AWS-Certified-Machine-Learning-Specialty Free Practice Test

A telecommunications company is developing a mobile app for its customers. The company is using an Amazon SageMaker hosted endpoint for machine learning model inferences.
Developers want to introduce a new version of the model for a limited number of users who subscribed to a preview feature of the app. After the new version of the model is tested as a preview, developers will evaluate its accuracy. If a new version of the model has better accuracy, developers need to be able to gradually release the new version for all users over a fixed period of time.
How can the company implement the testing model with the LEAST amount of operational overhead?

1. A. Update the ProductionVariant data type with the new version of the model by using the CreateEndpointConfig operation with the InitialVariantWeight parameter set to 0. Specify the TargetVariant parameter for InvokeEndpoint calls for users who subscribed to the preview featur
2. B. When the new version of the model is ready for release, gradually increase InitialVariantWeight until all users have the updated version.
3. C. Configure two SageMaker hosted endpoints that serve the different versions of the mode
4. D. Create an Application Load Balancer (ALB) to route traffic to both endpoints based on the TargetVariant query string paramete
5. E. Reconfigure the app to send the TargetVariant query string parameter for users who subscribed to the preview featur
6. F. When the new version of the model is ready for release, change the ALB's routing algorithm to weighted until all users have the updated version.
7. G. Update the DesiredWeightsAndCapacity data type with the new version of the model by using the UpdateEndpointWeightsAndCapacities operation with the DesiredWeight parameter set to 0. Specify the TargetVariant parameter for InvokeEndpoint calls for users who subscribed to the preview featur
8. H. When the new version of the model is ready for release, gradually increase DesiredWeight until all users have the updated version.
9. I. Configure two SageMaker hosted endpoints that serve the different versions of the mode
10. J. Create an Amazon Route 53 record that is configured with a simple routing policy and that points to the current version of the mode
11. K. Configure the mobile app to use the endpoint URL for users who subscribed to the preview feature and to use the Route 53 record for other user
12. L. When the new version of the model is ready for release, add a new model version endpoint to Route 53, and switch the policy to weighted until all users have the updated version.

Correct Answer: D

A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet.
How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?

1. A. Create a NAT gateway within the corporate VPC.
2. B. Route Amazon SageMaker traffic through an on-premises network.
3. C. Create Amazon SageMaker VPC interface endpoints within the corporate VPC.
4. D. Create VPC peering with Amazon VPC hosting Amazon SageMaker.

Correct Answer: A

An e-commerce company needs a customized training model to classify images of its shirts and pants products The company needs a proof of concept in 2 to 3 days with good accuracy Which compute choice should the Machine Learning Specialist select to train and achieve good accuracy on the model quickly?

1. A. m5 4xlarge (general purpose)
2. B. r5.2xlarge (memory optimized)
3. C. p3.2xlarge (GPU accelerated computing)
4. D. p3 8xlarge (GPU accelerated computing)

Correct Answer: C

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?

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

Correct Answer: B
https://aws.amazon.com/machine-learning/elastic-inference/

A company sells thousands of products on a public website and wants to automatically identify products with potential durability problems. The company has 1.000 reviews with date, star rating, review text, review summary, and customer email fields, but many reviews are incomplete and have empty fields. Each review has already been labeled with the correct durability result.
A machine learning specialist must train a model to identify reviews expressing concerns over product durability. The first model needs to be trained and ready to review in 2 days.
What is the MOST direct approach to solve this problem within 2 days?

1. A. Train a custom classifier by using Amazon Comprehend.
2. B. Build a recurrent neural network (RNN) in Amazon SageMaker by using Gluon and Apache MXNet.
3. C. Train a built-in BlazingText model using Word2Vec mode in Amazon SageMaker.
4. D. Use a built-in seq2seq model in Amazon SageMaker.

Correct Answer: B