Free Google Professional-Machine-Learning-Engineer Practice Test Questions

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

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

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

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

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

Answer: C

Question 3

You are developing a model to identify traffic signs in images extracted from videos taken from the dashboard of a vehicle. You have a dataset of 100 000 images that were cropped to show one out of ten different traffic signs. The images have been labeled accordingly for model training and are stored in a Cloud Storage bucket You need to be able to tune the model during each training run. How should you train the model?

A : Train a model for object detection by using Vertex Al AutoML.

B : Train a model for image classification by using Vertex Al AutoML.

C : Develop the model training code for object detection and tram a model by using Vertex Al custom training.

D : Develop the model training code for image classification and train a model by using Vertex Al custom training

Answer: C

Question 4

You need to design an architecture that serves asynchronous predictions to determine whether a particular mission-critical machine part will fail. Your system collects data from multiple sensors from the machine. You want to build a model that will predict a failure in the next N minutes, given the average of each sensor’s data from the past 12 hours. How should you design the architecture?

A : 1. HTTP requests are sent by the sensors to your ML model, which is deployed as a microservice and exposes a REST API for prediction2. Your application queries a Vertex AI endpoint where you deployed your model.3. Responses are received by the caller application as soon as the model produces the prediction.

B : 1. Events are sent by the sensors to Pub/Sub, consumed in real time, and processed by a Dataflow stream processing pipeline.2. The pipeline invokes the model for prediction and sends the predictions to another Pub/Sub topic.3. Pub/Sub messages containing predictions are then consumed by a downstream system for monitoring.

C : 1. Export your data to Cloud Storage using Dataflow.2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data.3. Export the batch prediction job outputs from Cloud Storage and import them into Cloud SQL

D : 1. Export the data to Cloud Storage using the BigQuery command-line tool2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data.3. Export the batch prediction job outputs from Cloud Storage and import them into BigQuery.

Answer: C

Question 5

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

Free Google Professional-Machine-Learning-Engineer Exam Practice Questions 2026