You are building a TensorFlow model for a financial institution that predicts the impact of consumer spending on inflation globally. Due to the size and nature of the data, your model is long-running across all types of hardware, and you have built frequent checkpointing into the training process. Your organization has asked you to minimize cost. What hardware should you choose?

• A.A Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with 4 NVIDIA P100 GPUs
• B.A Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with an NVIDIA P100 GPU
• C.A Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with a non- preemptible v3-8 TPU
• D.A Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with a preemptible v3-8 TPU

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You have trained a model by using data that was preprocessed in a batch Dataflow pipeline Your use case requires real-time inference. You want to ensure that the data preprocessing logic is applied consistently between training and serving. What should you do?

• A.Batch the real-time requests by using a time window and then use the Dataflow pipeline to preprocess the batched requests. Send the preprocessed requests to the endpoint.
• B.Refactor the transformation code in the batch data pipeline so that it can be used outside of the pipeline Share this code with the end users of the endpoint.
• C.Perform data validation to ensure that the input data to the pipeline is the same format as the input data to the endpoint.
• D.Refactor the transformation code in the batch data pipeline so that it can be used outside of the pipeline Use the same code in the endpoint.

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You work at a mobile gaming startup that creates online multiplayer games Recently, your company observed an increase in players cheating in the games, leading to a loss of revenue and a poor user experience. You built a binary classification model to determine whether a player cheated after a completed game session, and then send a message to other downstream systems to ban the player that cheated Your model has performed well during testing, and you now need to deploy the model to production You want your serving solution to provide immediate classifications after a completed game session to avoid further loss of revenue. What should you do?

• A.Import the model into Vertex Al Model Registry. Use the Vertex Batch Prediction service to run batch inference jobs.
• B.Import the model into Vertex Al Model Registry Create a Vertex Al endpoint that hosts the model and make online inference requests.
• C.Save the model files in a VM Load the model files each time there is a prediction request and run an inference job on the VM.
• D.Save the model files in a Cloud Storage Bucket Create a Cloud Function to read the model files and make online inference requests on the Cloud Function.

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You have deployed multiple versions of an image classification model on AI Platform. You want to monitor the performance of the model versions over time. How should you perform this comparison?

• A.Compare the loss performance for each model on a held-out dataset.
• B.Compare the loss performance for each model on the validation data.
• C.Compare the receiver operating characteristic (ROC) curve for each model using the What-If Tool.
• D.Compare the mean average precision across the models using the Continuous Evaluation feature.

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You are experimenting with a built-in distributed XGBoost model in Vertex AI Workbench user-managed notebooks. You use BigQuery to split your data into training and validation sets using the following queries:
CREATE OR REPLACE TABLE 'myproject.mydataset.training' AS
(SELECT * FROM 'myproject.mydataset.mytable' WHERE RAND() <= 0.8);
CREATE OR REPLACE TABLE 'myproject.mydataset.validation' AS
(SELECT * FROM 'myproject.mydataset.mytable' WHERE RAND() <= 0.2);
After training the model, you achieve an area under the receiver operating characteristic curve (AUC ROC) value of 0.8, but after deploying the model to production, you notice that your model performance has dropped to an AUC ROC value of 0.65. What problem is most likely occurring?

• A.There is training-serving skew in your production environment.
• B.There is not a sufficient amount of training data.
• C.The tables that you created to hold your training and validation records share some records, and you may not be using all the data in your initial table.
• D.The RAND() function generated a number that is less than 0.2 in both instances, so every record in the validation table will also be in the training table.

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