You have deployed a model on Vertex AI for real-time inference. During an online prediction request, you get an "Out of Memory" error. What should you do?

• A.Use batch prediction mode instead of online mode.
• B.Send the request again with a smaller batch of instances.
• C.Use base64 to encode your data before using it for prediction.
• D.Apply for a quota increase for the number of prediction requests.

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You recently designed and built a custom neural network that uses critical dependencies specific to your organization's framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by Al Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure. What should you do?

• A.Use a built-in model available on Al Platform Training
• B.Build your custom container to run jobs on Al Platform Training
• C.Build your custom containers to run distributed training jobs on Al Platform Training
• D.Reconfigure your code to a ML framework with dependencies that are supported by Al Platform Training

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

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

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You work for an auto insurance company. You are preparing a proof-of-concept ML application that uses images of damaged vehicles to infer damaged parts Your team has assembled a set of annotated images from damage claim documents in the company's database The annotations associated with each image consist of a bounding box for each identified damaged part and the part name. You have been given a sufficient budget to tram models on Google Cloud You need to quickly create an initial model What should you do?

• A.Download a pre-trained object detection mode! from TensorFlow Hub Fine-tune the model in Vertex Al Workbench by using the annotated image data.
• B.Train an object detection model in AutoML by using the annotated image data.
• C.Create a pipeline in Vertex Al Pipelines and configure the AutoMLTrainingJobRunOp compon it to train a custom object detection model by using the annotated image data.
• D.Train an object detection model in Vertex Al custom training by using the annotated image data.

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You are developing a model to predict whether a failure will occur in a critical machine part. You have a dataset consisting of a multivariate time series and labels indicating whether the machine part failed You recently started experimenting with a few different preprocessing and modeling approaches in a Vertex Al Workbench notebook. You want to log data and track artifacts from each run. How should you set up your experiments?

• A.
• B.
• C.
• D.

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