MLOps V2

You need to deploy a scikit-learn classification model to production. The model must be able to serve requests 24/7, and you expect millions of requests per second to the production application from 8 am to 7 pm. You need to minimize the cost of deployment. What should you do?

• Deploy an online Vertex AI prediction endpoint. Set the max replica count to 1.

• Deploy an online Vertex AI prediction endpoint. Set the max replica count to 100.

• Deploy an online Vertex AI prediction endpoint with one GPU per replica. Set the max replica count to 1.

• Deploy an online Vertex AI prediction endpoint with one GPU per replica. Set the max replica count to 100.

How should you set up your training environment?

• Configure a v3-8 TPU VM. SSH into the VM to train and debug the model.

• Configure a v3-8 TPU node. Use Cloud Shell to SSH into the Host VM to train and debug the model.

• Configure a n1 -standard-4 VM with 4 NVIDIA P100 GPUs. SSH into the VM and use ParameterServerStraregv to train the model.

• Configure a n1-standard-4 VM with 4 NVIDIA P100 GPUs. SSH into the VM and use MultiWorkerMirroredStrategy to train the model.

What should you do?

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What should you do?

• Stream prediction results to BigQuery. Use BigQuery’s CORR(X1, X2) function to calculate the Pearson correlation coefficient between each feature and the target variable.

• Use Vertex Explainable AI. Submit each prediction request with the explain' keyword to retrieve feature attributions using the sampled Shapley method.

• Use Vertex AI Workbench user-managed notebooks to perform a Lasso regression analysis on your model, which will eliminate features that do not provide a strong signal.

• Use the What-If tool in Google Cloud to determine how your model will perform when individual features are excluded. Rank the feature importance in order of those that caused the most significant performance drop when removed from the model.

What should you do?

• Create an AutoML tabular model by using the BigQuery data with integrated Vertex Explainable AI.

• Create a BigQuery ML deep neural network model and use the ML.EXPLAIN_PREDICT method with the num_integral_steps parameter.

• Upload the custom model to Vertex AI Model Registry and configure feature-based attribution by using sampled Shapley with input baselines.

• Update the custom serving container to include sampled Shapley-based explanations in the prediction outputs.

What should you do?

• Use FastAPI to implement an HTTP server. Create a Docker image that runs your HTTP server, and deploy it on your organization’s GKE cluster.

• Use FastAPI to implement an HTTP server. Create a Docker image that runs your HTTP server, Upload the image to Vertex AI Model Registry and deploy it to a Vertex AI endpoint.

• Use the Predictor interface to implement a custom prediction routine. Build the custom container, upload the container to Vertex AI Model Registry and deploy it to a Vertex AI endpoint.

• Use the XGBoost prebuilt serving container when importing the trained model into Vertex AI. Deploy the model to a Vertex AI endpoint. Work with the backend engineers to implement the pre- and postprocessing steps in the Golang backend service.

You received a training-serving skew alert from a Vertex AI Model Monitoring job running in production. You retrained the model with more recent training data, and deployed it back to the Vertex AI endpoint, but you are still receiving the same alert. What should you do?

• Update the model monitoring job to use a lower sampling rate.

• Update the model monitoring job to use the more recent training data that was used to retrain the model.

• Temporarily disable the alert. Enable the alert again after a sufficient amount of new production traffic has passed through the Vertex AI endpoint.

• Temporarily disable the alert until the model can be retrained again on newer training data. Retrain the model again after a sufficient amount of new production traffic has passed through the Vertex AI endpoint.

What should you do?

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You are deploying a new version of a model to a production Vertex Al endpoint that is serving traffic. You plan to direct all user traffic to the new model. You need to deploy the model with minimal disruption to your application. What should you do?

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What approach should you use to split the data?

• Use Vertex AI manual split, using the store name feature to assign one store for each set.

• Use Vertex AI default data split.

• Use Vertex AI chronological split, and specify the sales timestamp feature as the time variable.

• Use Vertex AI random split, assigning 70% of the rows to the training set, 10% to the validation set, and 20% to the test set.