Google Professional Machine Learning Engineer Practice Exams

Last updated on Oct 10,2026

Exam Code: Professional Machine Learning Engineer
Exam Name: Google Professional Machine Learning Engineer
Certification Provider: Google
Latest update: Oct 10,2026

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Question #371

You have built a custom model that performs several memory-intensive preprocessing tasks before it makes a prediction. You deployed the model to a Vertex Al endpoint. and validated that results were received in a reasonable amount of time After routing user traffic to the endpoint, you discover that the endpoint does not autoscale as expected when receiving multiple requests

What should you do?

A . Use a machine type with more memory
B . Decrease the number of workers per machine
C . Increase the CPU utilization target in the autoscaling configurations
D . Decrease the CPU utilization target in the autoscaling configurations

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Correct Answer: D
D

Explanation:

According to the web search results, Vertex AI is a unified platform for machine learning development and deployment. Vertex AI offers various services and tools for building, managing, and serving machine learning models1. Vertex AI allows you to deploy your models to endpoints for online prediction, and configure the compute resources and autoscaling options for your deployed models2. Autoscaling with Vertex AI endpoints is (by default) based on the CPU utilization across all cores of the machine type you have specified. The default threshold of 60% represents 60% on all cores. For example, for a 4 core machine, that means you need 240% utilization to trigger autoscaling3. Therefore, if you discover that the endpoint does not autoscale as expected when receiving multiple requests, you might need to decrease the CPU utilization target in the autoscaling configurations. This way, you can lower the threshold for triggering autoscaling and allocate more resources to handle the prediction requests. Therefore, option D is the best way to solve the problem for the given use case. The other options are not relevant or optimal for this scenario.

Reference: Vertex AI

Deploy a model to an endpoint

Vertex AI endpoint doesn’t scale up / down

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

Question #372

You want to train an AutoML model to predict house prices by using a small public dataset stored in BigQuery. You need to prepare the data and want to use the simplest most efficient approach.

What should you do?

A . Write a query that preprocesses the data by using BigQuery and creates a new table Create a Vertex Al managed dataset with the new table as the data source.
B . Use Dataflow to preprocess the data Write the output in TFRecord format to a Cloud Storage bucket.
C . Write a query that preprocesses the data by using BigQuery Export the query results as CSV files and use those files to create a Vertex Al managed dataset.
D . Use a Vertex Al Workbench notebook instance to preprocess the data by using the pandas library Export the data as CSV files, and use those files to create a Vertex Al managed dataset.

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Correct Answer: A
A

Explanation:

The simplest and most efficient approach for preparing the data for AutoML is to use BigQuery and Vertex AI. BigQuery is a serverless, scalable, and cost-effective data warehouse that can perform fast and interactive queries on large datasets. BigQuery can preprocess the data by using SQL functions such as filtering, aggregating, joining, transforming, and creating new features. The preprocessed data can be stored in a new table in BigQuery, which can be used as the data source for Vertex AI. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can create a managed dataset from a BigQuery table, which can be used to train an AutoML model. Vertex AI can also evaluate, deploy, and monitor the AutoML model, and provide online or batch predictions. By using BigQuery and Vertex AI, users can leverage the power and simplicity of Google Cloud to train an AutoML model to predict house prices.

The other options are not as simple or efficient as option A, for the following reasons:

Option B: Using Dataflow to preprocess the data and write the output in TFRecord format to a Cloud Storage bucket would require more steps and resources than using BigQuery and Vertex AI. Dataflow is a service that can create scalable and reliable pipelines to process large volumes of data from various sources. Dataflow can preprocess the data by using Apache Beam, a programming model for defining and executing data processing workflows. TFRecord is a binary file format that can store sequential data efficiently. However, using Dataflow and TFRecord would require writing code, setting up a pipeline, choosing a runner, and managing the output files. Moreover, TFRecord is not a supported format for Vertex AI managed datasets, so the data would need to be converted to CSV or JSONL files before creating a Vertex AI managed dataset.

Option C: Writing a query that preprocesses the data by using BigQuery and exporting the query results as CSV files would require more steps and storage than using BigQuery and Vertex AI. CSV is a text file format that can store tabular data in a comma-separated format. Exporting the query results as CSV files would require choosing a destination Cloud Storage bucket, specifying a file name or a wildcard, and setting the export options. Moreover, CSV files can have limitations such as size, schema, and encoding, which can affect the quality and validity of the data. Exporting the data as CSV files would also incur additional storage costs and reduce the performance of the queries.

Option D: Using a Vertex AI Workbench notebook instance to preprocess the data by using the pandas library and exporting the data as CSV files would require more steps and skills than using BigQuery and Vertex AI. Vertex AI Workbench is a service that provides an integrated development environment for data science and machine learning. Vertex AI Workbench allows users to create and run Jupyter notebooks on Google Cloud, and access various tools and libraries for data analysis and machine learning. Pandas is a popular Python library that can manipulate and analyze data in a tabular format. However, using Vertex AI Workbench and pandas would require creating a notebook instance, writing Python code, installing and importing pandas, connecting to BigQuery, loading and preprocessing the data, and exporting the data as CSV files. Moreover, pandas can have limitations such as memory usage, scalability, and compatibility, which can affect the efficiency and reliability of the data processing.

Reference: Preparing for Google Cloud Certification: Machine Learning Engineer, Course 2: Data Engineering for ML on Google Cloud, Week 1: Introduction to Data Engineering for ML

Google Cloud Professional Machine Learning Engineer Exam Guide, Section 1: Architecting low-code ML solutions, 1.3 Training models by using AutoML

Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 4: Low-code ML Solutions, Section 4.3: AutoML

BigQuery

Vertex AI

Dataflow

TFRecord

CSV

Vertex AI Workbench

Pandas

Question #373

Your task is classify if a company logo is present on an image. You found out that 96% of a data does not include a logo. You are dealing with data imbalance problem.

Which metric do you use to evaluate to model?

A . F1 Score
B . RMSE
C . F Score with higher precision weighting than recall
D . F Score with higher recall weighted than precision

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Correct Answer: A
A

Explanation:

The F1 score is a metric that combines both precision and recall, and is suitable for evaluating imbalanced classification problems. Precision measures the fraction of true positives among the predicted positives, and recall measures the fraction of true positives among the actual positives. The F1 score is the harmonic mean of precision and recall, and it ranges from 0 to 1, with higher values indicating better performance. The F1 score is a good metric for imbalanced data because it balances both the false positives and the false negatives, and does not favor the majority class over the minority class.

The other options are not good metrics for imbalanced data. RMSE (root mean squared error) is a metric for regression problems, not classification problems. It measures the average squared difference between the predicted and the actual values, and is not suitable for binary outcomes. F score with higher precision weighting than recall, or F0.5 score, is a metric that gives more importance to precision than recall. This means that it penalizes false positives more than false negatives, which is not desirable for imbalanced data where the minority class is more important. F score with higher recall weighting than precision, or F2 score, is a metric that gives more importance to recall than precision. This means that it penalizes false negatives more than false positives, which might be suitable for some imbalanced data problems, but not for the logo detection problem. In this problem, both false positives and false negatives are equally important, as we want to accurately identify the presence or absence of a logo in an image. Therefore, the F1 score is a better metric than the F2 score.

Reference: Tour of Evaluation Metrics for Imbalanced Classification

Metrics for imbalanced data (simply explained)

Question #374

The model will be stored in a Cloud Storage bucket and will be updated frequently

You want to minimize prediction latency and the effort required to update the model.

How should you reconfigure the architecture?

A . Write a Cloud Function that loads the model into memory for prediction Configure the function to be triggered when messages are sent to Pub/Sub.
B . Create a pipeline in Vertex Al Pipelines that performs preprocessing, prediction and postprocessing Configure the pipeline to be triggered by a Cloud Function when messages are sent to Pub/Sub.
C . Expose the model as a Vertex Al endpoint Write a custom DoFn in a Dataflow job that calls the endpoint for prediction.
D . Use the Runlnference API with watchFilePatterr. in a Dataflow job that wraps around the model and serves predictions.

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Correct Answer: D
D

Explanation:

According to the web search results, RunInference API1 is a feature of Apache Beam that enables you to run models as part of your pipeline in a way that is optimized for machine learning inference. RunInference API supports features like batching, caching, and model reloading. RunInference API can be used with various frameworks, such as TensorFlow, PyTorch, Sklearn, XGBoost, ONNX, and TensorRT1. Dataflow2 is a fully managed service for running Apache Beam pipelines on Google Cloud. Dataflow handles the provisioning and management of the compute resources, as well as the optimization and execution of the pipelines. Therefore, option D is the best way to reconfigure the architecture for the given use case, as it allows you to use the RunInference API with watchFilePattern in a Dataflow job that wraps around the model and serves predictions. This way, you can minimize prediction latency and the effort required to update the model, as the RunInference API will automatically reload the model from the Cloud Storage bucket whenever there is a change in the model file1. The other options are not relevant or optimal for this scenario.

Reference: RunInference API

Dataflow

Google Professional Machine Learning Certification Exam 2023

Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

Question #374

A . Write a Cloud Function that loads the model into memory for prediction Configure the function to be triggered when messages are sent to Pub/Sub.
B . Create a pipeline in Vertex Al Pipelines that performs preprocessing, prediction and postprocessing Configure the pipeline to be triggered by a Cloud Function when messages are sent to Pub/Sub.
C . Expose the model as a Vertex Al endpoint Write a custom DoFn in a Dataflow job that calls the endpoint for prediction.
D . Use the Runlnference API with watchFilePatterr. in a Dataflow job that wraps around the model and serves predictions.

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Question #376

You are an ML engineer at an ecommerce company and have been tasked with building a model that predicts how much inventory the logistics team should order each month.

Which approach should you take?

A . Use a clustering algorithm to group popular items together. Give the list to the logistics team so they can increase inventory of the popular items.
B . Use a regression model to predict how much additional inventory should be purchased each month. Give the results to the logistics team at the beginning of the month so they can increase inventory by the amount predicted by the model.
C . Use a time series forecasting model to predict each item’s monthly sales. Give the results to the logistics team so they can base inventory on the amount predicted by the model.
D . Use a classification model to classify inventory levels as UNDER_STOCKED, OVER_STOCKED, and CORRECTLY_STOCKED. Give the report to the logistics team each month so they can fine-tune inventory levels.

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Correct Answer: C
C

Explanation:

The best approach to build a model that predicts how much inventory the logistics team should order each month is to use a time series forecasting model to predict each item’s monthly sales. This approach can capture the temporal patterns and trends in the sales data, such as seasonality, cyclicality, and autocorrelation. It can also account for the variability and uncertainty in the demand, and provide confidence intervals and error metrics for the predictions. By using a time series forecasting model, you can provide the logistics team with accurate and reliable estimates of the future sales for each item, which can help them optimize the inventory levels and avoid overstocking or understocking. You can use various methods and tools to build a time series forecasting model, such as ARIMA, LSTM, Prophet, or BigQuery ML.

The other options are not optimal for the following reasons:

Question #377

You have developed an AutoML tabular classification model that identifies high-value customers who interact with your organization’s website.

You plan to deploy the model to a new Vertex Al endpoint that will integrate with your website application. You expect higher traffic to the website during nights and weekends. You need to configure the model endpoint’s deployment settings to minimize latency and cost.

What should you do?

A . Configure the model deployment settings to use an n1-standard-32 machine type.
B . Configure the model deployment settings to use an n1-standard-4 machine type. Set the minReplicaCount value to 1 and the maxReplicaCount value to 8.
C . Configure the model deployment settings to use an n1-standard-4 machine type and a GPU accelerator. Set the minReplicaCount value to 1 and the maxReplicaCount value to 4.
D . Configure the model deployment settings to use an n1-standard-8 machine type and a GPU accelerator.

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Correct Answer: B
B

Explanation:

Deploying a model to an endpoint in Vertex AI associates physical resources with the model so it can serve online predictions with low latency1. By configuring the model deployment settings to use an n1-standard-4 machine type and setting the minReplicaCount value to 1 and the maxReplicaCount value to 8, you can ensure that the model scales according to the traffic, thereby minimizing latency and cost1. The n1-standard-4 machine type provides a balance between computing power and cost, and the dynamic scaling allows the model to handle higher traffic during nights and weekends without incurring unnecessary costs during off-peak times

Question #378

You are an ML engineer at a global car manufacturer. 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 . Three individual features binned latitude, binned longitude, and one-hot encoded car type
B . One feature obtained as an element-wise product between latitude, longitude, and car type
C . One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type
D . Two feature crosses as a 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

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Correct Answer: C
C

Explanation:

A feature cross is a synthetic feature that is obtained by combining two or more existing features, usually by taking their product or concatenation. A feature cross can help to capture the nonlinear and interaction effects between the original features, and improve the predictive performance of the model. A feature cross can be applied to different types of features, such as numeric, categorical, or geospatial features1.

For the use case of building an ML model to predict car sales in different cities around the world, the best option is to use one feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type. This option involves creating a feature cross that combines three individual features: binned latitude, binned longitude, and one-hot encoded car type. Binning is a technique that transforms a continuous numeric feature into a discrete categorical feature by dividing its range into equal intervals, or bins. One-hot encoding is a technique that transforms a categorical feature into a binary vector, where each element corresponds to a possible category, and has a value of 1 if the feature belongs to that category, and 0 otherwise. By applying binning and one-hot encoding to the latitude, longitude, and car type features, the feature cross can capture the city-specific relationships between car type and number of sales, as each combination of bins and car types can represent a different city and its preference for a certain car type. For example, the feature cross can learn that a city with a latitude bin of [40, 50], a longitude bin of [-80, -70], and a car type of SUV has a higher number of sales than a city with a latitude bin of [-10, 0], a longitude bin of [10, 20], and a car type of sedan. Therefore, using one feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type is the best option for this use case.

Reference: Feature Crosses | Machine Learning Crash Course

Question #379

You work on a team that builds state-of-the-art deep learning models by using the TensorFlow framework. Your team runs multiple ML experiments each week which makes it difficult to track the experiment runs. You want a simple approach to effectively track, visualize and debug ML experiment runs on Google Cloud while minimizing any overhead code.

How should you proceed?

A . Set up Vertex Al Experiments to track metrics and parameters Configure Vertex Al TensorBoard for visualization.
B . Set up a Cloud Function to write and save metrics files to a Cloud Storage Bucket Configure a Google Cloud VM to host TensorBoard locally for visualization.
C . Set up a Vertex Al Workbench notebook instance Use the instance to save metrics data in a Cloud Storage bucket and to host TensorBoard locally for visualization.
D . Set up a Cloud Function to write and save metrics files to a BigQuery table. Configure a Google Cloud VM to host TensorBoard locally for visualization.

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Correct Answer: A
A

Explanation:

Vertex AI Experiments is a service that allows you to track, compare, and optimize your ML experiments on Google Cloud. You can use Vertex AI Experiments to log metrics and parameters from your TensorFlow models, and then visualize them in Vertex AI TensorBoard. Vertex AI TensorBoard is a managed service that provides a web interface for viewing and debugging your ML experiments. You can use Vertex AI TensorBoard to compare different runs, inspect model graphs, analyze scalars, histograms, images, and more. By using Vertex AI Experiments and Vertex AI TensorBoard, you can simplify your ML experiment tracking and visualization workflow, and avoid the overhead of setting up and maintaining your own Cloud Functions, Cloud Storage buckets, or VMs.

Reference: [Vertex AI Experiments documentation]

[Vertex AI TensorBoard documentation]

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

Question #380

If you receive an alert investigate the cause.

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Correct Answer: C

Explanation:

Vertex AI Endpoint is a service that allows you to serve your ML models online and scale them automatically. You can use Vertex AI Endpoint to deploy the custom ML model that you developed for recommending recipes to the users. You can maintain the same machine type on the endpoint, which is a single machine with 8 vCPUs and no accelerators. This machine type can optimize the costs by using the queries per second (QPS) that the model can serve. You can also configure the endpoint to enable autoscaling based on vCPU usage. Autoscaling is a feature that allows the endpoint to adjust the number of compute nodes based on the traffic demand. By enabling autoscaling based on vCPU usage, you can ensure that the endpoint can scale efficiently to the increased demand during the holiday season, without overprovisioning or underprovisioning the resources. You can also set up a monitoring job and an alert for CPU usage. Monitoring is a service that allows you to collect and analyze the metrics and logs from your Google Cloud resources. You can use Monitoring to monitor the CPU usage of your endpoint, which is an indicator of the load and performance of your model. You can also set up an alert for CPU usage, which is a feature that allows you to receive notifications when the CPU usage exceeds a certain threshold. By setting up a monitoring job and an alert for CPU usage, you can keep track of the health and status of your endpoint, and detect any issues or anomalies. If you receive an alert, you can investigate the cause by using the Monitoring dashboard, which provides a graphical interface for viewing and analyzing the metrics and logs from your endpoint. You can also use the Monitoring dashboard to troubleshoot and resolve the issues, such as adjusting the autoscaling parameters, optimizing the model, or updating the machine type. By using Vertex AI Endpoint, autoscaling, and Monitoring, you can ensure that the model can scale efficiently to the increased demand during the holiday season, and handle any issues or alerts that might arise.

Reference: [Vertex AI Endpoint documentation]

[Autoscaling documentation]

[Monitoring documentation]

[Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate]

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