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Amazon SAP-C02 Exam Syllabus Topics:

Section	Weight	Objectives
Design for New Solutions	29%	- Design high-performing and scalable solutions - Design cost-optimized architectures - Design secure and resilient architectures - Design for reliability and fault tolerance
Accelerate Workload Migration and Modernization	20%	- Modernize applications and data platforms - Migrate applications, data, and infrastructure - Integrate AWS services with existing systems - Develop migration strategies and plans
Continuous Improvement for Existing Solutions	25%	- Refactor and modernize legacy applications - Implement monitoring, logging, and observability - Optimize existing workloads for cost, performance, and security - Apply security best practices and compliance updates
Design Solutions for Organizational Complexity	26%	- Design multi-account and multi-region architectures - Design for compliance and governance requirements - Design for operational excellence and automation - Design hybrid and multi-cloud networking

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Amazon AWS Certified Solutions Architect - Professional (SAP-C02) Sample Questions (Q512-Q517):

NEW QUESTION # 512
A company needs to copy backups of 40 RDS for MySQL databases from a production account to a central backup account within AWS Organizations. The databases usedefault AWS-managed KMS encryption keys. The backups must be stored in a WORM (Write Once Read Many)backup account. What is the correct approach to enable cross-account backup?

A. Restore the databases with customer-managed KMS keys and use AWS Backup with cross- account vault sharing.
B. Share the default KMS keys with the central account and create backup vaults in the central account.
C. Use a Lambda function to decrypt and copy the snapshots to the central account.
D. Use a Lambda function to share and re-encrypt snapshots across accounts using the default KMS key.

Answer: A

NEW QUESTION # 513
A company is processing videos in the AWS Cloud by using Amazon EC2 instances in an Auto Scaling group. It takes 30 minutes to process a video. Several EC2 instances scale in and out depending on the number of videos in an Amazon Simple Queue Service (Amazon SQS) queue.
The company has configured the SQS queue with a redrive policy that specifies a target dead-letter queue and a maxReceiveCount of 1. The company has set the visibility timeout for the SQS queue to 1 hour. The company has set up an Amazon CloudWatch alarm to notify the development team when there are messages in the dead-letter queue.
Several times during the day, the development team receives notification that messages are in the dead-letter queue and that videos have not been processed properly. An investigation finds no errors in the application logs.
How can the company solve this problem?

A. Configure scale-in protection for the instances during processing.
B. Update the visibility timeout for the SOS queue to 3 hours.
C. Update the redrive policy and set maxReceiveCount to 0.
D. Turn on termination protection for the EC2 instances.

Answer: B

Explanation:
The best solution for this problem is to update the visibility timeout for the SQS queue to 3 hours. This is because when the visibility timeout is set to 1 hour, it means that if the EC2 instance doesn't process the message within an hour, it will be moved to the dead-letter queue. By increasing the visibility timeout to 3 hours, this should give the EC2 instance enough time to process the message before it gets moved to the dead-letter queue. Additionally, configuring scale-in protection for the EC2 instances during processing will help to ensure that the instances are not terminated while the messages are being processed.

NEW QUESTION # 514
A solutions architect is responsible (or redesigning a legacy Java application to improve its availability, data durability, and scalability. Currently, the application runs on a single high-memory Amazon EC2 instance. It accepts HTTP requests from upstream clients, adds them to an in-memory queue, and responds with a 200 status. A separate application thread reads items from the queue, processes them, and persists the results to an Amazon RDS MySQL instance. The processing time for each item takes 90 seconds on average, most of which is spent waiting on external service calls, but the application is written to process multiple items in parallel.
Traffic to this service is unpredictable. During periods of high load, items may sit in the internal queue for over an hour while the application processes the backlog. In addition, the current system has issues with availability and data loss if the single application node fails.
Clients that access this service cannot be modified. They expect to receive a response to each HTTP request they send within 10 seconds before they will time out and retry the request.
Which approach would improve the availability and durability of (he system while decreasing the processing latency and minimizing costs?

A. Create an Amazon API Gateway REST API that uses Lambda proxy integration to pass requests to an AWS Lambda function. Migrate the core processing code to a Lambda function and write a wrapper class that provides a handler method that converts the proxy events to the internal application data model and invokes the processing module.
B. Create an Amazon API Gateway REST API that uses a service proxy to put items in an Amazon SOS queue. Extract the core processing code from the existing application and update it to pull items from Amazon SOS instead of an in-memory queue. Deploy the new processing application to smaller EC2 instances within an Auto Scaling group that scales dynamically based on the approximate number of messages in the Amazon SOS queue.
C. Modify the application to use Amazon DynamoDB instead of Amazon RDS. Configure Auto Scaling for the DynamoDB table. Deploy the application within an Auto Scaling group with a scaling policy based on CPU utilization. Back the in-memory queue with a memory-mapped file to an instance store volume and periodically write that file to Amazon S3.
D. Update the application to use a Redis task queue instead of the in-memory queue. 8uild a Docker container image for the application. Create an Amazon ECS task definition that includes the application container and a separate container to host Redis. Deploy the new task definition as an ECS service using AWS Fargate, and enable Auto Scaling.

Answer: B

Explanation:
The obvious challenges here are long workloads, scalability based on queue load, and reliability. Almost always the defacto answer to queue related workload is SQS. Since the workloads are very long (90 minutes) Lambdas cannot be used (15 mins max timeout). So, autoscaled smaller EC2 nodes that wait on external services to complete the task makes more sense. If the task fails, the message is returned to the queue and retried.

NEW QUESTION # 515
A company is developing a new on-demand video application that is based on microservices. The application will have 5 million users at launch and will have 30 million users after 6 months. The company has deployed the application on Amazon Elastic Container Service (Amazon ECS) on AWS Fargate. The company developed the application by using ECS services that use the HTTPS protocol.
A solutions architect needs to implement updates to the application by using blue/green deployments. The solution must distribute traffic to each ECS service through a load balancer. The application must automatically adjust the number of tasks in response to an Amazon CloudWatch alarm.
Which solution will meet these requirements?

A. Configure the ECS services to use the blue/green deployment type and a Network Load Balancer.
Implement an Auto Scaling group for each ECS service by using the Cluster Autoscaler.
B. Configure the ECS services to use the blue/green deployment type and an Application Load Balancer.
Implement Service Auto Scaling for each ECS service.
C. Configure the ECS services to use the blue/green deployment type and an Application Load Balancer.
Implement an Auto Seating group for each ECS service by using the Cluster Autoscaler.
D. Configure the ECS services to use the blue/green deployment type and a Network Load Balancer.
Request increases to the service quota for tasks per service to meet the demand.

Answer: C

Explanation:
https://repost.aws/knowledge-center/ecs-fargate-service-auto-scaling

NEW QUESTION # 516
A company has deployed production workloads on Amazon EC2 On-Demand Instances and Amazon RDS for PostgreSQL DB instances in multiple environments. The company has the AWS Business Support plan. A solutions architect must optimize the cost of the workloads without negatively affecting the availability or compute capacity of the workloads. Which solution will meet these requirements?

A. Opt in to AWS Compute Optimizer. Use Compute Optimizer and AWS Trusted Advisor to identify underutilized instances. Implement recommendations from Compute Optimizer, modify instance types, rightsize instances, and apply Auto Scaling groups. Purchase a Compute Savings Plan.
B. Use AWS Cost Explorer recommendations to rightsize underutilized instances. Create billing alerts in Amazon CloudWatch. Replace the EC2 On-Demand Instances with Spot Instances for underutilized instances. Stop any instances that are not in use.
C. Use AWS Cost and Usage Reports to analyze the most expensive instances and usage patterns. Use AWS Lambda to terminate underutilized instances. Purchase Compute Savings Plans for instances for highly utilized workloads.
D. Use AWS Budgets to track spending for each environment. Configure AWS Trusted Advisor cost optimization checks to rightsize instances. Create billing alerts in Amazon CloudWatch. Terminate underutilized instances. Purchase Reserved Instances for highly utilized workloads.

Answer: A

Explanation:
Comprehensive and Detailed
Option C is the most comprehensive and aligns with AWS best practices for cost optimization without compromising availability or performance:
AWS Compute Optimizer provides recommendations to rightsize EC2 instances, EBS volumes, and Lambda functions based on utilization metrics. By opting in, the company can receive tailored suggestions for optimizing resource configurations.
AWS Trusted Advisor offers cost optimization checks, including identifying idle or underutilized resources, which can be rightsized or terminated to reduce costs.
Implementing recommendations from Compute Optimizer and Trusted Advisor ensures that resources are appropriately sized, leading to cost savings without affecting performance.
Auto Scaling groups help maintain application availability and allow automatic scaling of EC2 instances based on demand, ensuring that compute capacity aligns with workload requirements.
Compute Savings Plans provide flexible pricing for EC2, AWS Lambda, and AWS Fargate usage, offering significant savings over On-Demand pricing in exchange for a commitment to a consistent amount of usage.
This approach ensures cost optimization while maintaining the necessary compute capacity and availability for production workloads.
Reference:
AWS Compute Optimizer: Provides recommendations for optimizing AWS resources based on utilization metrics.
AWS Trusted Advisor: Offers real-time guidance to help provision resources following AWS best practices.
AWS Auto Scaling: Automatically adjusts capacity to maintain steady, predictable performance at the lowest possible cost.
AWS Savings Plans: Flexible pricing model offering lower prices compared to On-Demand pricing, in exchange for a commitment to a consistent amount of usage.

NEW QUESTION # 517
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