A development team runs monthly resource-intensive tests on its general purpose Amazon RDS for MySQL DB instance with Performance Insights enabled. The testing lasts for 48 hours once a month and is the only process that uses the database. The team wants to reduce the cost of running the tests without reducing the compute and memory attributes of the DB instance.
Which solution meets these requirements MOST cost-effectively?
A. Stop the DB instance when tests are completed. Restart the DB instance when required. B. Use an Auto Scaling policy with the DB instance to automatically scale when tests are completed. C. Create a snapshot when tests are completed. Terminate the DB instance and restore the snapshot when required. D. Modify the DB instance to a low-capacity instance when tests are completed. Modify the DB instance again when required.
C. Create a snapshot when tests are completed. Terminate the DB instance and restore the snapshot when required.
Question 272:
A company has an application that runs on a single Amazon EC2 instance. The application uses a MySQL database that runs on the same EC2 instance. The company needs a highly available and automatically scalable solution to handle increased traffic.
Which solution will meet these requirements?
A. Deploy the application to EC2 instances that run in an Auto Scaling group behind an Application Load Balancer. Create an Amazon Redshift cluster that has multiple MySQL-compatible nodes. B. Deploy the application to EC2 instances that are configured as a target group behind an Application Load Balancer. Create an Amazon RDS for MySQL cluster that has multiple instances. C. Deploy the application to EC2 instances that run in an Auto Scaling group behind an Application Load Balancer. Create an Amazon Aurora Serverless MySQL cluster for the database layer. D. Deploy the application to EC2 instances that are configured as a target group behind an Application Load Balancer. Create an Amazon ElastiCache for Redis cluster that uses the MySQL connector.
C. Deploy the application to EC2 instances that run in an Auto Scaling group behind an Application Load Balancer. Create an Amazon Aurora Serverless MySQL cluster for the database layer.
Question 273:
A company is hosting a three-tier ecommerce application in the AWS Cloud. The company hosts the website on Amazon S3 and integrates the website with an API that handles sales requests. The company hosts the API on three Amazon EC2 instances behind an Application Load Balancer (ALB). The API consists of static and dynamic front-end content along with backend workers that process sales requests asynchronously. The company is expecting a significant and sudden increase in the number of sales requests during events for the launch of new products.
What should a solutions architect recommend to ensure that all the requests are processed successfully?
A. Add an Amazon CloudFront distribution for the dynamic content. Increase the number of EC2 instances to handle the increase in traffic. B. Add an Amazon CloudFront distribution for the static content. Place the EC2 instances in an Auto Scaling group to launch new instances based on network traffic. C. Add an Amazon CloudFront distribution for the dynamic content. Add an Amazon ElastiCache instance in front of the ALB to reduce traffic for the API to handle. D. Add an Amazon CloudFront distribution for the static content. Add an Amazon Simple Queue Service (Amazon SQS) queue to receive requests from the website for later processing by the EC2 instances.
D. Add an Amazon CloudFront distribution for the static content. Add an Amazon Simple Queue Service (Amazon SQS) queue to receive requests from the website for later processing by the EC2 instances.
Question 274:
A company runs an application on AWS. The application receives inconsistent amounts of usage. The application uses AWS Direct Connect to connect to an on-premises MySQL-compatible database. The on-premises database consistently uses a minimum of 2 GiB of memory.
The company wants to migrate the on-premises database to a managed AWS service. The company wants to use auto scaling capabilities to manage unexpected workload increases.
Which solution will meet these requirements with the LEAST administrative overhead?
A. Provision an Amazon DynamoDB database with default read and write capacity settings. B. Provision an Amazon Aurora database with a minimum capacity of 1 Aurora capacity unit (ACU). C. Provision an Amazon Aurora Serverless v2 database with a minimum capacity of 1 Aurora capacity unit (ACU). D. Provision an Amazon RDS for MySQL database with 2 GiB of memory.
C. Provision an Amazon Aurora Serverless v2 database with a minimum capacity of 1 Aurora capacity unit (ACU).
Question 275:
A company runs all its business applications in the AWS Cloud. The company uses AWS Organizations to manage multiple AWS accounts.
A solutions architect needs to review all permissions that are granted to IAM users to determine which IAM users have more permissions than required.
Which solution will meet these requirements with the LEAST administrative overhead?
A. Use Network Access Analyzer to review all access permissions in the company's AWS accounts. B. Create an AWS CloudWatch alarm that activates when an IAM user creates or modifies resources in an AWS account. C. Use AWS Identity and Access Management (IAM) Access Analyzer to review all the company's resources and accounts. D. Use Amazon Inspector to find vulnerabilities in existing IAM policies.
C. Use AWS Identity and Access Management (IAM) Access Analyzer to review all the company's resources and accounts.
Question 276:
A company has an Amazon S3 bucket that contains sensitive data files. The company has an application that runs on virtual machines in an on-premises data center. The company currently uses AWS IAM Identity Center.
The application requires temporary access to files in the S3 bucket. The company wants to grant the application secure access to the files in the S3 bucket.
Which solution will meet these requirements?
A. Create an S3 bucket policy that permits access to the bucket from the public IP address range of the company's on-premises data center. B. Use IAM Roles Anywhere to obtain security credentials in IAM Identity Center that grant access to the S3 bucket. Configure the virtual machines to assume the role by using the AWS CLI. C. Install the AWS CLI on the virtual machine. Configure the AWS CLI with access keys from an IAM user that has access to the bucket. D. Create an IAM user and policy that grants access to the bucket. Store the access key and secret key for the IAM user in AWS Secrets Manager. Configure the application to retrieve the access key and secret key at startup.
B. Use IAM Roles Anywhere to obtain security credentials in IAM Identity Center that grant access to the S3 bucket. Configure the virtual machines to assume the role by using the AWS CLI.
Question 277:
A company provides a Voice over Internet Protocol (VoIP) service that uses UDP connections. The service consists of Amazon EC2 instances that run in an Auto Scaling group. The company has deployments across multiple AWS Regions.
The company needs to route users to the Region with the lowest latency. The company also needs automated failover between Regions.
Which solution will meet these requirements?
A. Deploy a Network Load Balancer (NLB) and an associated target group. Associate the target group with the Auto Scaling group. Use the NLB as an AWS Global Accelerator endpoint in each Region. B. Deploy an Application Load Balancer (ALB) and an associated target group. Associate the target group with the Auto Scaling group. Use the ALB as an AWS Global Accelerator endpoint in each Region. C. Deploy a Network Load Balancer (NLB) and an associated target group. Associate the target group with the Auto Scaling group. Create an Amazon Route 53 latency record that points to aliases for each NLB. Create an Amazon CloudFront distribution that uses the latency record as an origin. D. Deploy an Application Load Balancer (ALB) and an associated target group. Associate the target group with the Auto Scaling group. Create an Amazon Route 53 weighted record that points to aliases for each ALB. Deploy an Amazon CloudFront distribution that uses the weighted record as an origin.
A. Deploy a Network Load Balancer (NLB) and an associated target group. Associate the target group with the Auto Scaling group. Use the NLB as an AWS Global Accelerator endpoint in each Region.
Question 278:
The customers of a finance company request appointments with financial advisors by sending text messages. A web application that runs on Amazon EC2 instances accepts the appointment requests. The text messages are published to an Amazon Simple Queue Service (Amazon SQS) queue through the web application. Another application that runs on EC2 instances then sends meeting invitations and meeting confirmation email messages to the customers. After successful scheduling, this application stores the meeting information in an Amazon DynamoDB database. As the company expands, customers report that their meeting invitations are taking longer to arrive.
What should a solutions architect recommend to resolve this issue?
A. Add a DynamoDB Accelerator (DAX) cluster in front of the DynamoDB database. B. Add an Amazon API Gateway API in front of the web application that accepts the appointment requests. C. Add an Amazon CloudFront distribution. Set the origin as the web application that accepts the appointment requests. D. Add an Auto Scaling group for the application that sends meeting invitations. Configure the Auto Scaling group to scale based on the depth of the SQS queue.
D. Add an Auto Scaling group for the application that sends meeting invitations. Configure the Auto Scaling group to scale based on the depth of the SQS queue.
Question 279:
A company needs to provide customers with secure access to its data. The company processes customer data and stores the results in an Amazon S3 bucket. All the data is subject to strong regulations and security requirements. The data must be encrypted at rest. Each customer must be able to access only their data from their AWS account. Company employees must not be able to access the data.
Which solution will meet these requirements?
A. Provision an AWS Certificate Manager (ACM) certificate for each customer. Encrypt the data client-side. In the private certificate policy, deny access to the certificate for all principals except an IAM role that the customer provides. B. Provision a separate AWS Key Management Service (AWS KMS) key for each customer. Encrypt the data server-side. In the S3 bucket policy, deny decryption of data for all principals except an IAM role that the customer provides. C. Provision a separate AWS Key Management Service (AWS KMS) key for each customer. Encrypt the data server-side. In each KMS key policy, deny decryption of data for all principals except an IAM role that the customer provides. D. Provision an AWS Certificate Manager (ACM) certificate for each customer. Encrypt the data client-side. In the public certificate policy, deny access to the certificate for all principals except an IAM role that the customer provides.
C. Provision a separate AWS Key Management Service (AWS KMS) key for each customer. Encrypt the data server-side. In each KMS key policy, deny decryption of data for all principals except an IAM role that the customer provides.
Question 280:
A company needs a solution to ingest streaming sensor data from 100,000 devices, transform the data in near real time, and load the data into Amazon S3 for analysis. The solution must be fully managed, scalable, and maintain sub-second ingestion latency.
Which solution will meet these requirements?
A. Use Amazon Kinesis Data Streams to ingest the data. Use Amazon Managed Service for Apache Flink to process the data in near real time. Use an Amazon Data Firehose stream to send processed data to Amazon S3. B. Use Amazon Simple Queue Service (Amazon SQS) standard queues to collect the sensor data. Invoke AWS Lambda functions to transform and process SQS messages in batches. Configure the Lambda functions to use an AWS SDK to write transformed data to Amazon S3. C. Deploy a fleet of Amazon EC2 instances that run Apache Kafka to ingest the data. Run Apache Spark on Amazon EMR clusters to process the data. Configure Spark to write processed data directly to Amazon S3. D. Implement Amazon EventBridge to capture all sensor data. Use AWS Batch to run containerized transformation jobs on a schedule. Configure AWS Batch jobs to process data in chunks. Save results to Amazon S3.
A. Use Amazon Kinesis Data Streams to ingest the data. Use Amazon Managed Service for Apache Flink to process the data in near real time. Use an Amazon Data Firehose stream to send processed data to Amazon S3.
Explanation
The most scalable and managed solution for streaming ingestion, real-time transformation, and delivery to Amazon S3 is Amazon Kinesis Data Streams, Amazon Managed Service for Apache Flink, and Amazon Kinesis Data Firehose.
From AWS Documentation:
"Amazon Kinesis Data Streams enables real-time processing of streaming data at massive scale. With Apache Flink on Kinesis Data Analytics, you can process data streams in near real-time, then use Amazon Kinesis Data Firehose to reliably deliver that data to S3." (Source: Amazon Kinesis Developer Guide)
Why Option A is correct:
Fully managed: All services involved are serverless and managed.
Real-time ingestion: Kinesis Data Streams supports sub-second latency and can handle high-throughput workloads like 100,000+ devices.
Near real-time processing: Apache Flink is designed for continuous stream processing with complex event handling.
Efficient delivery: Kinesis Firehose delivers processed data directly to S3 with retry and backup capability.
Why other options are incorrect:
Option B: SQS is not optimized for real-time streaming at high volume.
Option C: EC2 + Kafka + EMR adds high operational overhead and cost.
Option D: EventBridge is event-driven, not designed for high-throughput streaming; AWS Batch is unsuitable for near real-time processing.
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