100% Free Google Professional-Machine-Learning-Engineer Practice Test Questions and Answers 2026

Start Learning with the Latest and Real 100% Free Google Professional-Machine-Learning-Engineer Exam Questions

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Total 289 Questions | Updated On: May 24, 2026

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Question 1

You work for a large technology company that wants to modernize their contact center. You have been asked to develop a solution to classify incoming calls by product so that requests can be more quickly routed to the correct support team. You have already transcribed the calls using the Speech-to-Text API. You want to minimize data preprocessing and development time. How should you build the model?

A : Use the AI Platform Training built-in algorithms to create a custom model.

B : Use AutoMlL Natural Language to extract custom entities for classification.

C : Use the Cloud Natural Language API to extract custom entities for classification.

D : Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm.

Answer: B

Question 2

You work at a subscription-based company. You have trained an ensemble of trees and neural networks to predict customer churn, which is the likelihood that customers will not renew their yearly subscription. The average prediction is a 15% churn rate, but for a particular customer the model predicts that they are 70% likely to churn. The customer has a product usage history of 30%, is located in New York City, and became a customer in 1997. You need to explain the difference between the actual prediction, a 70% churn rate, and the average prediction. You want to use Vertex Explainable AI. What should you do?

A : Train local surrogate models to explain individual predictions.

B : Configure sampled Shapley explanations on Vertex Explainable AI.

C : Configure integrated gradients explanations on Vertex Explainable AI.

D : Measure the effect of each feature as the weight of the feature multiplied by the feature value.

Answer: B

Question 3

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: B

Question 4

Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers' account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?

A : 1. Create a Pub/Sub topic for each user. 2. Deploy a Cloud Function that sends a notification when your model predicts that a user-s account balance will drop below the $25 threshold.

B : 1. Create a Pub/Sub topic for each user. 2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user-s account balance will drop below the $25 threshold.

C : 1. Build a notification system on Firebase. 2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold.

D : 1. Build a notification system on Firebase. 2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user-s account balance will drop below the $25 threshold.

Answer: D

Question 5

You are building a linear regression model on BigQuery ML to predict a customer’s likelihood of purchasing your company’s products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?

A : Use TensorFlow to create a categorical variable with a vocabulary list. Create the vocabulary file, and upload it as part of your model to BigQuery ML.

B : Create a new view with BigQuery that does not include a column with city information

C : Use Cloud Data Fusion to assign each city to a region labeled as 1, 2, 3, 4, or 5, and then use that number to represent the city in the model.

D : Use Dataprep to transform the state column using a one-hot encoding method, and make each city a column with binary values.

Answer: D

Page: 1 / 58

Total 289 Questions | Updated On: May 24, 2026

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