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Amazon AWS Certified Solutions Architect - Professional (SAP-C02) Sample Questions (Q246-Q251):
NEW QUESTION # 246
A company uses a service to collect metadata from applications that the company hosts on premises.
Consumer devices such as TVs and internet radios access the applications. Many older devices do not support certain HTTP headers and exhibit errors when these headers are present in responses. The company has configured an on-premises load balancer to remove the unsupported headers from responses sent to older devices, which the company identified by the User-Agent headers.
The company wants to migrate the service to AWS, adopt serverless technologies, and retain the ability to support the older devices. The company has already migrated the applications into a set of AWS Lambda functions.
Which solution will meet these requirements?
- A. Create an Amazon CloudFront distribution for the metadata service. Create an Application Load Balancer (ALB). Configure the CloudFront distribution to forward requests to the ALB. Configure the ALB to invoke the correct Lambda function for each type of request. Create a Lambda@Edge function that will remove the problematic headers in response to viewer requests based on the value of the User-Agent header.
- B. Create an Amazon API Gateway HTTP API for the metadata service. Configure API Gateway to invoke the correct Lambda function for each type of request. Create a response mapping template to remove the problematic headers based on the value of the User-Agent. Associate the response data mapping with the HTTP API.
- C. Create an Amazon API Gateway REST API for the metadata service. Configure API Gateway to invoke the correct Lambda function for each type of request. Modify the default gateway responses to remove the problematic headers based on the value of the User-Agent header.
- D. Create an Amazon CloudFront distribution for the metadata service. Create an Application Load Balancer (ALB). Configure the CloudFront distribution to forward requests to the ALB. Configure the ALB to invoke the correct Lambda function for each type of request. Create a CloudFront function to remove the problematic headers based on the value of the User-Agent header.
Answer: C
NEW QUESTION # 247
An auction website enables users to bid on collectible items The auction rules require that each bid is processed only once and in the order it was received The current implementation is based on a fleet of Amazon EC2 web servers that write bid records into Amazon Kinesis Data Streams A single 12 large instance has a cron job that runs the bid processor, which reads incoming bids from Kinesis Data Streams and processes each bid The auction site is growing in popularity, but users are complaining that some bids are not registering Troubleshooting indicates that the bid processor is too slow during peak demand hours sometimes crashes while processing and occasionally loses track of which record is being processed What changes should make the bid processing more reliable?
- A. Refactor the web application to post each incoming bid to an Amazon SNS topic in place of Kinesis Data Streams Configure the SNS topic to trigger an AWS Lambda function that B. processes each bid as soon as a user submits it
- B. Refactor the web application to post each incoming bid to an Amazon SQS FIFO queue in place of Kinesis Data Streams Refactor the bid processor to continuously consume the SQS queue Place the bid processing EC2 instance in an Auto Scaling group with a minimum and a maximum size of 1
- C. Switch the EC2 instance type from t2 large to a larger general compute instance type Put the bid processor EC2 instances in an Auto Scaling group that scales out the number of EC2 instances running the bid processor based on the incomingRecords metric in Kinesis Data Streams
- D. Refactor the web application to use the Amazon Kinesis Producer Library (KPL) when posting bids to Kinesis Data Streams Refactor the bid processor to flag each record in Kinesis Data Streams as being unread processing and processed At the start of each bid processing run; scan Kinesis Data Streams for unprocessed records
Answer: B
Explanation:
https://aws.amazon.com/sqs/faqs/#:~:text=A%20single%20Amazon%20SQS%20message,20%2C000%20for%20a%20FIFO%20queue.
NEW QUESTION # 248
A company is running a data-intensive application on AWS. The application runs on a cluster of hundreds of Amazon EC2 instances. A shared file system also runs on several EC2 instances that store 200 TB of dat a. The application reads and modifies the data on the shared file system and generates a report. The job runs once monthly, reads a subset of the files from the shared file system, and takes about 72 hours to complete. The compute instances scale in an Auto Scaling group, but the instances that host the shared file system run continuously. The compute and storage instances are all in the same AWS Region.
A solutions architect needs to reduce costs by replacing the shared file system instances. The file system must provide high performance access to the needed data for the duration of the 72-hour run.
Which solution will provide the LARGEST overall cost reduction while meeting these requirements?
- A. Migrate the data from the existing shared file system to an Amazon S3 bucket. Before the job runs each month, use AWS Storage Gateway to create a file gateway with the data from Amazon S3. Use the file gateway as the shared storage for the job. Delete the file gateway when the job is complete.
- B. Migrate the data from the existing shared file system to an Amazon S3 bucket that uses the S3 Intelligent-Tiering storage class. Before the job runs each month, use Amazon FSx for Lustre to create a new file system with the data from Amazon S3 by using lazy loading. Use the new file system as the shared storage for the duration of the job. Delete the file system when the job is complete.
- C. Migrate the data from the existing shared file system to a large Amazon Elastic Block Store (Amazon EBS) volume with Multi-Attach enabled. Attach the EBS volume to each of the instances by using a user data script in the Auto Scaling group launch template. Use the EBS volume as the shared storage for the duration of the job. Detach the EBS volume when the job is complete.
- D. Migrate the data from the existing shared file system to an Amazon S3 bucket that uses the S3 Standard storage class. Before the job runs each month, use Amazon FSx for Lustre to create a new file system with the data from Amazon S3 by using batch loading. Use the new file system as the shared storage for the duration of the job. Delete the file system when the job is complete.
Answer: B
Explanation:
https://aws.amazon.com/blogs/storage/new-enhancements-for-moving-data-between-amazon-fsx-for-lustre-and-amazon-s3/
NEW QUESTION # 249
A company recently deployed an application on AWS. The application uses Amazon DynamoDB. The company measured the application load and configured the RCUs and WCUs on the DynamoDB table to match the expected peak load. The peak load occurs once a week for a 4-hour period and is double the average load. The application load is close to the average load tor the rest of the week. The access pattern includes many more writes to the table than reads of the table.
A solutions architect needs to implement a solution to minimize the cost of the table.
Which solution will meet these requirements?
- A. Configure DynamoDB Accelerator (DAX) in front of the table. Configure on-demand capacity mode for the table.
- B. Use AWS Application Auto Scaling to increase capacity during the peak period. Purchase reserved RCUs and WCUs to match the average load.
- C. Configure on-demand capacity mode for the table.
- D. Configure DynamoDB Accelerator (DAX) in front of the table. Reduce the provisioned read capacity to match the new peak load on the table.
Answer: B
Explanation:
This solution meets the requirements by using Application Auto Scaling to automatically increase capacity during the peak period, which will handle the double the average load. And by purchasing reserved RCUs and WCUs to match the average load, it will minimize the cost of the table for the rest of the week when the load is close to the average.
NEW QUESTION # 250
A company's solutions architect is evaluating an AWS workload that was deployed several years ago. The application tier is stateless and runs on a single large Amazon EC2 instance that was launched from an AMI.
The application stores data in a MySOL database that runs on a single EC2 instance.
The CPU utilization on the application server EC2 instance often reaches 100% and causes the application to stop responding. The company manually installs patches on the instances. Patching has caused downtime in the past. The company needs to make the application highly available.
Which solution will meet these requirements with the LEAST development time?
- A. Move the application tier to containers by using Docker. Run the containers on Amazon Elastic Container Service (Amazon ECS) with EC2 instances. Create an Application Load Balancer to distribute traffic across the ECS cluster Configure the ECS cluster to scale based on CPU utilization.
Migrate the database to Amazon Neptune. - B. Create a new AMI that is configured with AWS Systems Manager Agent (SSM Agent). Use the new AMI to create a launch template for an Auto Scaling group. Use smaller instances in the Auto Scaling group. Create an Application Load Balancer to distribute traffic across the instances in the Auto Scaling group. Set the Auto Scaling group to scale based on CPU utilization. Migrate the database to Amazon Aurora MySQL.
- C. Change the EC2 instance type to a smaller Graviton powered instance type. use the existing AMI to create a launch template for an Auto Scaling group. Create an Application Load Balancer to distribute traffic across the instances in the Auto Scaling group. Set the Auto Scaling group to scale based on CPU utilization. Migrate the database to Amazon DynamoDB.
- D. Move the application tier to AWS Lambda functions in the existing VPC. Create an Application Load Balancer to distribute traffic across the Lambda functbns. Use Amazon GuardDuty to scan the Lambda functions. Migrate the database to Amazon DocumentDB (with MongoDB compatibility).
Answer: B
Explanation:
Explanation
This solution will meet the requirements of making the application highly available with the least development time. Creating a new AMI that is configured with SSM Agent will enable the company to use AWS Systems Manager to manage and patch the EC2 instances automatically, reducing downtime and human errors. Using a launch template for an Auto Scaling group will allow the company to launch multiple instances of the same configuration and scale them up or down based on demand. Using smaller instances in the Auto Scaling group will reduce the cost and improve the performance of the application tier. Creating an Application Load Balancer to distribute traffic across the instances in the Auto Scaling group will increase the availability and fault tolerance of the application tier. Migrating the database to Amazon Aurora MySQL will provide a fully managed, compatible, and scalable relational database service that can handle high throughput and concurrent connections.
NEW QUESTION # 251
......
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