Objective 4.1 Deploy a Model AIP-210 Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   When preparing the deployment environment for a machine learning model, which of the following is a key component to set up?

   • AA model training script
   • BCompute resources such as CPU or GPU instances
   • CA feature store for training data
   • DA data labeling tool
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2. 17foundation · easy

   What is the primary role of an API endpoint in a machine learning deployment?

   • ATo allow client applications to send requests and receive predictions
   • BTo visualize model performance metrics
   • CTo manage the model training process
   • DTo store the training data for the model
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4. 18application · medium

   A team is deploying a model that predicts loan default. The model expects input features in a specific order and scale. The API receives raw JSON from a web form. What should the serving logic do before passing the data to the model?

   • APass the raw JSON directly to the model
   • BPreprocess the data to match the model's expected input format and scaling
   • CConvert the JSON to a CSV file and load it
   • DTrain a new model that accepts raw JSON
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5. 19foundation · easy

   What is the main responsibility of the model serving logic in a production environment?

   • ARetrain the model periodically with new data
   • BLoad the trained model and process input data to generate predictions
   • CMonitor the model's accuracy on the training set
   • DStore the training data in a database
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6. 20foundation · easy

   Which of the following is a resource optimization technique for a deployed model?

   • AIncreasing the number of training epochs
   • BAdding more features to the input data
   • CUsing model quantization to reduce memory footprint
   • DStoring the model in a larger file format
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