A Machine Learning Specialist uploads a dataset to an Amazon S3 bucket protected with server- side encryption using AWS KMS.
How should the ML Specialist define the Amazon SageMaker notebook instance so it can read the same dataset from Amazon S3?

• A.Define security group(s) to allow all HTTP inbound/outbound traffic and assign those security group(s) to the Amazon SageMaker notebook instance.
• B.onfigure the Amazon SageMaker notebook instance to have access to the VPC. Grant permission in the KMS key policy to the notebook's KMS role.
• C.Assign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset.
  Grant permission in the KMS key policy to that role.
• D.Assign the same KMS key used to encrypt data in Amazon S3 to the Amazon SageMaker notebook instance.

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

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A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers Currently, the company has the following data in Amazon Aurora
* Profiles for all past and existing customers
* Profiles for all past and existing insured pets
* Policy-level information
* Premiums received
* Claims paid
What steps should be taken to implement a machine learning model to identify potential new customers on social media?

• A.Use regression on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.
• B.Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
• C.Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
• D.Use clustering on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.

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A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor, and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset.
Which tool should be used to improve the validation accuracy?

• A.Amazon Comprehend syntax analysis and entity detection
• B.Amazon SageMaker BlazingText cbowmode
• C.Natural Language Toolkit (NLTK) stemming and stop word removal
• D.Scikit-leam term frequency-inverse document frequency (TF-IDF) vectorizer

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A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.
The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:
Based on the model evaluation results, why is this a viable model for production?

• A.The precision of the model is 86%, which is less than the accuracy of the model.
• B.The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.
• C.The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.
• D.The precision of the model is 86%, which is greater than the accuracy of the model.

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