Does this free GCP PMLE practice exam include explanations?

Yes. Each question includes detailed option-by-option explanations to help you review the Google Cloud ML engineering concept being tested.

Free GCP PMLE mock test – Exam 1

Google Professional Machine Learning Engineer

Free Google Professional Machine Learning Engineer practice exam for GCP certification prep.

Q: Is this Google Professional Machine Learning Engineer practice exam free?

Yes. This Google Professional Machine Learning Engineer mock test is free to open, answer, and retake.

Use this free GCP Professional Machine Learning Engineer practice exam to review Vertex AI, Model Garden, BigQuery ML, MLOps pipelines, model monitoring, responsible AI, and production ML design decisions.

10 exam-style questions Free GCP PMLE mock test Detailed option explanations No signup required

Start Practice Exam 1 below. Answer each question first, then review the detailed explanation for every option to understand the Google Cloud ML engineering pattern behind the answer.

Google Professional Machine Learning Engineer Practice Exam 1

Free Google Professional Machine Learning Engineer practice exam 1 with scenario-based GCP ML questions and detailed explanations.

1 / 10

Question

An ML team wants to serve an internally fine-tuned large language model with the lowest possible inference latency. The team also needs full control over the serving infrastructure, accelerator placement, autoscaling behavior, and runtime configuration. Which deployment approach should the team use?

Which option meets the requirement?

A. Deploy the model to a Vertex AI endpoint by using one-click deployment from Model Garden.

B. Deploy the model on Google Kubernetes Engine by creating and managing custom Kubernetes manifests for the serving stack.

C. Deploy the model to a Vertex AI endpoint by creating a custom prediction container.

D. Use the managed Model Garden deployment workflow to deploy the model to GKE without customizing the manifests.

2 / 10

Question

A streaming music company retrained a next-song recommendation model with fresh data. Offline tests are positive, and the current model already runs on a Vertex AI endpoint. The team wants to test the new model with a small share of real production traffic while minimizing complexity. What should they do?

Which option meets the requirement?

A. Create a second endpoint and build a custom router that randomly sends 5 percent of requests to the new endpoint.

B. Capture production requests in BigQuery and compare batch predictions from both models before any live test.

C. Deploy the new model to the existing Vertex AI endpoint, use traffic splitting to send 5 percent of traffic to it, monitor business metrics, and gradually increase traffic if results improve.

D. Replace the deployed model immediately and rely on prediction drift alerts to decide whether to roll back.

3 / 10

Question

Several teams provide curated internal benchmark datasets for generative AI use cases. You need to compare widely available models that are also available on Google Cloud and produce a recommendation report efficiently. Which approach should you use?

Which option meets the requirement?

A. Deploy candidate models from Model Garden to Vertex AI endpoints and evaluate them with the Gen AI Evaluation Service API against the internal benchmark datasets.

B. Use public LLM leaderboard results and open-source datasets that look similar to the internal tasks.

C. Read Model Garden model cards and choose the model with the best published metrics.

D. Download all model weights manually, write a custom serving stack, and build a custom evaluation framework in Vertex AI Experiments.

4 / 10

Question

A team converts scanned customer forms to text with a TensorFlow model. New form images land in Cloud Storage throughout the day, and the team wants to run predictions once on the aggregated daily batch with minimal manual intervention. What should they use?

Which option meets the requirement?

A. A batch prediction job that reads input data from Cloud Storage and writes prediction output back to Cloud Storage.

B. A Compute Engine serving pipeline that stays online all day waiting for requests.

C. A Cloud Function that runs prediction every time an individual image is uploaded.

D. An online Vertex AI endpoint that receives one request per document as soon as the document arrives.

5 / 10

Question

A third-party data broker supplies training data but sometimes changes field names, data types, or formats without warning. You want the training pipeline to detect these issues before they degrade model quality. Which tool should you use?

Which option meets the requirement?

A. TensorFlow Data Validation to infer and enforce a schema and flag anomalies in incoming data.

B. TensorFlow Transform to silently replace all unexpected values with 0 during preprocessing.

C. tf.math summary operations in the model training script to calculate a few statistics.

D. Custom TensorFlow functions that check only the formatting errors you already know about.

6 / 10

Question

An ML research team frequently updates TensorFlow image segmentation code after reading new papers. They retrain on the same dataset to benchmark architecture changes. The team wants version control, low manual effort, and retraining only when code changes are pushed. What should they implement?

Which option meets the requirement?

A. Use Cloud Functions to poll Cloud Storage for changed source files and trigger training.

B. Manually submit training jobs with gcloud after every code update.

C. Store the code in Cloud Source Repositories and use a Cloud Build trigger to start the training workflow when new code is pushed.

D. Schedule Cloud Composer to check Cloud Storage once per day for source changes.

7 / 10

Question

A sales forecasting model has been deployed for months. The model binary has not changed, but accuracy has steadily deteriorated as market behavior changed. What is the most likely cause?

Which option meets the requirement?

A. The model needs retraining with newer production data because the data distribution has changed.

B. The original dataset must have had poor data quality, regardless of current market behavior.

C. The model architecture definitely has too few layers.

D. The original train, validation, and test split ratio was certainly incorrect.

8 / 10

Question

A pricing application chains several scikit-learn models. Other teams reuse the individual models in separate workflows. You need version control for each model and for the overall workflow, and the orchestration layer must scale down to zero. Which architecture best fits?

Which option meets the requirement?

A. Deploy each model to Vertex AI Endpoints and orchestrate the workflow from Cloud Run.

B. Package all model files inside one custom endpoint and track versions manually in BigQuery.

C. Deploy each model to Vertex AI Endpoints and orchestrate the workflow from another always-on Vertex AI endpoint.

D. Load all model files directly into Cloud Run and track model versions manually in BigQuery.

9 / 10

Question

A retail team uses BigQuery ML to forecast demand. They want an end-to-end process that prepares data, trains the model, evaluates it, and can run on a schedule with minimal infrastructure management. What is the best choice?

Which option meets the requirement?

A. Use BigQuery scheduled queries or Vertex AI Pipelines with BigQuery ML steps to run preparation, training, and evaluation in BigQuery.

B. Export all data to Compute Engine and run a cron job that invokes a local training script.

C. Move the data to Cloud Storage and build a custom Kubernetes training pipeline before loading results back to BigQuery.

D. Use only Looker Studio dashboards to monitor demand and manually trigger retraining.

10 / 10

Question

You are designing a responsible AI review for a credit scoring model. Stakeholders need to understand whether specific feature groups disproportionately influence decisions for protected populations. Which Google Cloud capability is most relevant?

Which option meets the requirement?

A. Vertex Explainable AI for feature attribution analysis combined with fairness review on evaluation slices.

B. Cloud Trace for request latency analysis.

C. Cloud Armor for blocking suspicious web traffic.

D. Cloud CDN for caching prediction responses.

Your score is

The average score is 0%

What Practice Exam 1 covers

Vertex AI endpoints, traffic splitting, and production rollout
Model Garden and generative AI evaluation
Batch prediction, data validation, and automated retraining
Cloud Build, Cloud Source Repositories, and MLOps triggers
Cloud Run orchestration and model versioning

Who should take this free mock test

Use this free Google Professional Machine Learning Engineer practice exam if you are preparing for the GCP PMLE certification and want focused practice with detailed answer explanations.

Browse GCP practice exams Visit DevOpsEngine

FAQ

Is this Google Professional Machine Learning Engineer practice exam free?

Yes. This GCP PMLE mock test is free to open and retake for certification study.

Does the free practice exam include explanations?

Yes. Each question includes detailed explanations for the correct and incorrect options so you can learn the service tradeoff, not just memorize an answer.

How should I review missed questions?

Read every option explanation, map the scenario to the relevant Google Cloud service, then revisit the matching Vertex AI, BigQuery ML, MLOps, or model monitoring topic before retaking the free practice exam.

Free Google Professional Machine Learning Engineer Practice Exam 1 | GCP PMLE Mock Test

Free Google Professional Machine Learning Engineer practice exam for GCP certification prep.

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Correct Answer: B

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Correct Answer: C

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Correct Answer: A

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Correct Answer: A

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Correct Answer: A

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Correct Answer: C

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Correct Answer: A

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Correct Answer: A

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Correct Answer: A

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Correct Answer: A

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What Practice Exam 1 covers

Who should take this free mock test

FAQ

Is this Google Professional Machine Learning Engineer practice exam free?

Does the free practice exam include explanations?

How should I review missed questions?