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

Option B is correct because CloudFormation Git sync is built specifically to connect CloudFormation stacks to source-controlled templates and deployment files in Git repositories. It supports GitHub Enterprise as a provider, can monitor repository changes, and can update stacks after approved changes are merged. AWS documentation states that Git sync works with pull requests and can post comments explaining what changes will be made before a stack update. This directly satisfies the requirement for automatic change set visibility on pull requests without building custom CodePipeline logic or manually processing templates. Option A introduces a custom pipeline, which the question explicitly rejects. Drift detection compares deployed resources against expected configuration; it does not create pull-request change sets. Lambda Hooks evaluate resources during CloudFormation operations but do not provide native Git pull-request synchronization.

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.

A is correct because when a Lambda function is configured to run inside a VPC, it uses ENIs in the selected subnets and does not receive a public IPv4 address. To reach the public internet from private subnets, outbound IPv4 traffic must go through a NAT gateway (or NAT instance). A NAT gateway uses an Elastic IP address (EIP) for its public source address. Therefore, if the external provider requires public IPv4 allowlisting, the company should place the Lambda function in private subnets with a route to the NAT gateways, then provide the provider with the EIPs of the NAT gateways that will be used for egress. This satisfies the requirement to provide connectivity details in advance and ensures the provider sees only the approved public IPv4 addresses.

Why the other options are incorrect:

B (egress-only internet gateway): An egress-only internet gateway is for IPv6 outbound-only traffic, not IPv4. It does not provide an IPv4 address to allowlist, and it is not used to solve “public IPv4 allowlist” requirements.

C (associate EIP with an internet gateway): You cannot associate an Elastic IP address with an internet gateway. Internet gateways do not have a single, customer-assigned EIP for all egress. Also, placing Lambda in public subnets does not automatically give Lambda a stable public IP; Lambda in a VPC still uses private IPs on ENIs and would need NAT (for private subnet egress) or different architecture for stable egress IPs.

D (ENA for Lambda): Lambda does not support attaching a custom ENA device or installing an ENA driver via a Lambda layer to control a stable public source IP. Lambda’s VPC networking is managed by AWS; you cannot present an ENI’s private IP as an internet-routable public IPv4 allowlist address.

Amazon S3 Intelligent-Tiering is a storage class that automatically moves objects between two access tiers based on changing access patterns. Objects that are accessed frequently are stored in the frequent access tier and objects that are accessed infrequently are stored in the infrequent access tier. This allows for cost optimization without requiring manual intervention. This makes it an ideal solution for the scenario described, as it can automatically move objects that are infrequently accessed after 30 days to a lower-cost storage tier while still maintaining millisecond retrieval availability.

The compute layer uses AWS Fargate, so the correct savings instrument is a Compute Savings Plan, not an EC2 Instance Savings Plan. AWS states that Compute Savings Plans automatically apply to EC2, Fargate, and Lambda usage, while EC2 Instance Savings Plans are restricted to EC2 instance usage in a selected family and Region. The database layer is Aurora MySQL, so a Reserved DB Instance is appropriate only for the predictable production database, not for development and testing databases that are regularly shut down. A 1-year commitment also matches the stated one-year predictable production demand. Buying 3-year commitments for all accounts would overcommit spend, especially for non-production environments with intermittent usage. Therefore, centralized 1-year production database reservations plus a 1-year Compute Savings Plan is the most cost-effective match.

Automating the process of increasing service quotas for Amazon EC2 instances in new AWS accounts with minimal operational overhead can be effectively achieved by using Amazon EventBridge, Amazon SNS, and AWS Lambda. An EventBridge rule can detect the creation of a new account and trigger an SNS topic, which in turn invokes a Lambda function. This function can then programmatically request a service quota increase for EC2 instances using the AWS Service Quotas API. This approach streamlines the process, reduces manual intervention, and ensures that new accounts are automatically configured with the desired service quotas.

Amazon EventBridge Documentation: Provides guidance on setting up event rules for detecting AWS account creation.

AWS Lambda Documentation: Details how to create and configure Lambda functions to perform automated tasks, such as requesting service quota increases.

AWS Service Quotas Documentation: Offers information on managing and requesting increases for AWS service quotas programmatically.

The correct answer is A.

A. This solution meets the requirements because it allows the company to create different usage plans for each customer, with different request quotas and time periods. The usage plans can be associated with API keys, which can be distributed to the users of each customer. The API Gateway REST API can invoke the Lambda function using a proxy integration, which passes the request data to the function as input and returns the function output as the response.This solution is scalable, secure, and cost-effective12

B. This solution is incorrect because API Gateway HTTP APIs do not support usage plans or API keys.These features are only available for REST APIs3

C. This solution is incorrect because it does not provide a way to enforce request quotas for each customer. Lambda function aliases can be used to create different versions of the function, but they do not have any quota mechanism.Moreover, this solution exposes the Lambda function URLs directly to the customers, which is not secure or recommended4

D. This solution is incorrect because it does not provide a way to differentiate between customers or users. AWS WAF rate-based rules can be used to limit requests based on IP addresses, but they do not support any other criteria such as user agents or headers.Moreover, this solution adds unnecessary complexity and cost by using an ALB and a VPC56