Free Google Professional-Machine-Learning-Engineer Exam Questions

Become Google Certified with updated Professional-Machine-Learning-Engineer exam questions and correct answers

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Total 312 Questions | Updated On: Jul 25, 2026

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Question 1

You work as an ML researcher at an investment bank and are experimenting with the Gemini large language model (LLM). You plan to deploy the model for an internal use case and need full control of the model’s underlying infrastructure while minimizing inference time. Which serving configuration should you use for this task?

A : Deploy the model on a Vertex AI endpoint using one-click deployment in Model Garden.

B : Deploy the model on a Google Kubernetes Engine (GKE) cluster manually by creating a custom YAML manifest.

C : Deploy the model on a Vertex AI endpoint manually by creating a custom inference container.

D : Deploy the model on a Google Kubernetes Engine (GKE) cluster using the deployment options in Model Garden.

Answer: B

Question 2

You trained a model, packaged it with a custom Docker container for serving, and deployed it to Vertex Al Model Registry. When you submit a batch prediction job, it fails with this error "Error model server never became ready Please validate that your model file or container configuration are valid. There are no additional errors in the logs What should you do?

A : Add a logging configuration to your application to emit logs to Cloud Logging.

B : Change the HTTP port in your model's configuration to the default value of 8080

C : Change the health Route value in your models configuration to /heal thcheck

D : Pull the Docker image locally and use the decker run command to launch it locally. Use the docker logs command to explore the error logs.

Answer: D

Question 3

You are an ML engineer at a global car manufacture. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

A : ee individual features: binned latitude, binned longitude, and one-hot encoded car type.

B : feature obtained as an element-wise product between latitude, longitude, and car type.

C : feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type.

D : o feature crosses as an element-wise product: the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type.

Answer: C

Question 4

You recently deployed an ML model. Three months after deployment, you notice that your model is underperforming on certain subgroups, thus potentially leading to biased results. You suspect that the inequitable performance is due to class imbalances in the training data, but you cannot collect more data. What should you do? (Choose two.)

A : move training examples of high-performing subgroups, and retrain the model.

B : an additional objective to penalize the model more for errors made on the minority class, and retrain the model

C : move the features that have the highest correlations with the majority class.

D : ample or reweight your existing training data, and retrain the model

E : deploy the model, and provide a label explaining the model's behavior to users.

Answer: B,D

Question 5

While running a model training pipeline on Vertex Al, you discover that the evaluation step is failing because of an out-of-memory error. You are currently using TensorFlow Model Analysis (TFMA) with a standard Evaluator TensorFlow Extended (TFX) pipeline component for the evaluation step. You want to stabilize the pipeline without downgrading the evaluation quality while minimizing infrastructure overhead. What should you do?

A : clude the flag -runner=DataflowRunner in beam_pipeline_args to run the evaluation step on Dataflow.

B : ve the evaluation step out of your pipeline and run it on custom Compute Engine VMs with sufficient memory.

C : grate your pipeline to Kubeflow hosted on Google Kubernetes Engine, and specify the appropriate node parameters for the evaluation step.

D : tfma.MetricsSpec () to limit the number of metrics in the evaluation step.

Answer: C

Page: 1 / 63

Total 312 Questions | Updated On: Jul 25, 2026

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