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NEW QUESTION 23
A life sciences company is using a combination of open source tools to manage data analysis workflows and Docker containers running on servers in its on-premises data center to process genomics data Sequencing data is generated and stored on a local storage area network (SAN), and then the data is processed. The research and development teams are running into capacity issues and have decided to re-architect their genomics analysis platform on AWS to scale based on workload demands and reduce the turnaround time from weeks to days
The company has a high-speed AWS Direct Connect connection Sequencers will generate around 200 GB of data for each genome, and individual jobs can take several hours to process the data with ideal compute capacity. The end result will be stored in Amazon S3. The company is expecting 10-15 job requests each day
Which solution meets these requirements?

• A. Use AWS DataSync to transfer the sequencing data to Amazon S3 Use S3 events to trigger an AWS Lambda function that starts an AWS Step Functions workflow Store the Docker images in Amazon Elastic Container Registry (Amazon ECR) and trigger AWS Batch to run the container and process the sequencing data
• B. Use AWS Data Pipeline to transfer the sequencing data to Amazon S3 Use S3 events to trigger an Amazon EC2 Auto Scaling group to launch custom-AMI EC2 instances running the Docker containers to process the data
• C. Use regularly scheduled AWS Snowball Edge devices to transfer the sequencing data into AWS When AWS receives the Snowball Edge device and the data is loaded into Amazon S3 use S3 events to trigger an AWS Lambda function to process the data
• D. Use an AWS Storage Gateway file gateway to transfer the sequencing data to Amazon S3 Use S3 events to trigger an AWS Batch job that runs on Amazon EC2 instances running the Docker containers to process the data

Answer: A

NEW QUESTION 24
A company has a website that enables users to upload videos. Company policy states the uploaded videos must be analyzed for restricted content. An uploaded video is placed in Amazon S3, and a message is pushed to an Amazon SOS queue with the video's location. A backend application pulls this location from Amazon SOS and analyzes the video.
The video analysis is compute-intensive and occurs sporadically during the day. The website scales with demand. The video analysis application runs on a fixed number of instances. Peak demand occurs during the holidays, so the company must add instances to the application dunng this time. All instances used are currently on-demand Amazon EC2 T2 instances. The company wants to reduce the cost of the current solution.
Which of the following solutions is MOST cost-effective?

• A. Migrate the website to AWS Elastic Beanstalk and Amazon EC2 R4 instances. Determine the minimum number of website instances required during off-peak times and use Reserved Instances to cover them while using On-Demand Instances to cover peak demand Use Spot Fleet for the video analysis application comprised of R4 and Amazon EC2 R5 instances
• B. Keep the website on T2 instances. Determine the minimum number of website instances required during off-peak times and use Reserved Instances to cover them while using On-Demand Instances to cover peak demand. Use Spot Fleet for the video analysis application comprised of Amazon EC2 C4 and Amazon EC2 C5 Spot Instances.
• C. Migrate the website to AWS Elastic Beanstalk and Amazon EC2 C4 instances. Determine the minimum number of website instances required during off-peak times and use On-Demand Instances to cover them while using Spot capacity to cover peak demand Use Spot Fleet for the video anarysis application comprised of C4 and Amazon EC2 C5 instances.
• D. Keep the website on T2 instances. Determine the minimum number of website instances required during off-peak times and use Spot Instances to cover them while using Reserved Instances to cover peak demand. Use Amazon EC2 R4 and Amazon EC2 R5 Reserved Instances in an Auto Scaling group for the video analysis application

Answer: B

NEW QUESTION 25
A company is building an electronic document management system in which users upload their documents. The application stack is entirely serverless and runs on AWS in the eu-central-1 Region. The system includes a web application that uses an Amazon CloudFront distribution for delivery with Amazon S3 as the origin. The web application communicates with Amazon API Gateway Regional endpoints. The API Gateway APIs call AWS Lambda functions that store metadata in an Amazon Aurora Serverless database and put the documents into an S3 bucket.
The company is growing steadily and has completed a proof of concept with its largest customer. The company must improve latency outside of Europe.
Which combination of actions will meet these requirements? (Select TWO.)

• A. Add an Amazon RDS proxy between the Lambda functions and the Aurora Serverless database.
• B. Enable S3 Transfer Acceleration on the S3 bucket. Ensure that the web application uses the Transfer Acceleration signed URLs.
• C. Change the API Gateway Regional endpoints to edge-optimized endpoints.
• D. Provision the entire stack in two other locations that are spread across the world. Use global databases on the Aurora Serverless cluster.
• E. Create an accelerator in AWS Global Accelerator. Attach the accelerator to the CloudFront distribution.

Answer: B,C

Explanation:
https://aws.amazon.com/global-accelerator/faqs/

NEW QUESTION 26
A company's interactive web application uses an Amazon CloudFront distribution to serve images from an Amazon S3 bucket. Occasionally, third-party tools ingest corrupted images into the S3 bucket. This image corruption causes a poor user experience in the application later. The company has successfully implemented and tested Python logic to detect corrupt images.
A solutions architect must recommend a solution to integrate the detection logic with minimal latency between the ingestion and serving.
Which solution will meet these requirements?

• A. Use a Lambda@Edge function that is invoked by a viewer-response event.
• B. Use a Lambda@Edge function that is invoked by an origin-response event.
• C. Use an S3 event notification that invokes an AWS Step Functions state machine.
• D. Use an S3 event notification that invokes an AWS Lambda function.

Answer: B

Explanation:
This solution will allow the detection logic to be run as soon as the image is uploaded to the S3 bucket, before it is served to users via the CloudFront distribution. This way, the detection logic can quickly identify any corrupted images and prevent them from being served to users, minimizing latency between ingestion and serving.

NEW QUESTION 27
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