Free PMLE practice test — 775+ 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 Every set is scored and drawn from the live question bank — so you practise exactly what the exam tests, not outdated dumps.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 775+ 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
Serving and Scaling Models
Architecting Low-Code ML Solutions
Scaling Prototypes into ML Models
Collaborating Within and Across Teams to Manage Data and Models
Monitoring ML Solutions
Collaborating to manage data and models
53 numbered sets, 7 domain question banks, and targeted sessions — every page is a unique set of questions.
100 questions
All 100 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 775+ 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.
In a Vertex AI Pipeline, a component produces a Metrics artifact that includes an evaluation metric. The engineer wants to use this metric value as a condition to decide whether to deploy the model. However, the metric value is stored in the artifact's metadata and not directly as a pipeline parameter. How can the engineer pass the metric value to a downstream conditional task?
Select an answer to reveal the explanation
You are using KFP SDK v2 to define a pipeline. You need to pass a large dataset between components. What is the best practice for passing data?
Select an answer to reveal the explanation
A machine learning engineer wants to define a lightweight pipeline component that runs custom Python code without building a container image. Which KFP SDK feature should they use?
Select an answer to reveal the explanation
You are using Vertex AI Vector Search for a product recommendation system. Your index is updated with new embeddings every hour. To minimize query latency while keeping the index fresh, what should you do?
Select an answer to reveal the explanation
A company needs to perform real-time similarity search on a dataset of 10 million embedding vectors. They expect low latency (under 10ms) and high throughput. Which index type should they use in Vertex AI Vector Search?
Select an answer to reveal the explanation
You are using Vertex AI Prediction with a custom container that requires a large model file (5 GB). Deployment takes 10 minutes to start. You want to reduce cold start latency. Which action would be MOST effective?
Select an answer to reveal the explanation
A company is deploying a new model version to an existing Vertex AI endpoint. They want to test the new version with 5% of traffic before fully rolling it out. What is the correct approach?
Select an answer to reveal the explanation
A financial institution needs to extract structured data from scanned PDFs of loan applications, including text fields and tables. They require a human review step for high-risk applications. Which Google Cloud service and configuration should they use?
Select an answer to reveal the explanation
An engineer needs to perform sentiment analysis on customer reviews. They have a large volume of text and need a solution that requires minimal customisation. Which option is most efficient?
Select an answer to reveal the explanation
Your PyTorch training script uses DistributedDataParallel (DDP) across 4 vertices each with 4 GPUs (16 GPUs total). You submit a Vertex AI custom training job. How should you configure the worker pool spec?
Select an answer to reveal the explanation
A machine learning team is deploying a PyTorch model on Vertex AI Prediction for real-time inference. The model was trained with preprocessing that includes tokenization and normalization. They want to embed the preprocessing logic in the model to reduce prediction latency and avoid additional service calls. Which approach should they take?
Select an answer to reveal the explanation
You want to use a pre-trained model from TensorFlow Hub for image classification, but you need to adapt it to classify your own custom categories with a small dataset. Which Vertex AI approach is most appropriate?
Select an answer to reveal the explanation
A data engineering team needs to compute rolling window features (7-day average, 30-day sum) from a high-volume stream of e-commerce events stored in BigQuery. They must output the features to Vertex AI Feature Store for online serving. Which approach is MOST cost-effective and scalable?
Select an answer to reveal the explanation
A team monitors features in Vertex AI Feature Store for drift. They want to set up automated alerts when a feature's distribution deviates significantly from the baseline. Which feature monitoring configuration should they use?
Select an answer to reveal the explanation
A data engineer needs to version large datasets (multiple TB) in a Data Lake on Google Cloud. They require ACID transactions to ensure consistency when multiple jobs read/write concurrently. Which solution should they use?
Select an answer to reveal the explanation
An ML team wants to automatically retrain a model when data drift is detected. They have set up a Cloud Monitoring alert on drift. What service should they use to trigger a retraining pipeline in response to the alert?
Select an answer to reveal the explanation
An MLOps team needs to automatically retrain a model when new training data becomes available. They use Vertex AI Pipelines. What is the recommended way to trigger the pipeline?
Select an answer to reveal the explanation
A data science team deploys a regression model to predict house prices. After one month, the mean absolute error (MAE) on the serving data increases by 20% compared to the test set. Which monitoring strategy should the team implement first to diagnose the issue?
Select an answer to reveal the explanation
A team is using Vertex AI AutoML to train a forecasting model. They need to retrain the model weekly and only if the new week's data significantly changes the data distribution. What is the most efficient way to achieve this?
Select an answer to reveal the explanation
A company is training a large neural network on Vertex AI and training jobs keep failing with 'Out of memory' errors. The VM uses a standard n1-standard-4 machine with 15 GB RAM. Which action should they take first?
Select an answer to reveal the explanation
Answer all 20 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%), Serving and Scaling Models (20%), Architecting Low-Code ML Solutions (13%), Scaling Prototypes into ML Models (20%), Collaborating Within and Across Teams to Manage Data and Models (11%), Monitoring ML Solutions (13%), 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 original — AI-assisted, checked against the official exam objectives, and published under the editorial oversight of an engineer with 12+ years' experience. They test the same knowledge areas using new scenarios and wording. You learn the material, not just the answers.
Per-domain analytics, spaced repetition, daily challenges — and every other certification on the platform.
Sign Up FreeFree forever · Every certification included