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
Question #339
You need to design a customized deep neural network in Keras that will predict customer purchases based on their purchase history. You want to explore model performance using multiple model architectures, store training data, and be able to compare the evaluation metrics in the same dashboard.
What should you do?
- A . Create multiple models using AutoML Tables
- B . Automate multiple training runs using Cloud Composer
- C . Run multiple training jobs on Al Platform with similar job names
- D . Create an experiment in Kubeflow Pipelines to organize multiple runs
Correct Answer: D
D
Explanation:
Kubeflow Pipelines is a service that allows you to create and run machine learning workflows on Google Cloud using various features, model architectures, and hyperparameters. You can use Kubeflow Pipelines to scale up your workflows, leverage distributed training, and access specialized hardware such as GPUs and TPUs1. An experiment in Kubeflow Pipelines is a workspace where you can try different configurations of your pipelines and organize your runs into logical groups. You can use experiments to compare the performance of different models and track the evaluation metrics in the same dashboard2.
For the use case of designing a customized deep neural network in Keras that will predict customer purchases based on their purchase history, the best option is to create an experiment in Kubeflow Pipelines to organize multiple runs. This option allows you to explore model performance using multiple model architectures, store training data, and compare the evaluation metrics in the same dashboard. You can use Keras to build and train your deep neural network models, and then package them as pipeline components that can be reused and combined with other components. You can also use Kubeflow Pipelines SDK to define and submit your pipelines programmatically, and use Kubeflow Pipelines UI to monitor and manage your experiments. Therefore, creating an experiment in Kubeflow Pipelines to organize multiple runs is the best option for this use case.
Reference: Kubeflow Pipelines documentation
Experiment | Kubeflow
D
Explanation:
Kubeflow Pipelines is a service that allows you to create and run machine learning workflows on Google Cloud using various features, model architectures, and hyperparameters. You can use Kubeflow Pipelines to scale up your workflows, leverage distributed training, and access specialized hardware such as GPUs and TPUs1. An experiment in Kubeflow Pipelines is a workspace where you can try different configurations of your pipelines and organize your runs into logical groups. You can use experiments to compare the performance of different models and track the evaluation metrics in the same dashboard2.
For the use case of designing a customized deep neural network in Keras that will predict customer purchases based on their purchase history, the best option is to create an experiment in Kubeflow Pipelines to organize multiple runs. This option allows you to explore model performance using multiple model architectures, store training data, and compare the evaluation metrics in the same dashboard. You can use Keras to build and train your deep neural network models, and then package them as pipeline components that can be reused and combined with other components. You can also use Kubeflow Pipelines SDK to define and submit your pipelines programmatically, and use Kubeflow Pipelines UI to monitor and manage your experiments. Therefore, creating an experiment in Kubeflow Pipelines to organize multiple runs is the best option for this use case.
Reference: Kubeflow Pipelines documentation
Experiment | Kubeflow
Question #339
You need to design a customized deep neural network in Keras that will predict customer purchases based on their purchase history. You want to explore model performance using multiple model architectures, store training data, and be able to compare the evaluation metrics in the same dashboard.
What should you do?
- A . Create multiple models using AutoML Tables
- B . Automate multiple training runs using Cloud Composer
- C . Run multiple training jobs on Al Platform with similar job names
- D . Create an experiment in Kubeflow Pipelines to organize multiple runs
Correct Answer: D
D
Explanation:
Kubeflow Pipelines is a service that allows you to create and run machine learning workflows on Google Cloud using various features, model architectures, and hyperparameters. You can use Kubeflow Pipelines to scale up your workflows, leverage distributed training, and access specialized hardware such as GPUs and TPUs1. An experiment in Kubeflow Pipelines is a workspace where you can try different configurations of your pipelines and organize your runs into logical groups. You can use experiments to compare the performance of different models and track the evaluation metrics in the same dashboard2.
For the use case of designing a customized deep neural network in Keras that will predict customer purchases based on their purchase history, the best option is to create an experiment in Kubeflow Pipelines to organize multiple runs. This option allows you to explore model performance using multiple model architectures, store training data, and compare the evaluation metrics in the same dashboard. You can use Keras to build and train your deep neural network models, and then package them as pipeline components that can be reused and combined with other components. You can also use Kubeflow Pipelines SDK to define and submit your pipelines programmatically, and use Kubeflow Pipelines UI to monitor and manage your experiments. Therefore, creating an experiment in Kubeflow Pipelines to organize multiple runs is the best option for this use case.
Reference: Kubeflow Pipelines documentation
Experiment | Kubeflow
D
Explanation:
Kubeflow Pipelines is a service that allows you to create and run machine learning workflows on Google Cloud using various features, model architectures, and hyperparameters. You can use Kubeflow Pipelines to scale up your workflows, leverage distributed training, and access specialized hardware such as GPUs and TPUs1. An experiment in Kubeflow Pipelines is a workspace where you can try different configurations of your pipelines and organize your runs into logical groups. You can use experiments to compare the performance of different models and track the evaluation metrics in the same dashboard2.
For the use case of designing a customized deep neural network in Keras that will predict customer purchases based on their purchase history, the best option is to create an experiment in Kubeflow Pipelines to organize multiple runs. This option allows you to explore model performance using multiple model architectures, store training data, and compare the evaluation metrics in the same dashboard. You can use Keras to build and train your deep neural network models, and then package them as pipeline components that can be reused and combined with other components. You can also use Kubeflow Pipelines SDK to define and submit your pipelines programmatically, and use Kubeflow Pipelines UI to monitor and manage your experiments. Therefore, creating an experiment in Kubeflow Pipelines to organize multiple runs is the best option for this use case.
Reference: Kubeflow Pipelines documentation
Experiment | Kubeflow
Question #343
You work at a bank. You need to develop a credit risk model to support loan application decisions You decide to implement the model by using a neural network in TensorFlow Due to regulatory requirements, you need to be able to explain the models predictions based on its features When the model is deployed, you also want to monitor the model’s performance overtime. You decided to use Vertex Al for both model development and deployment.
What should you do?
- A . Use Vertex Explainable Al with the sampled Shapley method, and enable Vertex Al Model Monitoring to
check for feature distribution drift. - B . Use Vertex Explainable Al with the sampled Shapley method, and enable Vertex Al Model Monitoring to
check for feature distribution skew. - C . Use Vertex Explainable Al with the XRAI method, and enable Vertex Al Model Monitoring to check for feature distribution drift.
- D . Use Vertex Explainable Al with the XRAI method and enable Vertex Al Model Monitoring to check for feature distribution skew.
Correct Answer: A
A
Explanation:
To develop a credit risk model that meets the regulatory requirements and can be monitored over time, you should follow these steps:
Use Vertex Explainable AI with the sampled Shapley method. Vertex Explainable AI is a service that provides feature attributions for machine learning models, which can help you understand how each feature contributes to the prediction1. The sampled Shapley method is a technique that estimates the Shapley values for each feature, which are based on the marginal contribution of each feature to the prediction across all possible feature subsets2. The sampled Shapley method is suitable for neural networks and other complex models, as it can capture the non-linear and interaction effects of the features3.
Enable Vertex AI Model Monitoring to check for feature distribution drift. Vertex AI Model Monitoring is a service that helps you track and manage the performance and quality of your deployed models over time4. Feature distribution drift is a type of data drift that occurs when the distribution of the input features changes significantly from the training data, which can affect the model accuracy and reliability. By checking for feature distribution drift, you can detect when your model needs to be retrained or updated with new data.
Reference: 1: Introduction to Vertex Explainable AI | Vertex AI | Google Cloud
2: Shapley value – Wikipedia
3: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
4: Introduction to Vertex AI Model Monitoring | Vertex AI | Google Cloud
[5]: Monitor models for data drift | Vertex AI | Google Cloud
A
Explanation:
To develop a credit risk model that meets the regulatory requirements and can be monitored over time, you should follow these steps:
Use Vertex Explainable AI with the sampled Shapley method. Vertex Explainable AI is a service that provides feature attributions for machine learning models, which can help you understand how each feature contributes to the prediction1. The sampled Shapley method is a technique that estimates the Shapley values for each feature, which are based on the marginal contribution of each feature to the prediction across all possible feature subsets2. The sampled Shapley method is suitable for neural networks and other complex models, as it can capture the non-linear and interaction effects of the features3.
Enable Vertex AI Model Monitoring to check for feature distribution drift. Vertex AI Model Monitoring is a service that helps you track and manage the performance and quality of your deployed models over time4. Feature distribution drift is a type of data drift that occurs when the distribution of the input features changes significantly from the training data, which can affect the model accuracy and reliability. By checking for feature distribution drift, you can detect when your model needs to be retrained or updated with new data.
Reference: 1: Introduction to Vertex Explainable AI | Vertex AI | Google Cloud
2: Shapley value – Wikipedia
3: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
4: Introduction to Vertex AI Model Monitoring | Vertex AI | Google Cloud
[5]: Monitor models for data drift | Vertex AI | Google Cloud
Question #343
You work at a bank. You need to develop a credit risk model to support loan application decisions You decide to implement the model by using a neural network in TensorFlow Due to regulatory requirements, you need to be able to explain the models predictions based on its features When the model is deployed, you also want to monitor the model’s performance overtime. You decided to use Vertex Al for both model development and deployment.
What should you do?
- A . Use Vertex Explainable Al with the sampled Shapley method, and enable Vertex Al Model Monitoring to
check for feature distribution drift. - B . Use Vertex Explainable Al with the sampled Shapley method, and enable Vertex Al Model Monitoring to
check for feature distribution skew. - C . Use Vertex Explainable Al with the XRAI method, and enable Vertex Al Model Monitoring to check for feature distribution drift.
- D . Use Vertex Explainable Al with the XRAI method and enable Vertex Al Model Monitoring to check for feature distribution skew.
Correct Answer: A
A
Explanation:
To develop a credit risk model that meets the regulatory requirements and can be monitored over time, you should follow these steps:
Use Vertex Explainable AI with the sampled Shapley method. Vertex Explainable AI is a service that provides feature attributions for machine learning models, which can help you understand how each feature contributes to the prediction1. The sampled Shapley method is a technique that estimates the Shapley values for each feature, which are based on the marginal contribution of each feature to the prediction across all possible feature subsets2. The sampled Shapley method is suitable for neural networks and other complex models, as it can capture the non-linear and interaction effects of the features3.
Enable Vertex AI Model Monitoring to check for feature distribution drift. Vertex AI Model Monitoring is a service that helps you track and manage the performance and quality of your deployed models over time4. Feature distribution drift is a type of data drift that occurs when the distribution of the input features changes significantly from the training data, which can affect the model accuracy and reliability. By checking for feature distribution drift, you can detect when your model needs to be retrained or updated with new data.
Reference: 1: Introduction to Vertex Explainable AI | Vertex AI | Google Cloud
2: Shapley value – Wikipedia
3: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
4: Introduction to Vertex AI Model Monitoring | Vertex AI | Google Cloud
[5]: Monitor models for data drift | Vertex AI | Google Cloud
A
Explanation:
To develop a credit risk model that meets the regulatory requirements and can be monitored over time, you should follow these steps:
Use Vertex Explainable AI with the sampled Shapley method. Vertex Explainable AI is a service that provides feature attributions for machine learning models, which can help you understand how each feature contributes to the prediction1. The sampled Shapley method is a technique that estimates the Shapley values for each feature, which are based on the marginal contribution of each feature to the prediction across all possible feature subsets2. The sampled Shapley method is suitable for neural networks and other complex models, as it can capture the non-linear and interaction effects of the features3.
Enable Vertex AI Model Monitoring to check for feature distribution drift. Vertex AI Model Monitoring is a service that helps you track and manage the performance and quality of your deployed models over time4. Feature distribution drift is a type of data drift that occurs when the distribution of the input features changes significantly from the training data, which can affect the model accuracy and reliability. By checking for feature distribution drift, you can detect when your model needs to be retrained or updated with new data.
Reference: 1: Introduction to Vertex Explainable AI | Vertex AI | Google Cloud
2: Shapley value – Wikipedia
3: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
4: Introduction to Vertex AI Model Monitoring | Vertex AI | Google Cloud
[5]: Monitor models for data drift | Vertex AI | Google Cloud
Question #345
You are an AI engineer working for a popular video streaming platform. You built a classification model using PyTorch to predict customer churn. Each week, the customer retention team plans to contact customers identified as at-risk for churning with personalized offers. You want to deploy the model while minimizing maintenance effort.
What should you do?
- A . Use Vertex AI’s prebuilt containers for prediction. Deploy the container on Cloud Run to generate online predictions.
- B . Use Vertex AI’s prebuilt containers for prediction. Deploy the model on Google Kubernetes Engine (GKE), and configure the model for batch prediction.
- C . Deploy the model to a Vertex AI endpoint, and configure the model for batch prediction. Schedule the batch prediction to run weekly.
- D . Deploy the model to a Vertex AI endpoint, and configure the model for online prediction. Schedule a job to query this endpoint weekly.
Correct Answer: C
C
Explanation:
Deploying the model on Vertex AI with a batch prediction configuration is ideal for weekly inference jobs since the retention team needs predictions once per week. Scheduling batch predictions minimizes computational costs, and Vertex AI’s endpoint management simplifies infrastructure setup without needing additional maintenance. Using Vertex AI’s prebuilt containers also provides a flexible deployment pipeline for any future model updates. Options A and D do not suit batch needs, and GKE (Option B) requires more manual maintenance.
C
Explanation:
Deploying the model on Vertex AI with a batch prediction configuration is ideal for weekly inference jobs since the retention team needs predictions once per week. Scheduling batch predictions minimizes computational costs, and Vertex AI’s endpoint management simplifies infrastructure setup without needing additional maintenance. Using Vertex AI’s prebuilt containers also provides a flexible deployment pipeline for any future model updates. Options A and D do not suit batch needs, and GKE (Option B) requires more manual maintenance.
Question #345
You are an AI engineer working for a popular video streaming platform. You built a classification model using PyTorch to predict customer churn. Each week, the customer retention team plans to contact customers identified as at-risk for churning with personalized offers. You want to deploy the model while minimizing maintenance effort.
What should you do?
- A . Use Vertex AI’s prebuilt containers for prediction. Deploy the container on Cloud Run to generate online predictions.
- B . Use Vertex AI’s prebuilt containers for prediction. Deploy the model on Google Kubernetes Engine (GKE), and configure the model for batch prediction.
- C . Deploy the model to a Vertex AI endpoint, and configure the model for batch prediction. Schedule the batch prediction to run weekly.
- D . Deploy the model to a Vertex AI endpoint, and configure the model for online prediction. Schedule a job to query this endpoint weekly.
Correct Answer: C
C
Explanation:
Deploying the model on Vertex AI with a batch prediction configuration is ideal for weekly inference jobs since the retention team needs predictions once per week. Scheduling batch predictions minimizes computational costs, and Vertex AI’s endpoint management simplifies infrastructure setup without needing additional maintenance. Using Vertex AI’s prebuilt containers also provides a flexible deployment pipeline for any future model updates. Options A and D do not suit batch needs, and GKE (Option B) requires more manual maintenance.
C
Explanation:
Deploying the model on Vertex AI with a batch prediction configuration is ideal for weekly inference jobs since the retention team needs predictions once per week. Scheduling batch predictions minimizes computational costs, and Vertex AI’s endpoint management simplifies infrastructure setup without needing additional maintenance. Using Vertex AI’s prebuilt containers also provides a flexible deployment pipeline for any future model updates. Options A and D do not suit batch needs, and GKE (Option B) requires more manual maintenance.
Question #347
You are the lead ML engineer on a mission-critical project that involves analyzing massive datasets using Apache Spark. You need to establish a robust environment that allows your team to rapidly prototype Spark models using Jupyter notebooks.
What is the fastest way to achieve this?
- A . Configure a Compute Engine instance with Spark and use Jupyter notebooks.
- B . Set up a Dataproc cluster with Spark and use Jupyter notebooks.
- C . Set up a Vertex AI Workbench instance with a Spark kernel.
- D . Use Colab Enterprise with a Spark kernel.
Correct Answer: B
B
Explanation:
Dataproc provides a managed Spark environment and integrates with Jupyter notebooks, ideal for large datasets and rapid prototyping. It reduces setup time compared to manual Spark configurations on Compute Engine or Vertex AI. Colab Enterprise is more suitable for small-scale prototyping rather than extensive Spark-based analysis.
B
Explanation:
Dataproc provides a managed Spark environment and integrates with Jupyter notebooks, ideal for large datasets and rapid prototyping. It reduces setup time compared to manual Spark configurations on Compute Engine or Vertex AI. Colab Enterprise is more suitable for small-scale prototyping rather than extensive Spark-based analysis.
Question #348
You are building a linear model with over 100 input features, all with values between -1 and 1. You suspect that many features are non-informative. You want to remove the non-informative features from your model while keeping the informative ones in their original form.
Which technique should you use?
- A . Use Principal Component Analysis to eliminate the least informative features.
- B . Use L1 regularization to reduce the coefficients of uninformative features to 0.
- C . After building your model, use Shapley values to determine which features are the most informative.
- D . Use an iterative dropout technique to identify which features do not degrade the model when removed.
Correct Answer: B
B
Explanation:
L1 regularization, also known as Lasso regularization, adds the sum of the absolute values of the model’s coefficients to the loss function1. It encourages sparsity in the model by shrinking some coefficients to precisely zero2. This way, L1 regularization can perform feature selection and remove the non-informative features from the model while keeping the informative ones in their original form. Therefore, using L1 regularization is the best technique for this use case.
Reference: Regularization in Machine Learning – GeeksforGeeks
Regularization in Machine Learning (with Code Examples) – Dataquest L1 And L2 Regularization Explained & Practical How To Examples L1 and L2 as Regularization for a Linear Model
B
Explanation:
L1 regularization, also known as Lasso regularization, adds the sum of the absolute values of the model’s coefficients to the loss function1. It encourages sparsity in the model by shrinking some coefficients to precisely zero2. This way, L1 regularization can perform feature selection and remove the non-informative features from the model while keeping the informative ones in their original form. Therefore, using L1 regularization is the best technique for this use case.
Reference: Regularization in Machine Learning – GeeksforGeeks
Regularization in Machine Learning (with Code Examples) – Dataquest L1 And L2 Regularization Explained & Practical How To Examples L1 and L2 as Regularization for a Linear Model
Question #348
You are building a linear model with over 100 input features, all with values between -1 and 1. You suspect that many features are non-informative. You want to remove the non-informative features from your model while keeping the informative ones in their original form.
Which technique should you use?
- A . Use Principal Component Analysis to eliminate the least informative features.
- B . Use L1 regularization to reduce the coefficients of uninformative features to 0.
- C . After building your model, use Shapley values to determine which features are the most informative.
- D . Use an iterative dropout technique to identify which features do not degrade the model when removed.
Correct Answer: B
B
Explanation:
L1 regularization, also known as Lasso regularization, adds the sum of the absolute values of the model’s coefficients to the loss function1. It encourages sparsity in the model by shrinking some coefficients to precisely zero2. This way, L1 regularization can perform feature selection and remove the non-informative features from the model while keeping the informative ones in their original form. Therefore, using L1 regularization is the best technique for this use case.
Reference: Regularization in Machine Learning – GeeksforGeeks
Regularization in Machine Learning (with Code Examples) – Dataquest L1 And L2 Regularization Explained & Practical How To Examples L1 and L2 as Regularization for a Linear Model
B
Explanation:
L1 regularization, also known as Lasso regularization, adds the sum of the absolute values of the model’s coefficients to the loss function1. It encourages sparsity in the model by shrinking some coefficients to precisely zero2. This way, L1 regularization can perform feature selection and remove the non-informative features from the model while keeping the informative ones in their original form. Therefore, using L1 regularization is the best technique for this use case.
Reference: Regularization in Machine Learning – GeeksforGeeks
Regularization in Machine Learning (with Code Examples) – Dataquest L1 And L2 Regularization Explained & Practical How To Examples L1 and L2 as Regularization for a Linear Model
Question #350
You are building an ML model to predict trends in the stock market based on a wide range of factors. While exploring the data, you notice that some features have a large range. You want to ensure that the features with the largest magnitude don’t overfit the model.
What should you do?
- A . Standardize the data by transforming it with a logarithmic function.
- B . Apply a principal component analysis (PCA) to minimize the effect of any particular feature.
- C . Use a binning strategy to replace the magnitude of each feature with the appropriate bin number.
- D . Normalize the data by scaling it to have values between 0 and 1.
Correct Answer: D
D
Explanation:
The best option to ensure that the features with the largest magnitude don’t overfit the model is to normalize the data by scaling it to have values between 0 and 1. This is also known as min-max scaling or feature scaling, and it can reduce the variance and skewness of the data, as well as improve the numerical stability and convergence of the model. Normalizing the data can also make the model less sensitive to the scale of the features, and more focused on the relative importance of each feature. Normalizing the data can be done using various methods, such as dividing each value by the maximum value, subtracting the minimum value and dividing by the range, or using the sklearn.preprocessing.MinMaxScaler function in Python.
The other options are not optimal for the following reasons:
D
Explanation:
The best option to ensure that the features with the largest magnitude don’t overfit the model is to normalize the data by scaling it to have values between 0 and 1. This is also known as min-max scaling or feature scaling, and it can reduce the variance and skewness of the data, as well as improve the numerical stability and convergence of the model. Normalizing the data can also make the model less sensitive to the scale of the features, and more focused on the relative importance of each feature. Normalizing the data can be done using various methods, such as dividing each value by the maximum value, subtracting the minimum value and dividing by the range, or using the sklearn.preprocessing.MinMaxScaler function in Python.
The other options are not optimal for the following reasons: