A machine learning (ML) specialist needs to extract embedding vectors from a text series. The goal is to provide a ready-to-ingest feature space for a data scientist to develop downstream ML predictive models. The text consists of curated sentences in English. Many sentences use similar words but in different contexts. There are questions and answers among the sentences, and the embedding space must differentiate between them.
Which options can produce the required embedding vectors that capture word context and sequential QA information? (Choose two.)

1. A. Amazon SageMaker seq2seq algorithm
2. B. Amazon SageMaker BlazingText algorithm in Skip-gram mode
3. C. Amazon SageMaker Object2Vec algorithm
4. D. Amazon SageMaker BlazingText algorithm in continuous bag-of-words (CBOW) mode
5. E. Combination of the Amazon SageMaker BlazingText algorithm in Batch Skip-gram mode with a custom recurrent neural network (RNN)

Correct Answer: AC

A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1:10]

Considering the graph, what is a reasonable selection for the optimal choice of k?

1. A. 1
2. B. 4
3. C. 7
4. D. 10

Correct Answer: C

A company will use Amazon SageMaker to train and host a machine learning (ML) model for a marketing campaign. The majority of data is sensitive customer data. The data must be encrypted at rest. The company wants AWS to maintain the root of trust for the master keys and wants encryption key usage to be logged.
Which implementation will meet these requirements?

1. A. Use encryption keys that are stored in AWS Cloud HSM to encrypt the ML data volumes, and to encryptthe model artifacts and data in Amazon S3.
2. B. Use SageMaker built-in transient keys to encrypt the ML data volume
3. C. Enable default encryption for new Amazon Elastic Block Store (Amazon EBS) volumes.
4. D. Use customer managed keys in AWS Key Management Service (AWS KMS) to encrypt the ML data volumes, and to encrypt the model artifacts and data in Amazon S3.
5. E. Use AWS Security Token Service (AWS STS) to create temporary tokens to encrypt the ML storage volumes, and to encrypt the model artifacts and data in Amazon S3.

Correct Answer: C

A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data.
Which of the following methods should the Specialist consider using to correct this? (Select THREE.)

1. A. Decrease regularization.
2. B. Increase regularization.
3. C. Increase dropout.
4. D. Decrease dropout.
5. E. Increase feature combinations.
6. F. Decrease feature combinations.

Correct Answer: BCD

A company is building a line-counting application for use in a quick-service restaurant. The company wants to use video cameras pointed at the line of customers at a given register to measure how many people are in line and deliver notifications to managers if the line grows too long. The restaurant locations have limited bandwidth for connections to external services and cannot accommodate multiple video streams without impacting other operations.
Which solution should a machine learning specialist implement to meet these requirements?

1. A. Install cameras compatible with Amazon Kinesis Video Streams to stream the data to AWS over the restaurant's existing internet connectio
2. B. Write an AWS Lambda function to take an image and send it to Amazon Rekognition to count the number of faces in the imag
3. C. Send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.
4. D. Deploy AWS DeepLens cameras in the restaurant to capture vide
5. E. Enable Amazon Rekognition on the AWS DeepLens device, and use it to trigger a local AWS Lambda function when a person is recognize
6. F. Use the Lambda function to send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.
7. G. Build a custom model in Amazon SageMaker to recognize the number of people in an imag
8. H. Install cameras compatible with Amazon Kinesis Video Streams in the restauran
9. I. Write an AWS Lambda function to take an imag
10. J. Use the SageMaker endpoint to call the model to count peopl
11. K. Send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.
12. L. Build a custom model in Amazon SageMaker to recognize the number of people in an imag
13. M. Deploy AWS DeepLens cameras in the restauran
14. N. Deploy the model to the camera
15. O. Deploy an AWS Lambda function to the cameras to use the model to count people and send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.

Correct Answer: A