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NEW QUESTION 50
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. Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.C. Redeploy the model on a P3dn instance.D. Redeploy the model as a batch transform job on an M5 instance.

Answer: B

 

NEW QUESTION 51
You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

A. Three individual features binned latitude, binned longitude, and one-hot encoded car typeB. One feature obtained as an element-wise product between latitude, longitude, and car typeC. Two feature crosses as a element-wise product the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car typeD. One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type

Answer: D

Explanation:
https://developers.google.com/machine-learning/crash-course/feature-crosses/check-your-understanding
https://developers.google.com/machine-learning/crash-course/feature-crosses/video-lecture
https://developers.google.com/machine-learning/crash-course/feature-crosses/check-your-understanding

 

NEW QUESTION 52
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. 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.B. Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.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. Build the image and upload it to Docker Hub.

Answer: C

 

NEW QUESTION 53
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