A financial services company wants to adopt Amazon SageMaker as its default data science environment. The company's data scientists run machine learning (ML) models on confidential financial dat a. The company is worried about data egress and wants an ML engineer to secure the environment.
Which mechanisms can the ML engineer use to control data egress from SageMaker? (Choose three.)

• A.Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink.
• B.Use SCPs to restrict access to SageMaker.
• C.Disable root access on the SageMaker notebook instances.
• D.Enable network isolation for training jobs and models.
• E.Restrict notebook presigned URLs to specific IPs used by the company.
• F.Protect data with encryption at rest and in transit. Use AWS Key Management Service (AWS KMS) to manage encryption keys.

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A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a machine learning specialist will build a binary classifier based on two features: age of account, denoted by x, and transaction month, denoted by y. The class distributions are illustrated in the provided figure. The positive class is portrayed in red, while the negative class is portrayed in black.

Which model would have the HIGHEST accuracy?

• A.Support vector machine (SVM) with a radial basis function kernel
• B.Decision tree
• C.Linear support vector machine (SVM)
• D.Single perceptron with a Tanh activation function

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A company is running an Amazon SageMaker training job that will access data stored in its Amazon S3 bucket A compliance policy requires that the data never be transmitted across the internet How should the company set up the job?

• A.Launch the notebook instances in a private subnet and access the data through a NAT gateway
• B.Launch the notebook instances in a public subnet and access the data through a NAT gateway
• C.Launch the notebook instances in a private subnet and access the data through an S3 VPC endpoint.
• D.Launch the notebook instances in a public subnet and access the data through the public S3 endpoint

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A Data Scientist is evaluating different binary classification models. A false positive result is 5 times more expensive (from a business perspective) than a false negative result.
The models should be evaluated based on the following criteria:
1) Must have a recall rate of at least 80%
2) Must have a false positive rate of 10% or less
3) Must minimize business costs
After creating each binary classification model, the Data Scientist generates the corresponding confusion matrix.
Which confusion matrix represents the model that satisfies the requirements?

• A.TN = 91, FP = 9
  FN = 22, TP = 78
• B.TN = 99, FP = 1
  FN = 21, TP = 79
• C.TN = 96, FP = 4
  FN = 10, TP = 90
• D.TN = 98, FP = 2
  FN = 18, TP = 82

Correct Answer: D

The following calculations are required:
TP = True Positive
FP = False Positive
FN = False Negative
TN = True Negative
FN = False Negative
Recall = TP / (TP + FN)
False Positive Rate (FPR) = FP / (FP + TN)
Cost = 5 * FP + FN

Options C and D have a recall greater than 80% and an FPR less than 10%, but D is the most cost effective.

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A bank's Machine Learning team is developing an approach for credit card fraud detection The company has a large dataset of historical data labeled as fraudulent The goal is to build a model to take the information from new transactions and predict whether each transaction is fraudulent or not Which built-in Amazon SageMaker machine learning algorithm should be used for modeling this problem?

• A.Random Cut Forest (RCF)
• B.XGBoost
• C.Seq2seq
• D.K-means

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