer (PMLE) validates the ability to design, build, and productionize ML models on Google Cloud — covering Vertex AI end-to-end, AutoML, custom training, model deployment, MLOps pipelines, and the operational realities of serving ML at scale. The exam emphasizes Vertex AI Pipelines (Kubeflow), Vertex AI Model Registry, Feature Store, Endpoints (online and batch), TensorFlow Extended (TFX), explainability with Vertex Explainable AI, monitoring for drift and skew, and integration with BigQuery ML and Generative AI offerings (Gemini family, Model Garden). Question style is scenario-heavy and rewards candidates who think about production ML lifecycle (CI/CD/CT), not just modeling.
AutoML for tabular / vision / language, BigQuery ML, pre-trained APIs (Vision, Speech, Translation, Document AI), and choosing between low-code and custom paths. Smallest domain at 12% but high-density.
Vertex AI Workbench, Feature Store (online and offline), data labeling and annotation, model versioning and metadata, experiment tracking with Vertex AI Experiments. 16%.
Custom training (single-node, distributed, GPU/TPU), hyperparameter tuning with Vizier, container-based training, Vertex AI Tuning, dealing with dataset bias. 18%.
Vertex AI Endpoints (online with autoscaling, traffic split), batch prediction, TensorFlow Serving, latency / throughput / cost tradeoffs, edge deployment. 19%.
Largest domain at 21%. Vertex AI Pipelines (Kubeflow Pipelines SDK and TFX), CI/CD/CT, retraining triggers, Cloud Build integration. Heavy on lifecycle automation.
Vertex AI Model Monitoring (training-serving skew, drift, attribution drift), Vertex Explainable AI, performance and cost monitoring with Cloud Operations. 14%.
$145k–$210k–$320k USD annual
Range reflects US-based senior ML engineers where Vertex AI is the primary platform. FAANG L5 ML engineer TC clears $400k; staff and principal levels go higher. ML engineering is the highest-paying cloud-engineering specialty by base, and the GCP-specific candidate pool is small relative to AWS / multi-cloud, which helps PMLE holders at hiring time.
Source: levels.fyi 2025–2026 (Google L4–L6 ML engineers, FAANG and AI-startup senior ML), U.S. BLS OEWS May 2024 (15-2099 mathematical science occupations / data scientists, 15-1252 software developers). Figures are approximate; actual compensation depends on role, region, and experience.
PMLE demand surged through 2024–2026 as GenAI hiring pulled qualified ML engineers across the board. Heavy demand at Google Cloud partners with ML practices, AI-first startups building on Vertex AI, and Google itself for customer-engineering ML specialists. The cert is also valuable on multi-cloud ML platform teams. PMLE pairs naturally with Professional Data Engineer (PDE) for an end-to-end "data + ML" senior profile and with Generative AI Leader (GAIL) for a strategic-plus-technical pair. Holders consistently report strong recruiter response — ML-engineering candidate pools remain tight even as the GenAI hype peak normalizes.
There are no formal prerequisites. Google recommends three or more years of industry experience and one or more years architecting and operationalizing ML solutions on Google Cloud. In practice, PMLE is not a credible first GCP cert and is rarely a credible first ML cert — successful candidates have shipped at least one production ML model and have working knowledge of TensorFlow or PyTorch.
Strong Python fluency, working knowledge of scikit-learn / TensorFlow / Keras / PyTorch, and at least conceptual familiarity with Kubeflow or another ML pipeline framework are effectively required. Comfort with BigQuery SQL is helpful since BigQuery ML appears in many scenarios. The official ML Engineer Learning Path on Google Cloud Skills Boost (around 50–80 hours) is a good baseline; most successful candidates also build a non-trivial Vertex AI Pipelines project end-to-end.
PMLE is rated professional and is consistently hard for candidates without production ML experience. Plan on 100–150 hours of study over 10–14 weeks if PMLE is your first ML-engineering cert, or 50–80 hours over 5–8 weeks if you already hold an AWS or Azure ML certification and have shipped models on either platform. The exam is 50–60 multiple-choice / multiple-select questions in 120 minutes, delivered through Pearson VUE (Google migrated from Kryterion / Webassessor in early 2026).
The most common stumbling block is the MLOps lifecycle — when to retrain, how to detect drift vs. skew, how to wire Vertex AI Pipelines into Cloud Build for CI/CD/CT. The second stumbling block is choosing between AutoML, BigQuery ML, custom training on Vertex AI, and pre-trained APIs for a given scenario, where Google's "preferred" answer often hinges on team skill and time-to-value rather than pure technical fit. Google does not publish numeric scores — only pass/fail. The credential is valid for two years and recertification requires re-passing the current exam.
Current exam guide refreshed in late 2024 to add Generative AI integration scenarios (Gemini, Model Garden), expanded Vertex AI Agent Builder coverage, and updated Feature Store content.
Major refresh consolidating around Vertex AI as the unified ML platform, retiring the older AI Platform / AutoML Tables coverage.
Original general availability, replacing the earlier "Data Engineer with ML focus" path.
PMLE (Google Cloud Professional Machine Learning Engineer) is a a challenging, scenario-heavy exam that requires deep hands-on experience and the ability to make architectural trade-off decisions Professional-level exam. Most candidates need 150–300 hours of study spread over 3–6 months for professional and expert-level exams. These exams typically expect prior associate-level proficiency. Most candidates who score consistently above the passing threshold on practice exams pass on their first attempt.