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Question 1

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?

AUse 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.

BUse 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.

CConvert 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.

DImport 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 : A

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

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BUse 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.

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Question 2

You work for a rapidly growing social media company. Your team builds TensorFlow recommender models in an on-premises CPU cluster. The data contains billions of historical user events and 100 000 categorical features. You notice that as the data increases the model training time increases. You plan to move the models to Google Cloud You want to use the most scalable approach that also minimizes training time. What should you do?

ADeploy the training jobs by using TPU VMs with TPUv3 Pod slices, and use the TPUEmbedding API.

BDeploy the training jobs in an autoscaling Google Kubernetes Engine cluster with CPUs

CDeploy a matrix factorization model training job by using BigQuery ML.

DDeploy the training jobs by using Compute Engine instances with A100 GPUs and use the
t f. nn. embedding_lookup API.

Correct : A

TPU VMs with TPUv3 Pod slices are the most scalable and performant option for training large-scale recommender models on Google Cloud. TPUv3 Pods can provide up to 2048 cores and 32 TB of memory, and can process billions of examples and features in minutes. The TPUEmbedding API is designed to efficiently handle large-scale categorical features and embeddings, and can reduce the memory footprint and communication overhead of the model. The other options are either less scalable (B and C) or less efficient (D) for this use case.

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ADeploy the training jobs by using TPU VMs with TPUv3 Pod slices, and use the TPUEmbedding API.

BDeploy the training jobs in an autoscaling Google Kubernetes Engine cluster with CPUs

CDeploy a matrix factorization model training job by using BigQuery ML.

DDeploy the training jobs by using Compute Engine instances with A100 GPUs and use the
t f. nn. embedding_lookup API.

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Question 3

You are training and deploying updated versions of a regression model with tabular data by using Vertex Al Pipelines. Vertex Al Training Vertex Al Experiments and Vertex Al Endpoints. The model is deployed in a Vertex Al endpoint and your users call the model by using the Vertex Al endpoint. You want to receive an email when the feature data distribution changes significantly, so you can retrigger the training pipeline and deploy an updated version of your model What should you do?

AUse Vertex Al Model Monitoring Enable prediction drift monitoring on the endpoint. and specify a notification email.

BIn Cloud Logging, create a logs-based alert using the logs in the Vertex Al endpoint. Configure Cloud Logging to send an email when the alert is triggered.

CIn Cloud Monitoring create a logs-based metric and a threshold alert for the metric. Configure Cloud Monitoring to send an email when the alert is triggered.

DExport the container logs of the endpoint to BigQuery Create a Cloud Function to run a SQL query over the exported logs and send an email. Use Cloud Scheduler to trigger the Cloud Function.

Correct : A

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.Reference:

Vertex AI Model Monitoring

Monitoring prediction drift

Setting up alerts and notifications

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AUse Vertex Al Model Monitoring Enable prediction drift monitoring on the endpoint. and specify a notification email.

BIn Cloud Logging, create a logs-based alert using the logs in the Vertex Al endpoint. Configure Cloud Logging to send an email when the alert is triggered.

CIn Cloud Monitoring create a logs-based metric and a threshold alert for the metric. Configure Cloud Monitoring to send an email when the alert is triggered.

DExport the container logs of the endpoint to BigQuery Create a Cloud Function to run a SQL query over the exported logs and send an email. Use Cloud Scheduler to trigger the Cloud Function.

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Question 4

You have trained an XGBoost model that you plan to deploy on Vertex Al for online prediction. You are now uploading your model to Vertex Al Model Registry, and you need to configure the explanation method that will serve online prediction requests to be returned with minimal latency. You also want to be alerted when feature attributions of the model meaningfully change over time. What should you do?

A1 Specify sampled Shapley as the explanation method with a path count of 5.
2 Deploy the model to Vertex Al Endpoints.
3. Create a Model Monitoring job that uses prediction drift as the monitoring objective.

B1 Specify Integrated Gradients as the explanation method with a path count of 5.
2 Deploy the model to Vertex Al Endpoints.
3. Create a Model Monitoring job that uses prediction drift as the monitoring objective.

C1. Specify sampled Shapley as the explanation method with a path count of 50.
2. Deploy the model to Vertex Al Endpoints.
3. Create a Model Monitoring job that uses training-serving skew as the monitoring objective.

D1 Specify Integrated Gradients as the explanation method with a path count of 50.
2. Deploy the model to Vertex Al Endpoints.
3 Create a Model Monitoring job that uses training-serving skew as the monitoring objective.

Correct : A

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

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Question 5

You developed a Python module by using Keras to train a regression model. You developed two model architectures, linear regression and deep neural network (DNN). within the same module. You are using the -- raining_method argument to select one of the two methods, and you are using the Learning_rate-and num_hidden_layers arguments in the DNN. You plan to use Vertex Al's hypertuning service with a Budget to perform 100 trials. You want to identify the model architecture and hyperparameter values that minimize training loss and maximize model performance What should you do?

ARun one hypertuning job for 100 trials. Set num hidden_layers as a conditional hypetparameter based on its parent hyperparameter training_mothod. and set learning rate as a non-conditional hyperparameter

BRun two separate hypertuning jobs. a linear regression job for 50 trials, and a DNN job for 50 trials Compare their final performance on a
common validation set. and select the set of hyperparameters with the least training loss

CRun one hypertuning job for 100 trials Set num_hidden_layers and learning_rate as conditional hyperparameters based on their parent hyperparameter training method.

DRun one hypertuning job with training_method as the hyperparameter for 50 trials Select the architecture with the lowest training loss. and further hypertune It and its corresponding hyperparameters for 50 trials

Correct : C

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CRun one hypertuning job for 100 trials Set num_hidden_layers and learning_rate as conditional hyperparameters based on their parent hyperparameter training method.

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