Free PMLE practice test — 990+ PMLE practice questions with detailed explanations across all 7 official PMLE exam domains. Every set is scored and drawn from the live question bank — so you practise exactly what the exam tests, not outdated dumps.
Courseiva includes 990+ Google Professional Machine Learning Engineer practice questions across the official exam domains.
Feature
Courseiva
This free PMLE practice test mirrors the structure and difficulty of the real Google Professional Machine Learning Engineer exam. Every question is written against the official 2026 exam blueprint published by Google Cloud, ensuring you practise exactly what the exam tests — not last year's objectives.
The PMLE blueprint is divided into 7weighted domains. Questions on this page are distributed proportionally across each domain, so the mix you see here reflects the same weighting you'll face on exam day. High-weight domains like Serving and Scaling Models and Scaling Prototypes into ML Models contribute the most questions, meaning focused practice on these areas gives you the highest return on study time.
PMLE Exam Blueprint — 7 Domains
Automating and Orchestrating ML Pipelines
Collaborating Within and Across Teams to Manage Data and Models
Serving and Scaling Models
Monitoring ML Solutions
Architecting Low-Code ML Solutions
Scaling Prototypes into ML Models
Collaborating to manage data and models
64 numbered sets, 7 domain question banks, and targeted sessions — every page is a unique set of questions.
79 questions
All 79 questions →Choose all correct answers
Each chapter page covers one topic in depth — theory, key concepts, and focused practice questions. Use these to close knowledge gaps before returning to full practice tests.
Getting the most from practice questions requires more than just clicking through answers. Here is the study method used by candidates who pass PMLE on their first attempt:
Answer before revealing
Read each PMLE question fully, eliminate obviously wrong choices, then commit to an answer before clicking to reveal. This active recall process is what builds lasting knowledge.
Read every explanation
Even when you answer correctly, read the full explanation. Knowing WHY the right answer is correct — and why the distractors are wrong — is what separates a 750 score from a 900 score.
Track weak domains
Note which PMLE domains you get wrong most often. Then do a targeted 20-30 question session focused only on that domain until your accuracy improves.
Simulate exam pacing
The real PMLE gives you roughly 2 minutes per question. Use the 60 or 120-question sessions to practise hitting that pace comfortably.
Most candidates who pass PMLE on their first attempt report doing between 400 and 800 practice questions over 4–8 weeks of preparation. With 990+ questions in the Courseiva bank, you have more than enough material to build that repetition without seeing the same question twice.
Answer each question to reveal the full explanation and correct answer. This starter set is drawn from all 7 exam domains in blueprint proportion. Use the session selector to start a longer focused practice run.
A data scientist creates a custom Python function component for a Vertex AI pipeline using the Kubeflow Pipelines SDK v2. The component takes a string parameter 'input_text' and outputs a Metrics artifact. The scientist wants to include a lightweight Python function without building a container. Which code snippet correctly defines this component?
Select an answer to reveal the explanation
A machine learning engineer is building a Vertex AI pipeline that uses a pre-built Google Cloud Pipeline Components (GCPC) to train a custom model. Which component should the engineer use to submit a custom training job to Vertex AI?
Select an answer to reveal the explanation
A team has a Vertex AI pipeline that includes a container component for data preprocessing. The team notices that the component is re-executed every time the pipeline runs, even when the inputs and code haven't changed. They want to leverage pipeline caching to avoid redundant executions. What should they do to enable caching for this component?
Select an answer to reveal the explanation
A data science team uses Vertex AI Experiments to track training runs. They want to automatically log parameters, metrics, and artifacts for all runs with minimal code changes. Which approach should they take?
Select an answer to reveal the explanation
A machine learning team wants to share features across multiple models to reduce training-serving skew and ensure consistency. Which Vertex AI service should they use?
Select an answer to reveal the explanation
A data scientist wants to deploy a trained TensorFlow model to Vertex AI for online predictions. They need to serve predictions with low latency and want to leverage GPU acceleration. Which machine type should they select when creating the Vertex AI endpoint?
Select an answer to reveal the explanation
You are deploying a new version of a model to a Vertex AI endpoint that already has a champion model serving 100% of traffic. You want to gradually shift traffic to the new version while monitoring for errors. Which approach should you use?
Select an answer to reveal the explanation
A company is using Vertex AI Prediction with a custom container that performs preprocessing before inference. The preprocessing step is CPU-intensive and the inference step uses a GPU. They want to minimize prediction latency while optimizing cost. Which architecture should they use?
Select an answer to reveal the explanation
You need to serve a large embedding model for similarity search with low latency. The model was trained to generate 256-dimensional embeddings. You plan to use Vertex AI Vector Search. Which index type should you choose to balance accuracy and performance for a dataset with 10 million vectors?
Select an answer to reveal the explanation
A data scientist has deployed a model on Vertex AI Endpoints and wants to monitor the model's predictions for any drift over time. Which Vertex AI service should they use?
Select an answer to reveal the explanation
An MLOps engineer needs to collect ground truth labels for a deployed classification model to compare predictions against actuals. Where should the engineer store the ground truth data to enable Vertex AI model quality monitoring?
Select an answer to reveal the explanation
A retail company wants to predict customer churn using historical purchase data stored in BigQuery. The data includes customer demographics, transaction history, and support interactions. The team is comfortable writing SQL and wants to avoid moving data to a separate environment. Which approach should they take?
Select an answer to reveal the explanation
A data scientist needs to train a time-series forecasting model on historical sales data stored in BigQuery to predict future demand. The data has strong seasonal patterns. Which BigQuery ML model type should they use?
Select an answer to reveal the explanation
You have a TensorFlow training script that runs on a single machine. To speed up training on Vertex AI with 8 GPUs on a single machine, which strategy should you use?
Select an answer to reveal the explanation
A data science team is building a feature engineering pipeline that processes large-scale data from BigQuery daily. They need to compute aggregate features and store the results in Vertex AI Feature Store for both online serving and offline training. Which Google Cloud service is best suited for this batch computation?
Select an answer to reveal the explanation
You are fine-tuning a large language model (LLM) from Hugging Face Transformers using Vertex AI Training. The model has 7 billion parameters and does not fit into the memory of a single GPU. You need to train across multiple GPUs, splitting the model layers across devices. Which distributed training approach should you use?
Select an answer to reveal the explanation
A company is using Vertex AI Vizier for hyperparameter tuning of a model with 5 integer hyperparameters, each with a range of 10-100. They have a budget of 50 trials and want to maximize the chance of finding the best configuration. Which Vizier algorithm should they use?
Select an answer to reveal the explanation
A data science team uses BigQuery to store raw data and Vertex AI for model training. They want to ensure that only authorized users can access training data, and that model artifacts are automatically versioned and tracked. Which combination of Google Cloud services should they use?
Select an answer to reveal the explanation
Answer all 18 questions to see your domain score breakdown
A structured study plan dramatically increases your chances of passing PMLE on the first attempt. The most effective approach combines reading the official Google Cloud documentation or a study guide, watching video explanations for difficult concepts, and then reinforcing everything with daily practice questions.
We recommend the following weekly structure for PMLE preparation:
Cover each PMLE domain systematically. Read the exam objectives, watch explanatory content, and do 10–20 practice questions per domain to test understanding as you go.
Run full 50–60 question mixed sessions daily. Review every wrong answer in detail. Identify which domains are consistently scoring below 70% and revisit those study materials.
Do 100–120 question timed sessions to simulate real exam conditions. Aim for consistent scores above 80% before booking your exam date. A score above 80% in practice typically translates to a passing PMLE score.
On exam day, the PMLE tests your ability to apply knowledge to realistic scenarios — not just recall definitions. This is why reading explanations and understanding the reasoning behind every answer matters more than simply grinding question volume. Use the high-count sessions (100, 120) in the final weeks as your confidence benchmark.
Questions
60
On the real exam
Time limit
120 min
2 min per question
Passing score
720/1000
Scaled scoring
The PMLE exam uses a scaled scoring system — your raw score of correct answers is converted to a score out of 1000. A passing score of 720/1000 does not mean you need 72% of questions correct; the conversion accounts for question difficulty. Consistently scoring above 75–80% on practice tests puts you in a strong position to achieve 720/1000 on the real exam.
Scenario-based questions covering exam objectives with detailed answer explanations.
Yes. Courseiva provides free Google Professional Machine Learning Engineer practice questions with explanations across the official exam domains. Start with a quick practice test, then continue with topic-based practice, mock exams, missed-question review, bookmarked questions, weak-topic recommendations, and readiness tracking. No account required. Create a free account to unlock per-domain analytics and progress tracking across every certification on the platform. Courseiva is free forever, supported by advertising.
Every question is written against the official PMLE exam blueprint published by Google Cloud. Our questions follow the same wording style, scenario complexity, and answer structure as the actual exam. They are original questions — not brain dumps — so you learn the underlying concepts and reasoning, not just memorised answers. Candidates who study with brain dumps often pass but have no transferable knowledge; Courseiva questions make you genuinely competent.
Most candidates who pass PMLE on their first attempt do 30–60 questions per day. Use the Quick 10 session for daily warm-ups when you are short on time. On study days, run a 50 or 60-question session to build stamina. Reserve 100 and 120-question sessions for the final two weeks when you want to simulate real exam conditions and benchmark your readiness.
The PMLE covers 7 domains: Automating and Orchestrating ML Pipelines (18%), Collaborating Within and Across Teams to Manage Data and Models (11%), Serving and Scaling Models (20%), Monitoring ML Solutions (13%), Architecting Low-Code ML Solutions (13%), Scaling Prototypes into ML Models (20%), Collaborating to manage data and models (5%). Each domain carries a different weight, so allocate your study time accordingly. The highest-weighted domains — Serving and Scaling Models and Scaling Prototypes into ML Models — should receive the most attention.
Exam dumps are memorised question-and-answer lists taken from actual exam papers, often obtained illegally and shared without Google Cloud's authorisation. Using them violates your NDA and Google Cloud's certification agreement, and can result in certification revocation. Courseiva questions are 100% original — written by certified engineers to test the same knowledge areas using new scenarios and wording. You learn the material, not just the answers.
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