Demonstrate the Ability to Train and Inference CERTIFIED-AI-EXPERT Practice Questions (Page 2)

Questions 6–10

1. 6application · medium

   A model that predicts house prices is deployed. For a new house listing, the model outputs a value of 0.75. What does this output likely represent?

   • AThe predicted price after applying the inverse of the preprocessing scaling used during training.
   • BThe probability that the house will sell within a month.
   • CAn error, because the model should output a whole number.
   • DThe raw price in dollars, so the house is predicted to cost $0.75.
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2. 7application · medium

   A model that classifies customer support tickets has been deployed. When a new ticket arrives, the system must predict the category. Which of the following is the correct sequence of steps in the inference process?

   • APass the raw ticket text directly to the model and read the output as a category.
   • BRetrain the model on the new ticket before predicting its category.
   • CCompare the ticket to all previous tickets and use the most common category.
   • DPreprocess the ticket text (e.g., tokenization), pass it through the model, and map the output to a category label.
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4. 8foundation · easy

   During the iterative training phase of an AI model, what is the primary purpose of evaluating the model on a validation set after each epoch?

   • ATo replace the test set for final reporting
   • BTo monitor performance on unseen data and guide adjustments
   • CTo increase the size of the training dataset
   • DTo permanently freeze the model's weights
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5. 9application · medium

   A data science team has trained a deep learning model on a large GPU cluster, spending hours on iterative training runs. Now they need to deploy the model to serve real-time predictions for a customer-facing application. Which of the following best describes the primary shift in computational requirements and latency expectations during this transition?

   • ATraining requires high-throughput batch processing with relaxed latency, while inference requires low-latency, high-availability serving with optimized model execution.
   • BInference requires more computational power than training because it must handle many concurrent requests, while training is a one-time process.
   • CTraining and inference both require the same computational resources and latency targets, so the deployment is simply a matter of copying the model files to a server.
   • DTraining and inference have identical data handling, but inference requires more storage because it must retain all training data for each prediction.
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6. 10application · medium

   A data scientist is training a model and notices that the training loss decreases but the validation loss increases after a certain number of epochs. Which of the following actions is most appropriate in the iterative training workflow?

   • AContinue training for more epochs to allow the model to converge further.
   • BStop training early and use the model from the epoch with the lowest validation loss.
   • CIncrease the learning rate to escape the local minimum.
   • DReduce the size of the training dataset to reduce overfitting.
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