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Amazon Web Services Updated MLS-C01 Exam Questions and Answers by oisin

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Amazon Web Services MLS-C01 Exam Overview :

Exam Name: AWS Certified Machine Learning - Specialty
Exam Code: MLS-C01 Dumps
Vendor: Amazon Web Services Certification: AWS Certified Specialty
Questions: 281 Q&A's Shared By: oisin
Question 32

An office security agency conducted a successful pilot using 100 cameras installed at key locations within the main office. Images from the cameras were uploaded to Amazon S3 and tagged using Amazon Rekognition, and the results were stored in Amazon ES. The agency is now looking to expand the pilot into a full production system using thousands of video cameras in its office locations globally. The goal is to identify activities performed by non-employees in real time.

Which solution should the agency consider?

Options:

A.

Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique

Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Video and create

a stream processor to detect faces from a collection of known employees, and alert when non-employees

are detected.

B.

Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique

Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Image to detect

faces from a collection of known employees and alert when non-employees are detected.

C.

Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to

Amazon Kinesis Video Streams for each camera. On each stream, use Amazon Rekognition Video and

create a stream processor to detect faces from a collection on each stream, and alert when nonemployees

are detected.

D.

Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to

Amazon Kinesis Video Streams for each camera. On each stream, run an AWS Lambda function to

capture image fragments and then call Amazon Rekognition Image to detect faces from a collection of

known employees, and alert when non-employees are detected.

Discussion
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Question 33

During mini-batch training of a neural network for a classification problem, a Data Scientist notices that training accuracy oscillates What is the MOST likely cause of this issue?

Options:

A.

The class distribution in the dataset is imbalanced

B.

Dataset shuffling is disabled

C.

The batch size is too big

D.

The learning rate is very high

Discussion
Question 34

A wildlife research company has a set of images of lions and cheetahs. The company created a dataset of the images. The company labeled each image with a binary label that indicates whether an image contains a lion or cheetah. The company wants to train a model to identify whether new images contain a lion or cheetah.

.... Dh Amazon SageMaker algorithm will meet this requirement?

Options:

A.

XGBoost

B.

Image Classification - TensorFlow

C.

Object Detection - TensorFlow

D.

Semantic segmentation - MXNet

Discussion
Question 35

A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy

sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as

either a potential risk or no risk. The model is not performing well, even though the Data Scientist has

experimented with many different network structures and tuned the corresponding hyperparameters.

Which approach will provide the MAXIMUM performance boost?

Options:

A.

Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large

collection of news articles related to the energy sector.

B.

Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation loss

stops decreasing.

C.

Reduce the learning rate and run the training process until the training loss stops decreasing.

D.

Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the

energy sector.

Discussion
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