A web-based company wants to improve its conversion rate on its landing page Using a large historical dataset of customer visits, the company has repeatedly trained a multi-class deep learning network algorithm on Amazon SageMaker However there is an overfitting problem training data shows 90% accuracy in predictions, while test data shows 70% accuracy only The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases Which action is recommended to provide the HIGHEST accuracy model for the company's test and validation data?

• A.Increase the randomization of training data in the mini-batches used in training.
• B.Allocate a higher proportion of the overall data to the training dataset
• C.Apply L1 or L2 regularization and dropouts to the training.
• D.Reduce the number of layers and units (or neurons) from the deep learning network.

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A data scientist has been running an Amazon SageMaker notebook instance for a few weeks. During this time, a new version of Jupyter Notebook was released along with additional software updates. The security team mandates that all running SageMaker notebook instances use the latest security and software updates provided by SageMaker.
How can the data scientist meet this requirements?

• A.Call the UpdateNotebookInstanceLifecycleConfig API operation
• B.Create a new SageMaker notebook instance and mount the Amazon Elastic Block Store (Amazon EBS) volume from the original instance
• C.Stop and then restart the SageMaker notebook instance
• D.Call the CreateNotebookInstanceLifecycleConfig API operation

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A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.
What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?

• A.Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.
• B.Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.
• C.Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.
• D.Serialize the trained model so the format is compressed for deployment. Tag the Docker image with the registry hostname and upload it to Amazon S3.

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Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors. While exploring the data, the Specialist notices that the magnitude of the input features vary greatly. The Specialist does not want variables with a larger magnitude to dominate the model.
What should the Specialist do to prepare the data for model training?

• A.Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.
• B.Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.
• C.Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.
• D.Apply the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.

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Amazon Connect has recently been tolled out across a company as a contact call center The solution has been configured to store voice call recordings on Amazon S3 The content of the voice calls are being analyzed for the incidents being discussed by the call operators Amazon Transcribe is being used to convert the audio to text, and the output is stored on Amazon S3 Which approach will provide the information required for further analysis?

• A.Use the AWS Deep Learning AMI with Gluon Semantic Segmentation on the transcribed files to train and build a model for the key topics
• B.Use Amazon Comprehend with the transcribed files to build the key topics
• C.Use Amazon Translate with the transcribed files to train and build a model for the key topics
• D.Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the transcribed files to generate a word embeddings dictionary for the key topics

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