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AWS Certified Solutions Architect - Professional Questions and Answers

Question 125

A company runs a processing engine in the AWS Cloud. The engine processes environmental data from logistics centers to calculate a sustainability index. The company has millions of devices in logistics centers that are spread across Europe. The devices send information to the processing engine through a RESTful API. The API experiences unpredictable bursts of traffic. The company must implement a solution to process all data that the devices send to the processing engine. Data loss is unacceptable. Which solution will meet these requirements?

Options:

A.

Create an Application Load Balancer (ALB) for the RESTful API. Create an Amazon SQS queue. Create a listener and a target group for the ALB. Add the SQS queue as the target. Use a container that runs in Amazon ECS with the Fargate launch type to process messages in the queue.

B.

Create an Amazon API Gateway HTTP API that implements the RESTful API. Create an Amazon SQS queue. Create an API Gateway service integration with the SQS queue. Create an AWS Lambda function to process messages in the SQS queue.

C.

Create an Amazon API Gateway REST API that implements the RESTful API. Create a fleet of Amazon EC2 instances in an Auto Scaling group. Create an API Gateway Auto Scaling group proxy integration. Use the EC2 instances to process incoming data.

D.

Create an Amazon CloudFront distribution for the RESTful API. Create a data stream in Amazon Kinesis Data Streams. Set the data stream as the origin for the distribution. Create an AWS Lambda function to consume and process data in the data stream.

Question 126

A company that provides image storage services wants to deploy a customer-lacing solution to AWS. Millions of individual customers will use the solution. The solution will receive batches of large image files, resize the files, and store the files in an Amazon S3 bucket for up to 6 months.

The solution must handle significant variance in demand. The solution must also be reliable at enterprise scale and have the ability to rerun processing jobs in the event of failure.

Which solution will meet these requirements MOST cost-effectively?

Options:

A.

Use AWS Step Functions to process the S3 event that occurs when a user stores an image. Run an AWS Lambda function that resizes the image in place and replaces the original file in the S3 bucket. Create an S3 Lifecycle expiration policy to expire all stored images after 6 months.

B.

Use Amazon EventBridge to process the S3 event that occurs when a user uploads an image. Run an AWS Lambda function that resizes the image in place and replaces the original file in the S3 bucket. Create an S3 Lifecycle expiration policy to expire all stored images after 6 months.

C.

Use S3 Event Notifications to invoke an AWS Lambda function when a user stores an image. Use the Lambda function to resize the image in place and to store the original file in the S3 bucket. Create an S3 Lifecycle policy to move all stored images to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months.

D.

Use Amazon Simple Queue Service (Amazon SQS) to process the S3 event that occurs when a user stores an image. Run an AWS Lambda function that resizes the image and stores the resized file in an S3 bucket that uses S3 Standard-Infrequent Access (S3 Standard-IA). Create an S3 Lifecycle policy to move all stored images to S3 Glacier Deep Archive after 6 months.

Question 127

A company is running an event ticketing platform on AWS and wants to optimize the platform's cost-effectiveness. The platform is deployed on Amazon Elastic Kubernetes Service (Amazon EKS) with Amazon EC2 and is backed by an Amazon RDS for MySQL DB instance. The company is developing new application features to run on Amazon EKS with AWS Fargate.

The platform experiences infrequent high peaks in demand. The surges in demand depend on event dates.

Which solution will provide the MOST cost-effective setup for the platform?

Options:

A.

Purchase Standard Reserved Instances for the EC2 instances that the EKS cluster uses in its baseline load. Scale the cluster with Spot Instances to handle peaks. Purchase 1-year All Upfront Reserved Instances for the database to meet predicted peak load for the year.

B.

Purchase Compute Savings Plans for the predicted medium load of the EKS cluster. Scale the cluster with On-Demand Capacity Reservations based on event dates for peaks. Purchase 1-year No Upfront Reserved Instances for the database to meet the predicted base load. Temporarily scale out database read replicas during peaks.

C.

Purchase EC2 Instance Savings Plans for the predicted base load of the EKS cluster. Scale the cluster with Spot Instances to handle peaks. Purchase 1-year All Upfront Reserved Instances for the database to meet the predicted base load. Temporarily scale up the DB instance manually during peaks.

D.

Purchase Compute Savings Plans for the predicted base load of the EKS cluster. Scale the cluster with Spot Instances to handle peaks. Purchase 1-year All Upfront Reserved Instances for the database to meet the predicted base load. Temporarily scale up the DB instance manually during peaks.

Question 128

A company has loT sensors that monitor traffic patterns throughout a large city. The company wants to read and collect data from the sensors and perform aggregations on the data.

A solutions architect designs a solution in which the loT devices are streaming to Amazon Kinesis Data Streams. Several applications are reading from the stream. However, several consumers are experiencing throttling and are periodically and are periodically encountering a RealProvisioned Throughput Exceeded error.

Which actions should the solution architect take to resolve this issue? (Select THREE.)

Options:

A.

Reshard the stream to increase the number of shards s in the stream.

B.

Use the Kinesis Producer Library KPL). Adjust the polling frequency.

C.

Use consumers with the enhanced fan-out feature.

D.

Reshard the stream to reduce the number of shards in the stream.

E.

Use an error retry and exponential backoff mechanism in the consumer logic.

F.

Configure the stream to use dynamic partitioning.

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