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Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Exam is a certification exam designed for individuals who want to demonstrate their expertise in machine learning on the AWS platform. AWS-Certified-Machine-Learning-Specialty exam is intended for professionals who have experience using AWS services for designing, building, and deploying machine learning solutions. AWS Certified Machine Learning - Specialty certification exam validates the candidate's ability to design, implement, and deploy machine learning models using AWS services.

The AWS-Certified-Machine-Learning-Specialty Exam is a challenging certification program that requires a comprehensive understanding of machine learning concepts such as data preparation, model training, and model evaluation. AWS-Certified-Machine-Learning-Specialty exam covers a wide range of topics, including machine learning algorithms, AWS services such as Amazon SageMaker, and data analysis techniques. Candidates must also demonstrate their ability to design, deploy, and maintain machine learning solutions using AWS services.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q49-Q54):

NEW QUESTION # 49
A Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access notebooks, train models, and deploy endpoints. To ensure the best operational performance, the Specialist needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked.
Which services are integrated with Amazon SageMaker to track this information? (Choose two.)

A. AWS CloudTrail
B. AWS Config
C. Amazon CloudWatch
D. AWS Trusted Advisor
E. AWS Health

Answer: A,C

Explanation:
https://aws.amazon.com/sagemaker/faqs/

NEW QUESTION # 50
A Machine Learning team runs its own training algorithm on Amazon SageMaker. The training algorithm requires external assets. The team needs to submit both its own algorithm code and algorithm-specific parameters to Amazon SageMaker.
What combination of services should the team use to build a custom algorithm in Amazon SageMaker?
(Choose two.)

A. AWS Secrets Manager
B. Amazon S3
C. Amazon ECR
D. AWS CodeStar
E. Amazon ECS

Answer: B,C

NEW QUESTION # 51
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?

A. 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 feature. When the new version of the model is ready for release, gradually increase DesiredWeight until all users have the updated version.
B. Configure two SageMaker hosted endpoints that serve the different versions of the model. Create an Amazon Route 53 record that is configured with a simple routing policy and that points to the current version of the model. 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 users. 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.
C. Configure two SageMaker hosted endpoints that serve the different versions of the model. Create an Application Load Balancer (ALB) to route traffic to both endpoints based on the TargetVariant query string parameter. Reconfigure the app to send the TargetVariant query string parameter for users who subscribed to the preview feature. 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.
D. 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 feature. When the new version of the model is ready for release, gradually increase InitialVariantWeight until all users have the updated version.

Answer: B

NEW QUESTION # 52
A financial services company wants to adopt Amazon SageMaker as its default data science environment. The company's data scientists run machine learning (ML) models on confidential financial dat a. The company is worried about data egress and wants an ML engineer to secure the environment.
Which mechanisms can the ML engineer use to control data egress from SageMaker? (Choose three.)

A. Use SCPs to restrict access to SageMaker.
B. Protect data with encryption at rest and in transit. Use AWS Key Management Service (AWS KMS) to manage encryption keys.
C. Disable root access on the SageMaker notebook instances.
D. Enable network isolation for training jobs and models.
E. Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink.
F. Restrict notebook presigned URLs to specific IPs used by the company.

Answer: A,D,F

Explanation:
https://aws.amazon.com/blogs/machine-learning/millennium-management-secure-machine-learning-using-amazon-sagemaker/

NEW QUESTION # 53
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?

A. 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 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 Video and create a stream processor 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, 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.
D. 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.

Answer: A

Explanation:
https://aws.amazon.com/blogs/machine-learning/video-analytics-in-the-cloud-and-at-the-edge- with-aws-deeplens-and-kinesis-video-streams/

NEW QUESTION # 54
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