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 #291
Create a Model Monitoring job that uses training-serving skew as the monitoring objective.
Correct Answer: A
Explanation:
Sampled Shapley is a fast and scalable approximation of the Shapley value, which is a game-theoretic concept that measures the contribution of each feature to the model prediction. Sampled Shapley is suitable for online prediction requests, as it can return feature attributions with minimal latency. The path count parameter controls the number of samples used to estimate the Shapley value, and a lower value means faster computation. Integrated Gradients is another explanation method that computes the average gradient along the path from a baseline input to the actual input. Integrated Gradients is more accurate than Sampled Shapley, but also more computationally intensive. Therefore, it is not recommended for online prediction requests, especially with a high path count. Prediction drift is the change in the distribution of feature values or labels over time. It can affect the performance and accuracy of the model, and may require retraining or redeploying the model.
Vertex AI Model Monitoring allows you to monitor prediction drift on your deployed models and endpoints, and set up alerts and notifications when the drift exceeds a certain threshold. You can specify an email address to receive the notifications, and use the information to retrigger the training pipeline and deploy an updated version of your model. This is the most direct and convenient way to achieve your goal. Training-serving skew is the difference between the data used for training the model and the data used for serving the model. It can also affect the performance and accuracy of the model, and may indicate data quality issues or model staleness. Vertex AI Model Monitoring allows you to monitor training-serving skew on your deployed models and endpoints, and set up alerts and notifications when the skew exceeds a certain threshold. However, this is not relevant to the question, as the question is about the feature attributions of the model, not the data distribution.
Reference: Vertex AI: Explanation methods
Vertex AI: Configuring explanations
Vertex AI: Monitoring prediction drift
Vertex AI: Monitoring training-serving skew
Explanation:
Sampled Shapley is a fast and scalable approximation of the Shapley value, which is a game-theoretic concept that measures the contribution of each feature to the model prediction. Sampled Shapley is suitable for online prediction requests, as it can return feature attributions with minimal latency. The path count parameter controls the number of samples used to estimate the Shapley value, and a lower value means faster computation. Integrated Gradients is another explanation method that computes the average gradient along the path from a baseline input to the actual input. Integrated Gradients is more accurate than Sampled Shapley, but also more computationally intensive. Therefore, it is not recommended for online prediction requests, especially with a high path count. Prediction drift is the change in the distribution of feature values or labels over time. It can affect the performance and accuracy of the model, and may require retraining or redeploying the model.
Vertex AI Model Monitoring allows you to monitor prediction drift on your deployed models and endpoints, and set up alerts and notifications when the drift exceeds a certain threshold. You can specify an email address to receive the notifications, and use the information to retrigger the training pipeline and deploy an updated version of your model. This is the most direct and convenient way to achieve your goal. Training-serving skew is the difference between the data used for training the model and the data used for serving the model. It can also affect the performance and accuracy of the model, and may indicate data quality issues or model staleness. Vertex AI Model Monitoring allows you to monitor training-serving skew on your deployed models and endpoints, and set up alerts and notifications when the skew exceeds a certain threshold. However, this is not relevant to the question, as the question is about the feature attributions of the model, not the data distribution.
Reference: Vertex AI: Explanation methods
Vertex AI: Configuring explanations
Vertex AI: Monitoring prediction drift
Vertex AI: Monitoring training-serving skew
Question #292
You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories.
What should you do?
- A . Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run
- B . Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.
- C . Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories Configure a Pub/Sub trigger for Cloud Run, and execute the unit tests on Cloud Run.
- D . Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic
Correct Answer: B
B
Explanation:
Cloud Build is a service that executes your builds on Google Cloud Platform infrastructure. Cloud Build can import source code from Cloud Source Repositories, Cloud Storage, GitHub, or Bitbucket, execute a build to your specifications, and produce artifacts such as Docker containers or Java archives1 Cloud Build allows you to set up automated triggers that start a build when changes are pushed to a source code repository. You can configure triggers to filter the changes based on the branch, tag, or file path2
To automate the execution of unit tests for a Kubeflow Pipeline that require custom libraries, you can use Cloud Build to set an automated trigger to execute the unit tests when changes are pushed to your development branch in Cloud Source Repositories. You can specify the steps of the build in a YAML or JSON file, such as installing the custom libraries, running the unit tests, and reporting the results. You can also use Cloud Build to build and deploy the Kubeflow Pipeline components if the unit tests pass3. The other options are not recommended or feasible. Writing a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run is not a good practice, as it does not leverage the benefits of Cloud Build and its integration with Cloud Source Repositories. Setting up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories and using a Pub/Sub trigger for Cloud Run or Cloud Function to execute the unit tests is unnecessarily complex and inefficient, as it adds extra steps and latency to the process. Cloud Run and Cloud Function are also not designed for executing unit tests, as they have limitations on the memory, CPU, and execution time45
Reference: 1: Cloud Build overview 2: Creating and managing build triggers 3: Building and deploying Kubeflow Pipelines using Cloud Build 4: Cloud Run documentation 5: Cloud Functions documentation
B
Explanation:
Cloud Build is a service that executes your builds on Google Cloud Platform infrastructure. Cloud Build can import source code from Cloud Source Repositories, Cloud Storage, GitHub, or Bitbucket, execute a build to your specifications, and produce artifacts such as Docker containers or Java archives1 Cloud Build allows you to set up automated triggers that start a build when changes are pushed to a source code repository. You can configure triggers to filter the changes based on the branch, tag, or file path2
To automate the execution of unit tests for a Kubeflow Pipeline that require custom libraries, you can use Cloud Build to set an automated trigger to execute the unit tests when changes are pushed to your development branch in Cloud Source Repositories. You can specify the steps of the build in a YAML or JSON file, such as installing the custom libraries, running the unit tests, and reporting the results. You can also use Cloud Build to build and deploy the Kubeflow Pipeline components if the unit tests pass3. The other options are not recommended or feasible. Writing a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run is not a good practice, as it does not leverage the benefits of Cloud Build and its integration with Cloud Source Repositories. Setting up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories and using a Pub/Sub trigger for Cloud Run or Cloud Function to execute the unit tests is unnecessarily complex and inefficient, as it adds extra steps and latency to the process. Cloud Run and Cloud Function are also not designed for executing unit tests, as they have limitations on the memory, CPU, and execution time45
Reference: 1: Cloud Build overview 2: Creating and managing build triggers 3: Building and deploying Kubeflow Pipelines using Cloud Build 4: Cloud Run documentation 5: Cloud Functions documentation
Question #293
You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent.
Which data transformation strategy would likely improve the performance of your classifier?
- A . Write your data in TFRecords.
- B . Z-normalize all the numeric features.
- C . Oversample the fraudulent transaction 10 times.
- D . Use one-hot encoding on all categorical features.
Correct Answer: C
C
Explanation:
Oversampling is a technique for dealing with imbalanced datasets, where the majority class dominates the minority class. It balances the distribution of classes by increasing the number of samples in the minority class. Oversampling can improve the performance of a classifier by reducing the bias towards the majority class and increasing the sensitivity to the minority class.
In this case, the dataset includes transactions, of which 1% are identified as fraudulent. This means that the fraudulent transactions are the minority class and the non-fraudulent transactions are the majority class. A random forest model trained on this dataset might have a low recall for the fraudulent transactions, meaning that it might miss many of them and fail to detect fraud. This could have a high cost for the bank and its customers.
One way to overcome this problem is to oversample the fraudulent transactions 10 times, meaning that each fraudulent transaction is duplicated 10 times in the training dataset. This would increase the proportion of fraudulent transactions from 1% to about 10%, making the dataset more balanced. This would also make the random forest model more aware of the patterns and features that distinguish fraudulent transactions from non-fraudulent ones, and thus improve its accuracy and recall for the minority class.
For more information about oversampling and other techniques for imbalanced data, see the following references:
Random Oversampling and Undersampling for Imbalanced Classification Exploring Oversampling Techniques for Imbalanced Datasets
C
Explanation:
Oversampling is a technique for dealing with imbalanced datasets, where the majority class dominates the minority class. It balances the distribution of classes by increasing the number of samples in the minority class. Oversampling can improve the performance of a classifier by reducing the bias towards the majority class and increasing the sensitivity to the minority class.
In this case, the dataset includes transactions, of which 1% are identified as fraudulent. This means that the fraudulent transactions are the minority class and the non-fraudulent transactions are the majority class. A random forest model trained on this dataset might have a low recall for the fraudulent transactions, meaning that it might miss many of them and fail to detect fraud. This could have a high cost for the bank and its customers.
One way to overcome this problem is to oversample the fraudulent transactions 10 times, meaning that each fraudulent transaction is duplicated 10 times in the training dataset. This would increase the proportion of fraudulent transactions from 1% to about 10%, making the dataset more balanced. This would also make the random forest model more aware of the patterns and features that distinguish fraudulent transactions from non-fraudulent ones, and thus improve its accuracy and recall for the minority class.
For more information about oversampling and other techniques for imbalanced data, see the following references:
Random Oversampling and Undersampling for Imbalanced Classification Exploring Oversampling Techniques for Imbalanced Datasets
Question #293
You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent.
Which data transformation strategy would likely improve the performance of your classifier?
- A . Write your data in TFRecords.
- B . Z-normalize all the numeric features.
- C . Oversample the fraudulent transaction 10 times.
- D . Use one-hot encoding on all categorical features.
Correct Answer: C
C
Explanation:
Oversampling is a technique for dealing with imbalanced datasets, where the majority class dominates the minority class. It balances the distribution of classes by increasing the number of samples in the minority class. Oversampling can improve the performance of a classifier by reducing the bias towards the majority class and increasing the sensitivity to the minority class.
In this case, the dataset includes transactions, of which 1% are identified as fraudulent. This means that the fraudulent transactions are the minority class and the non-fraudulent transactions are the majority class. A random forest model trained on this dataset might have a low recall for the fraudulent transactions, meaning that it might miss many of them and fail to detect fraud. This could have a high cost for the bank and its customers.
One way to overcome this problem is to oversample the fraudulent transactions 10 times, meaning that each fraudulent transaction is duplicated 10 times in the training dataset. This would increase the proportion of fraudulent transactions from 1% to about 10%, making the dataset more balanced. This would also make the random forest model more aware of the patterns and features that distinguish fraudulent transactions from non-fraudulent ones, and thus improve its accuracy and recall for the minority class.
For more information about oversampling and other techniques for imbalanced data, see the following references:
Random Oversampling and Undersampling for Imbalanced Classification Exploring Oversampling Techniques for Imbalanced Datasets
C
Explanation:
Oversampling is a technique for dealing with imbalanced datasets, where the majority class dominates the minority class. It balances the distribution of classes by increasing the number of samples in the minority class. Oversampling can improve the performance of a classifier by reducing the bias towards the majority class and increasing the sensitivity to the minority class.
In this case, the dataset includes transactions, of which 1% are identified as fraudulent. This means that the fraudulent transactions are the minority class and the non-fraudulent transactions are the majority class. A random forest model trained on this dataset might have a low recall for the fraudulent transactions, meaning that it might miss many of them and fail to detect fraud. This could have a high cost for the bank and its customers.
One way to overcome this problem is to oversample the fraudulent transactions 10 times, meaning that each fraudulent transaction is duplicated 10 times in the training dataset. This would increase the proportion of fraudulent transactions from 1% to about 10%, making the dataset more balanced. This would also make the random forest model more aware of the patterns and features that distinguish fraudulent transactions from non-fraudulent ones, and thus improve its accuracy and recall for the minority class.
For more information about oversampling and other techniques for imbalanced data, see the following references:
Random Oversampling and Undersampling for Imbalanced Classification Exploring Oversampling Techniques for Imbalanced Datasets
Question #293
You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent.
Which data transformation strategy would likely improve the performance of your classifier?
- A . Write your data in TFRecords.
- B . Z-normalize all the numeric features.
- C . Oversample the fraudulent transaction 10 times.
- D . Use one-hot encoding on all categorical features.
Correct Answer: C
C
Explanation:
Oversampling is a technique for dealing with imbalanced datasets, where the majority class dominates the minority class. It balances the distribution of classes by increasing the number of samples in the minority class. Oversampling can improve the performance of a classifier by reducing the bias towards the majority class and increasing the sensitivity to the minority class.
In this case, the dataset includes transactions, of which 1% are identified as fraudulent. This means that the fraudulent transactions are the minority class and the non-fraudulent transactions are the majority class. A random forest model trained on this dataset might have a low recall for the fraudulent transactions, meaning that it might miss many of them and fail to detect fraud. This could have a high cost for the bank and its customers.
One way to overcome this problem is to oversample the fraudulent transactions 10 times, meaning that each fraudulent transaction is duplicated 10 times in the training dataset. This would increase the proportion of fraudulent transactions from 1% to about 10%, making the dataset more balanced. This would also make the random forest model more aware of the patterns and features that distinguish fraudulent transactions from non-fraudulent ones, and thus improve its accuracy and recall for the minority class.
For more information about oversampling and other techniques for imbalanced data, see the following references:
Random Oversampling and Undersampling for Imbalanced Classification Exploring Oversampling Techniques for Imbalanced Datasets
C
Explanation:
Oversampling is a technique for dealing with imbalanced datasets, where the majority class dominates the minority class. It balances the distribution of classes by increasing the number of samples in the minority class. Oversampling can improve the performance of a classifier by reducing the bias towards the majority class and increasing the sensitivity to the minority class.
In this case, the dataset includes transactions, of which 1% are identified as fraudulent. This means that the fraudulent transactions are the minority class and the non-fraudulent transactions are the majority class. A random forest model trained on this dataset might have a low recall for the fraudulent transactions, meaning that it might miss many of them and fail to detect fraud. This could have a high cost for the bank and its customers.
One way to overcome this problem is to oversample the fraudulent transactions 10 times, meaning that each fraudulent transaction is duplicated 10 times in the training dataset. This would increase the proportion of fraudulent transactions from 1% to about 10%, making the dataset more balanced. This would also make the random forest model more aware of the patterns and features that distinguish fraudulent transactions from non-fraudulent ones, and thus improve its accuracy and recall for the minority class.
For more information about oversampling and other techniques for imbalanced data, see the following references:
Random Oversampling and Undersampling for Imbalanced Classification Exploring Oversampling Techniques for Imbalanced Datasets
Question #296
You work for a semiconductor manufacturing company. You need to create a real-time application that automates the quality control process High-definition images of each semiconductor are taken at the end of the assembly line in real time. The photos are uploaded to a Cloud Storage bucket along with tabular data that includes each semiconductor’s batch number serial number dimensions, and weight You need to configure model training and serving while maximizing model accuracy.
What should you do?
- A . Use Vertex Al Data Labeling Service to label the images and train an AutoML image classification model.
Deploy the model and configure Pub/Sub to publish a message when an image is categorized into the failing class. - B . Use Vertex Al Data Labeling Service to label the images and train an AutoML image classification model. Schedule a daily batch prediction job that publishes a Pub/Sub message when the job completes.
- C . Convert the images into an embedding representation Import this data into BigQuery, and train a BigQuery. ML K-means clustenng model with two clusters Deploy the model and configure Pub/Sub to publish a message when a semiconductor’s data is categorized into the failing cluster.
- D . Import the tabular data into BigQuery use Vertex Al Data Labeling Service to label the data and train an AutoML tabular classification model Deploy the model and configure Pub/Sub to publish a message when a semiconductor’s data is categorized into the failing class.
Correct Answer: A
A
Explanation:
Vertex AI is a unified platform for building and managing machine learning solutions on Google Cloud. It provides various services and tools for different stages of the machine learning lifecycle, such as data preparation, model training, deployment, monitoring, and experimentation. Vertex AI Data Labeling Service is a service that allows you to create and manage human-labeled datasets for machine learning. You can use Vertex AI Data Labeling Service to label the images of semiconductors with binary labels, such as “pass” or “fail”, based on the quality criteria. You can also use Vertex AI AutoML Image Classification, which is a service that allows you to create and train custom image classification models without writing any code. You can use Vertex AI AutoML Image Classification to train an image classification model on the labeled images of semiconductors, and optimize the model for accuracy. You can also use Vertex AI to deploy the model to an endpoint, which is a service that allows you to serve online predictions from your model. You can configure Pub/Sub, which is a service that allows you to publish and subscribe to messages, to publish a message when an image is categorized into the failing class by the model. You can use the message to trigger an action, such as alerting the quality control team or stopping the production line. This solution can help you create a real-time application that automates the quality control process of semiconductors, and maximizes the model accuracy.
Reference: The answer can be verified from official Google Cloud documentation and resources related to Vertex AI, Vertex AI Data Labeling Service, Vertex AI AutoML Image Classification, and Pub/Sub.
Vertex AI | Google Cloud
Vertex AI Data Labeling Service | Google Cloud
Vertex AI AutoML Image Classification | Google Cloud
Pub/Sub | Google Cloud
A
Explanation:
Vertex AI is a unified platform for building and managing machine learning solutions on Google Cloud. It provides various services and tools for different stages of the machine learning lifecycle, such as data preparation, model training, deployment, monitoring, and experimentation. Vertex AI Data Labeling Service is a service that allows you to create and manage human-labeled datasets for machine learning. You can use Vertex AI Data Labeling Service to label the images of semiconductors with binary labels, such as “pass” or “fail”, based on the quality criteria. You can also use Vertex AI AutoML Image Classification, which is a service that allows you to create and train custom image classification models without writing any code. You can use Vertex AI AutoML Image Classification to train an image classification model on the labeled images of semiconductors, and optimize the model for accuracy. You can also use Vertex AI to deploy the model to an endpoint, which is a service that allows you to serve online predictions from your model. You can configure Pub/Sub, which is a service that allows you to publish and subscribe to messages, to publish a message when an image is categorized into the failing class by the model. You can use the message to trigger an action, such as alerting the quality control team or stopping the production line. This solution can help you create a real-time application that automates the quality control process of semiconductors, and maximizes the model accuracy.
Reference: The answer can be verified from official Google Cloud documentation and resources related to Vertex AI, Vertex AI Data Labeling Service, Vertex AI AutoML Image Classification, and Pub/Sub.
Vertex AI | Google Cloud
Vertex AI Data Labeling Service | Google Cloud
Vertex AI AutoML Image Classification | Google Cloud
Pub/Sub | Google Cloud
Question #296
You work for a semiconductor manufacturing company. You need to create a real-time application that automates the quality control process High-definition images of each semiconductor are taken at the end of the assembly line in real time. The photos are uploaded to a Cloud Storage bucket along with tabular data that includes each semiconductor’s batch number serial number dimensions, and weight You need to configure model training and serving while maximizing model accuracy.
What should you do?
- A . Use Vertex Al Data Labeling Service to label the images and train an AutoML image classification model.
Deploy the model and configure Pub/Sub to publish a message when an image is categorized into the failing class. - B . Use Vertex Al Data Labeling Service to label the images and train an AutoML image classification model. Schedule a daily batch prediction job that publishes a Pub/Sub message when the job completes.
- C . Convert the images into an embedding representation Import this data into BigQuery, and train a BigQuery. ML K-means clustenng model with two clusters Deploy the model and configure Pub/Sub to publish a message when a semiconductor’s data is categorized into the failing cluster.
- D . Import the tabular data into BigQuery use Vertex Al Data Labeling Service to label the data and train an AutoML tabular classification model Deploy the model and configure Pub/Sub to publish a message when a semiconductor’s data is categorized into the failing class.
Correct Answer: A
A
Explanation:
Vertex AI is a unified platform for building and managing machine learning solutions on Google Cloud. It provides various services and tools for different stages of the machine learning lifecycle, such as data preparation, model training, deployment, monitoring, and experimentation. Vertex AI Data Labeling Service is a service that allows you to create and manage human-labeled datasets for machine learning. You can use Vertex AI Data Labeling Service to label the images of semiconductors with binary labels, such as “pass” or “fail”, based on the quality criteria. You can also use Vertex AI AutoML Image Classification, which is a service that allows you to create and train custom image classification models without writing any code. You can use Vertex AI AutoML Image Classification to train an image classification model on the labeled images of semiconductors, and optimize the model for accuracy. You can also use Vertex AI to deploy the model to an endpoint, which is a service that allows you to serve online predictions from your model. You can configure Pub/Sub, which is a service that allows you to publish and subscribe to messages, to publish a message when an image is categorized into the failing class by the model. You can use the message to trigger an action, such as alerting the quality control team or stopping the production line. This solution can help you create a real-time application that automates the quality control process of semiconductors, and maximizes the model accuracy.
Reference: The answer can be verified from official Google Cloud documentation and resources related to Vertex AI, Vertex AI Data Labeling Service, Vertex AI AutoML Image Classification, and Pub/Sub.
Vertex AI | Google Cloud
Vertex AI Data Labeling Service | Google Cloud
Vertex AI AutoML Image Classification | Google Cloud
Pub/Sub | Google Cloud
A
Explanation:
Vertex AI is a unified platform for building and managing machine learning solutions on Google Cloud. It provides various services and tools for different stages of the machine learning lifecycle, such as data preparation, model training, deployment, monitoring, and experimentation. Vertex AI Data Labeling Service is a service that allows you to create and manage human-labeled datasets for machine learning. You can use Vertex AI Data Labeling Service to label the images of semiconductors with binary labels, such as “pass” or “fail”, based on the quality criteria. You can also use Vertex AI AutoML Image Classification, which is a service that allows you to create and train custom image classification models without writing any code. You can use Vertex AI AutoML Image Classification to train an image classification model on the labeled images of semiconductors, and optimize the model for accuracy. You can also use Vertex AI to deploy the model to an endpoint, which is a service that allows you to serve online predictions from your model. You can configure Pub/Sub, which is a service that allows you to publish and subscribe to messages, to publish a message when an image is categorized into the failing class by the model. You can use the message to trigger an action, such as alerting the quality control team or stopping the production line. This solution can help you create a real-time application that automates the quality control process of semiconductors, and maximizes the model accuracy.
Reference: The answer can be verified from official Google Cloud documentation and resources related to Vertex AI, Vertex AI Data Labeling Service, Vertex AI AutoML Image Classification, and Pub/Sub.
Vertex AI | Google Cloud
Vertex AI Data Labeling Service | Google Cloud
Vertex AI AutoML Image Classification | Google Cloud
Pub/Sub | Google Cloud
Question #296
You work for a semiconductor manufacturing company. You need to create a real-time application that automates the quality control process High-definition images of each semiconductor are taken at the end of the assembly line in real time. The photos are uploaded to a Cloud Storage bucket along with tabular data that includes each semiconductor’s batch number serial number dimensions, and weight You need to configure model training and serving while maximizing model accuracy.
What should you do?
- A . Use Vertex Al Data Labeling Service to label the images and train an AutoML image classification model.
Deploy the model and configure Pub/Sub to publish a message when an image is categorized into the failing class. - B . Use Vertex Al Data Labeling Service to label the images and train an AutoML image classification model. Schedule a daily batch prediction job that publishes a Pub/Sub message when the job completes.
- C . Convert the images into an embedding representation Import this data into BigQuery, and train a BigQuery. ML K-means clustenng model with two clusters Deploy the model and configure Pub/Sub to publish a message when a semiconductor’s data is categorized into the failing cluster.
- D . Import the tabular data into BigQuery use Vertex Al Data Labeling Service to label the data and train an AutoML tabular classification model Deploy the model and configure Pub/Sub to publish a message when a semiconductor’s data is categorized into the failing class.
Correct Answer: A
A
Explanation:
Vertex AI is a unified platform for building and managing machine learning solutions on Google Cloud. It provides various services and tools for different stages of the machine learning lifecycle, such as data preparation, model training, deployment, monitoring, and experimentation. Vertex AI Data Labeling Service is a service that allows you to create and manage human-labeled datasets for machine learning. You can use Vertex AI Data Labeling Service to label the images of semiconductors with binary labels, such as “pass” or “fail”, based on the quality criteria. You can also use Vertex AI AutoML Image Classification, which is a service that allows you to create and train custom image classification models without writing any code. You can use Vertex AI AutoML Image Classification to train an image classification model on the labeled images of semiconductors, and optimize the model for accuracy. You can also use Vertex AI to deploy the model to an endpoint, which is a service that allows you to serve online predictions from your model. You can configure Pub/Sub, which is a service that allows you to publish and subscribe to messages, to publish a message when an image is categorized into the failing class by the model. You can use the message to trigger an action, such as alerting the quality control team or stopping the production line. This solution can help you create a real-time application that automates the quality control process of semiconductors, and maximizes the model accuracy.
Reference: The answer can be verified from official Google Cloud documentation and resources related to Vertex AI, Vertex AI Data Labeling Service, Vertex AI AutoML Image Classification, and Pub/Sub.
Vertex AI | Google Cloud
Vertex AI Data Labeling Service | Google Cloud
Vertex AI AutoML Image Classification | Google Cloud
Pub/Sub | Google Cloud
A
Explanation:
Vertex AI is a unified platform for building and managing machine learning solutions on Google Cloud. It provides various services and tools for different stages of the machine learning lifecycle, such as data preparation, model training, deployment, monitoring, and experimentation. Vertex AI Data Labeling Service is a service that allows you to create and manage human-labeled datasets for machine learning. You can use Vertex AI Data Labeling Service to label the images of semiconductors with binary labels, such as “pass” or “fail”, based on the quality criteria. You can also use Vertex AI AutoML Image Classification, which is a service that allows you to create and train custom image classification models without writing any code. You can use Vertex AI AutoML Image Classification to train an image classification model on the labeled images of semiconductors, and optimize the model for accuracy. You can also use Vertex AI to deploy the model to an endpoint, which is a service that allows you to serve online predictions from your model. You can configure Pub/Sub, which is a service that allows you to publish and subscribe to messages, to publish a message when an image is categorized into the failing class by the model. You can use the message to trigger an action, such as alerting the quality control team or stopping the production line. This solution can help you create a real-time application that automates the quality control process of semiconductors, and maximizes the model accuracy.
Reference: The answer can be verified from official Google Cloud documentation and resources related to Vertex AI, Vertex AI Data Labeling Service, Vertex AI AutoML Image Classification, and Pub/Sub.
Vertex AI | Google Cloud
Vertex AI Data Labeling Service | Google Cloud
Vertex AI AutoML Image Classification | Google Cloud
Pub/Sub | Google Cloud
Question #299
You work on a growing team of more than 50 data scientists who all use AI Platform. You are designing a strategy to organize your jobs, models, and versions in a clean and scalable way.
Which strategy should you choose?
- A . Set up restrictive IAM permissions on the AI Platform notebooks so that only a single user or group can access a given instance.
- B . Separate each data scientist’s work into a different project to ensure that the jobs, models, and versions created by each data scientist are accessible only to that user.
- C . Use labels to organize resources into descriptive categories. Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources.
- D . Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about AI Platform resource usage. In BigQuery, create a SQL view that maps users to the resources they are using
Correct Answer: C
C
Explanation:
Labels are key-value pairs that you can attach to AI Platform resources such as jobs, models, and versions. Labels can help you organize your resources into descriptive categories that reflect your business needs. For example, you can use labels to indicate the owner, purpose, environment, or status of a resource. You can also use labels to filter the results when you list or monitor your resources on the Google Cloud Console or the Cloud SDK. Using labels can help you manage your resources in a clean and scalable way, without requiring separate projects or restrictive permissions.
Reference: Using labels to organize AI Platform resources
Creating and managing labels
C
Explanation:
Labels are key-value pairs that you can attach to AI Platform resources such as jobs, models, and versions. Labels can help you organize your resources into descriptive categories that reflect your business needs. For example, you can use labels to indicate the owner, purpose, environment, or status of a resource. You can also use labels to filter the results when you list or monitor your resources on the Google Cloud Console or the Cloud SDK. Using labels can help you manage your resources in a clean and scalable way, without requiring separate projects or restrictive permissions.
Reference: Using labels to organize AI Platform resources
Creating and managing labels
Question #300
You work for a toy manufacturer that has been experiencing a large increase in demand. You need to build an ML model to reduce the amount of time spent by quality control inspectors checking for product defects. Faster defect detection is a priority. The factory does not have reliable Wi-Fi. Your company wants to implement the new ML model as soon as possible.
Which model should you use?
- A . AutoML Vision model
- B . AutoML Vision Edge mobile-versatile-1 model
- C . AutoML Vision Edge mobile-low-latency-1 model
- D . AutoML Vision Edge mobile-high-accuracy-1 model
Correct Answer: C
C
Explanation:
AutoML Vision Edge is a service that allows you to create custom image classification and object detection models that can run on edge devices, such as mobile phones, tablets, or IoT devices1. AutoML Vision Edge offers four types of models that vary in size, accuracy, and latency:
mobile-versatile-1, mobile-low-latency-1, mobile-high-accuracy-1, and mobile-core-ml-low-latency-12. Each model has its own trade-offs and use cases, depending on the device specifications and the application requirements.
For the use case of building an ML model to reduce the amount of time spent by quality control inspectors checking for product defects, the best model to use is the AutoML Vision Edge mobile-low-latency-1 model. This model is optimized for fast inference on mobile devices, with a latency of less than 50 milliseconds on a Pixel 1 phone2. Faster defect detection is a priority for the toy manufacturer, and the factory does not have reliable Wi-Fi, so a low-latency model that can run on the device without internet connection is ideal. The mobile-low-latency-1 model also has a small size of less than 4 MB, which makes it easy to deploy and update2. The mobile-low-latency-1 model has a slightly lower accuracy than the mobile-high-accuracy-1 model, but it is still suitable for most image classification tasks2. Therefore, the AutoML Vision Edge mobile-low-latency-1 model is the best option for this use case.
Reference: AutoML Vision Edge documentation
AutoML Vision Edge model types
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Explanation:
AutoML Vision Edge is a service that allows you to create custom image classification and object detection models that can run on edge devices, such as mobile phones, tablets, or IoT devices1. AutoML Vision Edge offers four types of models that vary in size, accuracy, and latency:
mobile-versatile-1, mobile-low-latency-1, mobile-high-accuracy-1, and mobile-core-ml-low-latency-12. Each model has its own trade-offs and use cases, depending on the device specifications and the application requirements.
For the use case of building an ML model to reduce the amount of time spent by quality control inspectors checking for product defects, the best model to use is the AutoML Vision Edge mobile-low-latency-1 model. This model is optimized for fast inference on mobile devices, with a latency of less than 50 milliseconds on a Pixel 1 phone2. Faster defect detection is a priority for the toy manufacturer, and the factory does not have reliable Wi-Fi, so a low-latency model that can run on the device without internet connection is ideal. The mobile-low-latency-1 model also has a small size of less than 4 MB, which makes it easy to deploy and update2. The mobile-low-latency-1 model has a slightly lower accuracy than the mobile-high-accuracy-1 model, but it is still suitable for most image classification tasks2. Therefore, the AutoML Vision Edge mobile-low-latency-1 model is the best option for this use case.
Reference: AutoML Vision Edge documentation
AutoML Vision Edge model types