A Solutions Architect must update an application environment within AWS Elastic Beanstalk using a blue/ green deployment methodology. The Solutions Architect creates an environment that is identical to the existing application environment and deploys the application to the new environment.
What should be done next to complete the update?
A. Redirect to the new environment using Amazon Route 53
B. Select the Swap Environment URLs option
C. Replace the Auto Scaling launch configuration
D. Update the DNS records to point to the green environment
A Solutions Architect is migrating a 10 TB PostgreSQL database to Amazon RDS for PostgreSQL. The company's internet link is 50 MB with a VPN in the Amazon VPC, and the Solutions Architect needs to migrate the data and synchronize the changes before the cutover. The cutover must take place within an 8-day period. What is the LEAST complex method of migrating the database securely and reliably?
A. Order an AWS Snowball device and copy the database using the AWS DMS. When the database is available in Amazon S3, use AWS DMS to load it to Amazon RDS, and configure a job to synchronize changes before the cutover.
B. Create an AWS DMS job to continuously replicate the data from on premises to AWS. Cutover to Amazon RDS after the data is synchronized.
C. Order an AWS Snowball device and copy a database dump to the device. After the data has been copied to Amazon S3, import it to the Amazon RDS instance. Set up log shipping over a VPN to synchronize changes before the cutover.
D. Order an AWS Snowball device and copy the database by using the AWS Schema Conversion Tool. When the data is available in Amazon S3, use AWS DMS to load it to Amazon RDS, and configure a job to synchronize changes before the cutover.
A bank is re-architecting its mainframe-based credit card approval processing application to a cloud-native application on the AWS cloud. The new application will receive up to 1,000 requests per second at peak load. There are multiple steps to each transaction, and each step must receive the result of the previous step. The entire request must return an authorization response within less than 2 seconds with zero data loss. Every request must receive a response. The solution must be Payment Card Industry Data Security Standard (PCI DSS)-compliant. Which option will meet all of the bank's objectives with the LEAST complexity and LOWEST cost while also meeting compliance requirements?
A. Create an Amazon API Gateway to process inbound requests using a single AWS Lambda task that performs multiple steps and returns a JSON object with the approval status. Open a support case to increase the limit for the number of concurrent Lambdas to allow room for bursts of activity due to the new application.
B. Create an Application Load Balancer with an Amazon ECS cluster on Amazon EC2 Dedicated Instances in a target group to process incoming requests. Use Auto Scaling to scale the cluster out/in based on average CPU utilization. Deploy a web service that processes all of the approval steps and returns a JSON object with the approval status.
C. Deploy the application on Amazon EC2 on Dedicated Instances. Use an Elastic Load Balancer in front of a farm of application servers in an Auto Scaling group to handle incoming requests. Scale out/in based on a custom Amazon CloudWatch metric for the number of inbound requests per second after
measuring the capacity of a single instance.
D. Create an Amazon API Gateway to process inbound requests using a series of AWS Lambda processes, each with an Amazon SQS input queue. As each step completes, it writes its result to the next step's queue. The final step returns a JSON object with the approval status. Open a support case to increase the limit for the number of concurrent Lambdas to allow room for bursts of activity due to the new application.
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 t2.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 records is being processed.
What changes should make the bid processing more reliable?
A. 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.
B. 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 processes each bid as soon as a user submits it.
C. 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 the SQS queue. Place the bid processing EC2 instance in an Auto Scaling group with a minimum and a maximum size of 1.
D. 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.
A Solutions Architect is designing a system that will collect and store data from 2,000 internet-connected
sensors. Each sensor produces 1 KB of data every second. The data must be available for analysis within
a few seconds of it being sent to the system and stored for analysis indefinitely.
Which is the MOST cost-effective solution for collecting and storing the data?
A. Put each record in Amazon Kinesis Data Streams. Use an AWS Lambda function to write each record to an object in Amazon S3 with a prefix that organizes the records by hour and hashes the record's key. Analyze recent data from Kinesis Data Streams and historical data from Amazon S3.
B. Put each record in Amazon Kinesis Data Streams. Set up Amazon Kinesis Data Firehouse to read records from the stream and group them into objects in Amazon S3. Analyze recent data from Kinesis Data Streams and historical data from Amazon S3.
C. Put each record into an Amazon DynamoDB table. Analyze the recent data by querying the table. Use an AWS Lambda function connected to a DynamoDB stream to group records together, write them into objects in Amazon S3, and then delete the record from the DynamoDB table. Analyze recent data from the DynamoDB table and historical data from Amazon S3
D. Put each record into an object in Amazon S3 with a prefix what organizes the records by hour and hashes the record's key. Use S3 lifecycle management to transition objects to S3 infrequent access storage to reduce storage costs.
Analyze recent and historical data by accessing the data in Amazon S3
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.
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