A company runs an IoT platform on AWS. IoT sensors in various locations send data to the company’s Node.js API servers on Amazon EC2 instances running behind an Application Load Balancer. The data is stored in an Amazon RDS MySQL DB instance that uses a 4 TB General Purpose SSD volume. The number of sensors the company has deployed in the field has increased over time, and is expected to grow significantly. The API servers are consistently overloaded and RDS metrics show high write latency. Which of the following steps together will resolve the issues permanently and enable growth as new sensors are provisioned, while keeping this platform cost-efficient? (Choose two.)

A. Resize the MySQL General Purpose SSD storage to 6 TB to improve the volume’s IOPS
B. Re-architect the database tier to use Amazon Aurora instead of an RDS MySQL DB instance and add read replicas
C. Leverage Amazon Kinesis Data Streams and AWS Lambda to ingest and process the raw data
D. Use AWS-X-Ray to analyze and debug application issues and add more API servers to match the load
E. Re-architect the database tier to use Amazon DynamoDB instead of an RDS MySQL DB instance

A company that is new to AWS reports it has exhausted its service limits across several accounts that are on the Basic Support plan. The company would like to prevent this from happening in the future.
What is the MOST efficient way of monitoring and managing all service limits in the company’s accounts?

A. Use Amazon CloudWatch and AWS Lambda to periodically calculate the limits across all linked accounts using AWS Trusted Advisor, provide notifications using Amazon SNS if the limits are close to exceeding the threshold.
B. Reach out to AWS Support to proactively increase the limits across all accounts. That way, the customer avoids creating and managing infrastructure just to raise the service limits.
C. Use Amazon CloudWatch and AWS Lambda to periodically calculate the limits across all linked accounts using AWS Trusted Advisor, programmatically increase the limits that are close to exceeding the threshold.
D. Use Amazon CloudWatch and AWS Lambda to periodically calculate the limits across all linked accounts using AWS Trusted Advisor, and use Amazon SNS for notifications if a limit is close to exceeding the threshold. Ensure that the accounts are using the AWS Business Support plan at a minimum.

A company receives clickstream data files to Amazon S3 every five minutes. A Python script runs as a cron job once a day on an Amazon EC2 instance to process each file and load it into a database hosted on Amazon RDS. The cron job takes 15 to 30 minutes to process 24 hours of data. The data consumers ask for the data be available as soon as possible.
Which solution would accomplish the desired outcome?

A. Increase the size of the instance to speed up processing and update the schedule to run once an hour.
B. Convert the cron job to an AWS Lambda function and trigger this new function using a cron job on an EC2 instance.
C. Convert the cron job to an AWS Lambda function and schedule it to run once an hour using Amazon CloudWatch Events.
D. Create an AWS Lambda function that runs when a file is delivered to Amazon S3 using S3 event notifications.

A company runs its containerized batch jobs on Amazon ECS. The jobs are scheduled by submitting a container image, a task definition, and the relevant data to an Amazon S3 bucket. Container images may be unique per job. Running the jobs as quickly as possible is of utmost importance, so submitting job artifacts to the S3 bucket triggers the job to run immediately. Sometimes there may be no jobs running at all. However, jobs of any size can be submitted with no prior warning to the IT Operations team. Job definitions include CPU and memory resource requirements.
What solution will allow the batch jobs to complete as quickly as possible after being scheduled?

A. Schedule the jobs on an Amazon ECS cluster using the Amazon EC2 launch type. Use Service Auto Scaling to increase or decrease the number of running tasks to suit the number of running jobs.
B. Schedule the jobs directly on EC2 instances. Use Reserved Instances for the baseline minimum load, and use On-Demand Instances in an Auto Scaling group to scale up the platform based on demand.
C. Schedule the jobs on an Amazon ECS cluster using the Fargate launch type. Use Service Auto Scaling to increase or decrease the number of running tasks to suit the number of running jobs.
D. Schedule the jobs on an Amazon ECS cluster using the Fargate launch type. Use Spot Instances in an Auto Scaling group to scale the platform based on demand. Use Service Auto Scaling to increase or decrease the number of running tasks to suit the number of running jobs.

A company used Amazon EC2 instances to deploy a web fleet to host a blog site. The EC2 instances are behind an Application Load Balancer (ALB) and are configured in an Auto Scaling group. The web application stores all blog content on an Amazon EFS volume.
The company recently added a feature for bloggers to add video to their posts, attracting 10 times the previous user traffic. At peak times of day, users report buffering and timeout issues while attempting to reach the site or watch videos.
Which is the MOST cost-efficient and scalable deployment that will resolve the issues for users?

A. Reconfigure Amazon EFS to enable maximum I/O.
B. Update the blog site to use instance store volumes for storage. Copy the site contents to the volumes at launch and to Amazon S3 at shutdown.
C. Configure an Amazon CloudFront distribution. Point the distribution to an S3 bucket, and migrate the videos from EFS to Amazon S3.
D. Set up an Amazon CloudFront distribution for all site contents, and point the distribution at the ALB.

A company is using AWS to run an internet-facing production application written in Node.js. The Development team is responsible for pushing new versions of their software directly to production. The application software is updated multiple times a day. The team needs guidance from a Solutions Architect to help them deploy the software to the production fleet quickly and with the least amount of disruption to the service.
Which option meets these requirements?

A. Prepackage the software into an AMI and then use Auto Scaling to deploy the production fleet. For software changes, update the AMI and allow Auto Scaling to automatically push the new AMI to production.
B. Use AWS CodeDeploy to push the prepackaged AMI to production. For software changes, reconfigure CodeDeploy with new AMI identification to push the new AMI to the production fleet.
C. Use AWS Elastic Beanstalk to host the production application. For software changes, upload the new application version to Elastic Beanstalk to push this to the production fleet using a blue/green deployment method.
D. Deploy the base AMI through Auto Scaling and bootstrap the software using user data. For software changes, SSH to each of the instances and replace the software with the new version.

A company is planning to migrate an application from on-premises to AWS. The application currently uses an Oracle database and the company can tolerate a brief downtime of 1 hour when performing the switch to the new infrastructure. As part of the migration, the database engine will be changed to MySQL. A Solutions Architect needs to determine which AWS services can be used to perform the migration while minimizing the amount of work and time required.
Which of the following will meet the requirements?

A. Use AWS SCT to generate the schema scripts and apply them on the target prior to migration. Use AWS DMS to analyze the current schema and provide a recommendation for the optimal database engine. Then, use AWS DMS to migrate to the recommended engine. Use AWS SCT to identify what embedded SQL code in the application can be converted and what has to be done manually.
B. Use AWS SCT to generate the schema scripts and apply them on the target prior to migration. Use AWS DMS to begin moving data from the on-premises database to AWS. After the initial copy, continue to use AWS DMS to keep the databases in sync until cutting over to the new database. Use AWS SCT to identify what embedded SQL code in the application can be converted and what has to be done manually.
C. Use AWS DMS to help identify the best target deployment between installing the database engine on Amazon EC2 directly or moving to Amazon RDS. Then, use AWS DMS to migrate to the platform. Use AWS Application Discovery Service to identify what embedded SQL code in the application can be converted and what has to be done manually.
D. Use AWS DMS to begin moving data from the on-premises database to AWS. After the initial copy, continue to use AWS DMS to keep the databases in sync until cutting over to the new database. Use AWS Application Discovery Service to identify what embedded SQL code in the application can be converted and what has to be done manually.

A company is using AWS CloudFormation to deploy its infrastructure. The company is concerned that, if a production CloudFormation stack is deleted, important data stored in Amazon RDS databases or Amazon EBS volumes might also be deleted.
How can the company prevent users from accidentally deleting data in this way?

A. Modify the CloudFormation templates to add a DeletionPolicyattribute to RDS and EBS resources.
B. Configure a stack policy that disallows the deletion of RDS and EBS resources.
C. Modify IAM policies to deny deleting RDS and EBS resources that are tagged with an “aws:cloudformation:stack-name” tag.
D. Use AWS Config rules to prevent deleting RDS and EBS resources.

A company has an application that generates a weather forecast that is updated every 15 minutes with an output resolution of 1 billion unique positions, each approximately 20 bytes in size (20 Gigabytes per forecast). Every hour, the forecast data is globally accessed approximately 5 million times (1,400 requests per second), and up to 10 times more during weather events. The forecast data is overwritten every update. Users of the current weather forecast application expect responses to queries to be returned in less than two seconds for each request.
Which design meets the required request rate and response time?

A. Store forecast locations in an Amazon ES cluster. Use an Amazon CloudFront distribution targeting an Amazon API Gateway endpoint with AWS Lambda functions responding to queries as the origin. Enable API caching on the API Gateway stage with a cache-control timeout set for 15 minutes.
B. Store forecast locations in an Amazon EFS volume. Create an Amazon CloudFront distribution that targets an Elastic Load Balancing group of an Auto Scaling fleet of Amazon EC2 instances that have mounted the Amazon EFS volume. Set the cache-control timeout for 15 minutes in the CloudFront distribution.
C. Store forecast locations in an Amazon ES cluster. Use an Amazon CloudFront distribution targeting an API Gateway endpoint with AWS Lambda functions responding to queries as the origin. Create an Amazon Lambda@Edge function that caches the data locally at edge locations for 15 minutes.
D. Store forecast locations in Amazon S3 as individual objects. Create an Amazon CloudFront distribution targeting an Elastic Load Balancing group of an Auto Scaling fleet of EC2 instances, querying the origin of the S3 object. Set the cache-control timeout for 15 minutes in the CloudFront distribution.

A Solutions Architect must migrate an existing on-premises web application with 70 TB of static files supporting a public open-data initiative. The Architect wants to upgrade to the latest version of the host operating system as part of the migration effort.
Which is the FASTEST and MOST cost-effective way to perform the migration?

A. Run a physical-to-virtual conversion on the application server. Transfer the server image over the internet, and transfer the static data to Amazon S3.
B. Run a physical-to-virtual conversion on the application server. Transfer the server image over AWS Direct Connect, and transfer the static data to Amazon S3.
C. Re-platform the server to Amazon EC2, and use AWS Snowball to transfer the static data to Amazon S3.
D. Re-platform the server by using the AWS Server Migration Service to move the code and data to a new Amazon EC2 instance.