AWS Certified Machine Learning - Specialty (MLS-C01): MLS-C01

An Amazon SageMaker notebook instance is launched into Amazon VPC The SageMaker notebook references data contained in an Amazon S3 bucket in another account The bucket is encrypted using SSE-KMS The instance returns an access denied error when trying to access data in Amazon S3.

Which of the following are required to access the bucket and avoid the access denied error? (Select THREE )

A. An AWS KMS key policy that allows access to the customer master key (CMK)

B. A SageMaker notebook security group that allows access to Amazon S3

C. An 1AM role that allows access to the specific S3 bucket

D. A permissive S3 bucket policy

E. An S3 bucket owner that matches the notebook owner

F. A SegaMaker notebook subnet ACL that allow traffic to Amazon S3.

A health care company is planning to use neural networks to classify their X-ray images into normal and abnormal classes. The labeled data is divided into a training set of 1,000 images and a test set of 200 images. The initial training of a neural network model with 50 hidden layers yielded 99% accuracy on the training set, but only 55% accuracy on the test set.

What changes should the Specialist consider to solve this issue? (Choose three.)

A. Choose a higher number of layers

B. Choose a lower number of layers

C. Choose a smaller learning rate

D. Enable dropout

E. Include all the images from the test set in the training set

F. Enable early stopping

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.)

A. Amazon SageMaker seq2seq algorithm

B. Amazon SageMaker BlazingText algorithm in Skip-gram mode

C. Amazon SageMaker Object2Vec algorithm

D. Amazon SageMaker BlazingText algorithm in continuous bag-of-words (CBOW) mode

E. Combination of the Amazon SageMaker BlazingText algorithm in Batch Skip-gram mode with a custom recurrent neural network (RNN)