A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.
Which architecture changes would ensure that provisioned resources are being utilized effectively?

• A.Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.
• B.Redeploy the model on a P3dn instance.
• C.Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.
• D.Redeploy the model as a batch transform job on an M5 instance.

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A Machine Learning Specialist is developing recommendation engine for a photography blog Given a picture, the recommendation engine should show a picture that captures similar objects The Specialist would like to create a numerical representation feature to perform nearest-neighbor searches What actions would allow the Specialist to get relevant numerical representations?

• A.Run images through a neural network pie-trained on ImageNet, and collect the feature vectors from the penultimate layer
• B.Reduce image resolution and use reduced resolution pixel values as features
• C.Use Amazon Mechanical Turk to label image content and create a one-hot representation indicating the presence of specific labels
• D.Average colors by channel to obtain three-dimensional representations of images.

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A company is building a demand forecasting model based on machine learning (ML). In the development stage, an ML specialist uses an Amazon SageMaker notebook to perform feature engineering during work hours that consumes low amounts of CPU and memory resources. A data engineer uses the same notebook to perform data preprocessing once a day on average that requires very high memory and completes in only 2 hours. The data preprocessing is not configured to use GPU. All the processes are running well on an ml.m5.4xlarge notebook instance.
The company receives an AWS Budgets alert that the billing for this month exceeds the allocated budget.
Which solution will result in the MOST cost savings?

• A.Change the notebook instance type to a memory optimized instance with the same vCPU number as the ml.m5.4xlarge instance has. Stop the notebook when it is not in use. Run both data preprocessing and feature engineering development on that instance.
• B.Change the notebook instance type to a smaller general purpose instance. Stop the notebook when it is not in use. Run data preprocessing on an R5 instance with the same memory size as the ml.m5.4xlarge instance by using the Reserved Instance option.
• C.Change the notebook instance type to a smaller general purpose instance. Stop the notebook when it is not in use. Run data preprocessing on an ml.r5 instance with the same memory size as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.
• D.Keep the notebook instance type and size the same. Stop the notebook when it is not in use. Run data preprocessing on a P3 instance type with the same memory as the ml.m5.4xlarge instance by using Amazon SageMaker Processing.

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A data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist.

How should the data scientist split the dataset into a training and test set for this use case?

• A.Shuffle all interaction data. Split off the last 10% of the interaction data for the test set.
• B.Identify the most recent 10% of interactions for each user. Split off these interactions for the test set.
• C.Identify the 10% of users with the least interaction data. Split off all interaction data from these users for the test set.
• D.Randomly select 10% of the users. Split off all interaction data from these users for the test set.

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A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (PII).
The dataset:
* Must be accessible from a VPC only.
* Must not traverse the public internet.
How can these requirements be satisfied?

• A.Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.
• B.Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.
• C.Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance
• D.Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.

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