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Google Professional ML Engineer Exam (Professional Machine Learning Engineer Exam) - Google Cloud Exam Questions
Last updated on June 20, 2026
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1
Question
You work for a social media company. You need to detect whether posted images contain cars. Each training example is a member of exactly one class. You have trained an object detection neural network and deployed the model version to Al Platform Prediction for evaluation. Before deployment, you created an evaluation job and attached it to the Al Platform Prediction model version. You notice that the precision is lower than your business requirements allow. How should you adjust the model's final layer softmax threshold to increase precision?
Options
A
Decrease the recall.
B
Increase the number of false positives
C
Increase the recall
D
Decrease the number of false negatives
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2
Question
You are collaborating on a model prototype with your team. You need to create a Vertex Al Workbench environment for the members of your team and also limit access to other employees in your project. What should you do?
Options
A
1. Grant the Vertex Al User role to the default Compute Engine service account.
2. Grant the Service Account User role to each team member on the default Compute Engine
service account.
3. Provision a Vertex Al Workbench user-managed notebook instance that uses the default
Compute Engine service account.
2. Grant the Service Account User role to each team member on the default Compute Engine
service account.
3. Provision a Vertex Al Workbench user-managed notebook instance that uses the default
Compute Engine service account.
B
1 Create a new service account and grant it the Vertex Al User role.
2 Grant the Service Account User role to each team member on the service account.
3. Grant the Notebook Viewer role to each team member.
4 Provision a Vertex Al Workbench user-managed notebook instance that uses the new service account.
2 Grant the Service Account User role to each team member on the service account.
3. Grant the Notebook Viewer role to each team member.
4 Provision a Vertex Al Workbench user-managed notebook instance that uses the new service account.
C
1. Create a new service account and grant it the Notebook Viewer role.
2 Grant the Service Account User role to each team member on the service account.
3 Grant the Vertex Al User role to each team member.
4. Provision a Vertex Al Workbench user-managed notebook instance that uses the new service
account.
2 Grant the Service Account User role to each team member on the service account.
3 Grant the Vertex Al User role to each team member.
4. Provision a Vertex Al Workbench user-managed notebook instance that uses the new service
account.
D
1 Grant the Vertex Al User role to the primary team member.
2. Grant the Notebook Viewer role to the other team members.
3. Provision a Vertex Al Workbench user-managed notebook instance that uses the primary user’s
account.
2. Grant the Notebook Viewer role to the other team members.
3. Provision a Vertex Al Workbench user-managed notebook instance that uses the primary user’s
account.
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3
Question
You work for a bank You have been asked to develop an ML model that will support loan application decisions. You need to determine which Vertex Al services to include in the workflow You want to track the model's training parameters and the metrics per training epoch. You plan to compare the performance of each version of the model to determine the best model based on your chosen metrics. Which Vertex Al services should you use?
Options
A
Vertex Al Pipelines. Vertex Al Experiments, and Vertex Al Vizier
B
Vertex ML Metadata Vertex Al Experiments, and Vertex Al TensorBoard
C
Vertex ML Metadata Vertex Al Feature Store, and Vertex Al Vizier
D
Vertex Al Pipelines. Vertex Al Feature Store, and Vertex Al TensorBoard
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4
Question
You recently trained a XGBoost model that you plan to deploy to production for online inference Before sending a predict request to your model's binary you need to perform a simple data preprocessing step This step exposes a REST API that accepts requests in your internal VPC Service Controls and returns predictions You want to configure this preprocessing step while minimizing cost and effort What should you do?
Options
A
Build a Flask-based app. package the app and a pickled model in a custom container image, and deploy the model to Vertex Al Endpoints.
B
Build a custom predictor class based on XGBoost Predictor from the Vertex Al SDK. package it and a pickled model in a custom container image based on a Vertex built-in image, and deploy the model to Vertex Al Endpoints.
C
Store a pickled model in Cloud Storage Build a Flask-based app packages the app in a custom container image, and deploy the model to Vertex Al Endpoints.
D
Build a custom predictor class based on XGBoost Predictor from the Vertex Al SDK and package the handler in a custom container image based on a Vertex built-in container image Store a pickled model in Cloud Storage and deploy the model to Vertex Al Endpoints.
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5
Question
You are developing a Kubeflow pipeline on Google Kubernetes Engine. The first step in the pipeline is to issue a query against BigQuery. You plan to use the results of that query as the input to the next step in your pipeline. You want to achieve this in the easiest way possible. What should you do?
Options
A
Write a Python script that uses the BigQuery API to execute queries against BigQuery Execute this script as the first step in your Kubeflow pipeline
B
Use the Kubeflow Pipelines domain-specific language to create a custom component that uses the
Python BigQuery client library to execute queries
Python BigQuery client library to execute queries
C
Use the BigQuery console to execute your query and then save the query results Into a new BigQuery table.
D
Locate the Kubeflow Pipelines repository on GitHub Find the BigQuery Query Component, copy that component's URL, and use it to load the component into your pipeline. Use the component to execute queries against BigQuery
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