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An ML team trained a model using SageMaker and stored the model artifacts in S3 with server-side encryption using AWS KMS (SSE-KMS). They need to deploy the model to a SageMaker endpoint that uses a different KMS key for inference data encryption. What must they do to ensure the endpoint can decrypt the model artifacts?
The SageMaker endpoint needs to decrypt the model artifacts stored with SSE-KMS using the original KMS key, and then re-encrypt the inference data with a different KMS key. The SageMaker execution role must have kms:Decrypt permission on the key used for the model artifacts and k…Read full explanation
Refer to the exhibit. A data scientist reviews the CloudWatch Logs from an Amazon SageMaker real-time endpoint. What is the MOST likely root cause of the NaN output?
The NaN (Not a Number) output from a SageMaker real-time endpoint is most commonly caused by input data containing values outside the range seen during training. This can lead to numerical instability in the model's forward pass, such as division by zero, log of zero, or exponent…Read full explanation
A data scientist trained a logistic regression model on a dataset with 100 features. After training, the training accuracy is 0.99 but validation accuracy is 0.75. Which action is MOST likely to reduce overfitting?
The model shows high training accuracy (0.99) but significantly lower validation accuracy (0.75), which is a classic sign of overfitting. Increasing the regularization strength (e.g., L1 or L2 penalty) in logistic regression directly penalizes large coefficients, reducing the mod…Read full explanation
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