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, 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.
• 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, 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.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.

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A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier:
Total number of images available = 1,000 Test set images = 100 (constant test set) The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.
Which techniques can be used by the ML Specialist to improve this specific test error?

• A.Increase the number of epochs for model training.
• B.Increase the dropout rate for the second-to-last layer.
• C.Increase the number of layers for the neural network.
• D.Increase the training data by adding variation in rotation for training images.

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A company needs to quickly make sense of a large amount of data and gain insight from it. The data is in different formats, the schemas change frequently, and new data sources are added regularly. The company wants to use AWS services to explore multiple data sources, suggest schemas, and enrich and transform the dat a. The solution should require the least possible coding effort for the data flows and the least possible infrastructure management.
Which combination of AWS services will meet these requirements?

• A.Amazon EMR for data discovery, enrichment, and transformation
  Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights
• B.Amazon Kinesis Data Analytics for data ingestion
  Amazon EMR for data discovery, enrichment, and transformation
  Amazon Redshift for querying and analyzing the results in Amazon S3
• C.AWS Glue for data discovery, enrichment, and transformation
  Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights
• D.AWS Data Pipeline for data transfer
  AWS Step Functions for orchestrating AWS Lambda jobs for data discovery, enrichment, and transformation Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL

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A gaming company has launched an online game where people can start playing for free but they need to pay if they choose to use certain features The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year The company has gathered a labeled dataset from 1 million users The training dataset consists of 1.000 positive samples (from users who ended up paying within 1 year) and
999.000 negative samples (from users who did not use any paid features) Each data sample consists of 200 features including user age, device, location, and play patterns Using this dataset for training, the Data Science team trained a random forest model that converged with over
99% accuracy on the training set However, the prediction results on a test dataset were not satisfactory.
Which of the following approaches should the Data Science team take to mitigate this issue? (Select TWO.)

• A.Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.
• B.Change the cost function so that false positives have a higher impact on the cost value than false negatives
• C.Change the cost function so that false negatives have a higher impact on the cost value than false positives
• D.indicate a copy of the samples in the test database in the training dataset
• E.Add more deep trees to the random forest to enable the model to learn more features.

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A machine learning (ML) specialist wants to secure calls to the Amazon SageMaker Service API. The specialist has configured Amazon VPC with a VPC interface endpoint for the Amazon SageMaker Service API and is attempting to secure traffic from specific sets of instances and IAM users. The VPC is configured with a single public subnet.
Which combination of steps should the ML specialist take to secure the traffic? (Choose two.)

• A.Add a VPC endpoint policy to allow access to the IAM users.
• B.Add a SageMaker Runtime VPC endpoint interface to the VPC.
• C.Modify the ACL on the endpoint network interface to restrict access to the instances.
• D.Modify the users' IAM policy to allow access to Amazon SageMaker Service API calls only.
• E.Modify the security group on the endpoint network interface to restrict access to the instances.

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