SAP-C02 Amazon Web Services AWS Certified Solutions Architect - Professional Free Practice Exam Questions (2026 Updated)

CloudFront origin failover is implemented by using an origin group with a primary origin and a secondary origin. CloudFront can automatically fail over to the secondary origin when the primary origin fails under configured conditions. The default origin connection behavior can take up to 30 seconds because CloudFront uses multiple connection attempts and timeout values. AWS documentation states that reducing the connection timeout and reducing the number of connection attempts can make failover occur faster. Option C correctly adds the replicated S3 bucket as a second origin, creates an origin group, updates the default cache behavior to use that origin group, and sets both connection timeout and connection attempts to 1. Option B misses updating the cache behavior and uses the wrong timeout. Lambda-based promotion is unnecessary operational overhead.

Amazon Elastic Container Service (ECS) on AWS Fargate is a fully managed service that allows you to run containers without having to manage the underlying infrastructure. When you launch tasks on Fargate, resources are automatically allocated based on the number of tasks running, which reduces the operational overhead.

Using ECS on Fargate allows you to assign custom tags to tasks using the --tags option in the run-task command, as described in the documentation:https://docs.aws.amazon.com/cli/latest/reference/ecs/run-task.htmlYou can also set enableECSManagedTags to true, which allows the service to automatically add the cluster name and service name as tags.https://docs.aws.amazon.com/AmazonECS/latest/developerguide/task-placement-constraints.html#tag-based-scheduling

migrating the data processing script to an AWS Lambda function and using an S3 event notification to invoke the Lambda function to process the objects when the company uploads the objects. This solution meets the company ' s requirements of high availability and scalability, as well as reducing long-term management overhead, and is likely to be the most cost-effective option.

C is correct because the root cause is synchronous chaining of Lambda functions that causes the initial function to wait and time out. AWS Step Functions is designed to orchestrate multiple steps (including Lambda invocations) with managed state, retries, and timeouts at the workflow level. By moving the chaining logic into a Step Functions state machine and removing direct function-to-function invocations, the initial Lambda no longer needs to remain running while waiting for downstream functions. Step Functions can coordinate the sequence (or parallel branches), handle transient failures with retries/backoff, and maintain execution state without requiring a long-lived “waiting” Lambda invocation. This resolves timeouts while preserving the operational and cost advantages of a serverless Lambda-based architecture and typically requires minimal code change compared to rewriting or migrating platforms.

Why the other options are incorrect:

A: Migrating to containers/EKS is a major architectural change and increases operational overhead, violating “minimum application changes” and the desire to keep Lambda benefits.

B: Rewriting in Java is extensive and unnecessary. Adding SQS between functions changes invocation style and error handling and still requires significant redesign (and does not inherently solve orchestration/branching as cleanly as Step Functions).

D: Grouping functions into containers and running on ECS Spot changes the compute model and introduces more operational overhead and larger application changes than adopting Step Functions.

This option allows the solutions architect to use a DeletionPolicy attribute to specify how AWS CloudFormation handles the deletion of an S3 bucket when the stack is deleted1. By setting the value of Delete, the solutions architect can instruct CloudFormation to delete the bucket and all of itscontents1. This option does not require any major changes to the application’s architecture or any additional resources.

Deletion policies

Creating a headless Aurora DB cluster in the secondary Region as part of an Aurora global database ensures continuous, low-latency replication without running active instances in the secondary Region.

This configuration meets the RTO and RPO requirements by allowing rapid promotion of the secondary cluster in a failover scenario.

It also reduces operational costs compared to continuously running standby instances in the secondary Region.

This aligns with AWS best practices for DR and cross-Region resiliency.

The application is business-critical with an SLA requirement of 99.95% uptime and is currently limited by an on-premises data center. The architecture includes a PostgreSQL database and separate business logic and presentation layers. Remote users experience high latency when accessing the application, which is latency-sensitive.

To meet or exceed a 99.95% SLA for the database while minimizing operational burden, a managed database service with built-in high availability is preferred. Amazon Aurora PostgreSQL-compatible editions provide high availability and durability by automatically replicating data across multiple Availability Zones in a Region. Aurora can offer higher availability and faster failover compared to self-managed databases on EC2, and offloads patching, backups, and maintenance.

For the application and presentation layers, running them on Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer preserves the basic architecture (stateless application servers behind a load balancer) while moving to a managed, highly available environment that can scale out and in automatically based on demand. This results in minimal changes to the application components and provides improved resilience compared to on-premises VMs.

Remote users experience slow load times because their desktop clients connect over higher-latency networks to the on-premises servers. By using Amazon AppStream 2.0, the application can be streamed from AWS to end users. AppStream 2.0 runs the desktop application close to the backend database and application servers within AWS, and streams only the display and input over the network. This significantly reduces the effect of latency between the user and the application backend, improving user experience, especially for latency-sensitive desktop applications, without requiring major changes to the application itself.

Option A uses a PostgreSQL database on EC2, which requires the customer to manage availability, backups, patching, and failover. This does not provide the same managed high-availability guarantees as Aurora or RDS Multi-AZ. It also suggests allocating a full Amazon WorkSpaces desktop per user, which can be more expensive and more complex than streaming only the application via AppStream 2.0.

Option C uses Amazon RDS for PostgreSQL with Multi-AZ configuration, which provides high availability and is a valid managed option. However, it runs the application and presentation layers on Fargate containers behind a Network Load Balancer. NLB is typically used for TCP-level load balancing and is better suited for protocols that require low-level handling, whereas HTTP/HTTPS web applications are commonly placed behind an Application Load Balancer, which provides advanced HTTP routing features. Also, ElastiCache can reduce database load and improve response times but does not directly solve the end-user network latency issue in a desktop client scenario.

Option D is incorrect because Amazon Redshift is a data warehouse service, not an operational PostgreSQL-compatible database for transaction processing. It is not appropriate for hosting the application’s primary relational database. CloudFront accelerates delivery of static and cached web content, not interactive desktop client-server traffic.

Therefore, migrating the database to Aurora PostgreSQL, running the application and presentation layers on EC2 Auto Scaling behind an Application Load Balancer, and using Amazon AppStream 2.0 to deliver the application to remote users (option B) meets the availability requirements, improves user experience, requires relatively little change to the application architecture, and minimizes operational costs compared to more complex or less-managed alternatives.

Option A, Deploy the security tool on EC2 instances in a new Auto Scaling group in the existing VPC, allows the company to use its existing security tool while still running it within the AWS environment. This ensures that all packets coming in and out of the VPC are inspected by the security tool in real time. Option D, Provision a Gateway Load Balancer for each Availability Zone to redirect the traffic to the security tool, allows for high availability within an AWS Region. By provisioning a Gateway Load Balancer for each Availability Zone, the traffic is redirected to the security tool in the event of any failures or outages. This ensures that the security tool is always available to inspect the traffic, even in the event of a failure.