A company would like to implement a serverless application by using Amazon API Gateway, AWS Lambda, and Amazon DynamoDB. They deployed a proof of concept and stated that the average response time is greater than what their upstream services can accept. Amazon CloudWatch metrics did not indicate any issues with DynamoDB but showed that some Lambda functions were hitting their timeout.
Which of the following actions should the Solutions Architect consider to improve performance? (Choose two.)

A. Configure the AWS Lambda function to reuse containers to avoid unnecessary startup time.
B. Increase the amount of memory and adjust the timeout on the Lambda function. Complete performance testing to identify the ideal memory and timeout configuration for the Lambda function.
C. Create an Amazon ElastiCache cluster running Memcached, and configure the Lambda function for VPC integration with access to the Amazon ElastiCache cluster.
D. Enable API cache on the appropriate stage in Amazon API Gateway, and override the TTL for individual methods that require a lower TTL than the entire stage.
E. Increase the amount of CPU, and adjust the timeout on the Lambda function. Complete performance testing to identify the ideal CPU and timeout configuration for the Lambda function.

A Solutions Architect is redesigning an image-viewing and messaging platform to be delivered as SaaS. Currently, there is a farm of virtual desktop infrastructure (VDI) that runs a desktop image-viewing application and a desktop messaging application. Both applications use a shared database to manage user accounts and sharing. Users log in from a web portal that launches the applications and streams the view of the application on the user’s machine. The Development Operations team wants to move away from using VDI and wants to rewrite the application.
What is the MOST cost-effective architecture that offers both security and ease of management?

A. Run a website from an Amazon S3 bucket with a separate S3 bucket for images and messaging data. Call AWS Lambda functions from embedded JavaScript to manage the dynamic content, and use Amazon Cognito for user and sharing management.
B. Run a website from Amazon EC2 Linux servers, storing the images in Amazon S3, and use Amazon Cognito for user accounts and sharing. Create AWS CloudFormation templates to launch the application by using EC2 user data to install and configure the application.
C. Run a website as an AWS Elastic Beanstalk application, storing the images in Amazon S3, and using an Amazon RDS database for user accounts and sharing. Create AWS CloudFormation templates to launch the application and perform blue/green deployments.
D. Run a website from an Amazon S3 bucket that authorizes Amazon AppStream to stream applications for a combined image viewer and messenger that stores images in Amazon S3. Have the website use an Amazon RDS database for user accounts and sharing.

A photo-sharing and publishing company receives 10,000 to 150,000 images daily. The company receives the images from multiple suppliers and users registered with the service. The company is moving to AWS and wants to enrich the existing metadata by adding data using Amazon Rekognition.
The following is an example of the additional data:

As part of the cloud migration program, the company uploaded existing image data to Amazon S3 and told users to upload images directly to Amazon S3.
What should the Solutions Architect do to support these requirements?

A. Trigger AWS Lambda based on an S3 event notification to create additional metadata using Amazon Rekognition. Use Amazon DynamoDB to store the metadata and Amazon ES to create an index. Use a web front-end to provide search capabilities backed by Amazon ES.
B. Use Amazon Kinesis to stream data based on an S3 event. Use an application running in Amazon EC2 to extract metadata from the images. Then store the data on Amazon DynamoDB and Amazon CloudSearch and create an index. Use a web front-end with search capabilities backed by CloudSearch.
C. Start an Amazon SQS queue based on S3 event notifications. Then have Amazon SQS send the metadata information to Amazon DynamoDB. An application running on Amazon EC2 extracts data from Amazon Rekognition using the API and adds data to DynamoDB and Amazon ES. Use a web front-end to provide search capabilities backed by Amazon ES.
D. Trigger AWS Lambda based on an S3 event notification to create additional metadata using Amazon Rekognition. Use Amazon RDS MySQL Multi-AZ to store the metadata information and use Lambda to create an index. Use a web front-end with search capabilities backed by Lambda.

What combination of steps could a Solutions Architect take to protect a web workload running on Amazon EC2 from DDoS and application layer attacks? (Choose two.)

A. Put the EC2 instances behind a Network Load Balancer and configure AWS WAF on it.
B. Migrate the DNS to Amazon Route 53 and use AWS Shield.
C. Put the EC2 instances in an Auto Scaling group and configure AWS WAF on it.
D. Create and use an Amazon CloudFront distribution and configure AWS WAF on it.
E. Create and use an internet gateway in the VPC and use AWS Shield.

A company has a legacy application running on servers on premises. To increase the application’s reliability, the company wants to gain actionable insights using application logs. A Solutions Architect has been given following requirements for the solution:

• Aggregate logs using AWS.
• Automate log analysis for errors.
• Notify the Operations team when errors go beyond a specified threshold.

What solution meets the requirements?

A. Install Amazon Kinesis Agent on servers, send logs to Amazon Kinesis Data Streams and use Amazon Kinesis Data Analytics to identify errors, create an Amazon CloudWatch alarm to notify the Operations team of errors
B. Install an AWS X-Ray agent on servers, send logs to AWS Lambda and analyze them to identify errors, use Amazon CloudWatch Events to notify the Operations team of errors.
C. Install Logstash on servers, send logs to Amazon S3 and use Amazon Athena to identify errors, use sendmail to notify the Operations team of errors.
D. Install the Amazon CloudWatch agent on servers, send logs to Amazon CloudWatch Logs and use metric filters to identify errors, create a CloudWatch alarm to notify the Operations team of errors.

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