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

https://aws.amazon.com/about-aws/whats-new/2022/02/amazon-cloudfront-managed-prefix-list/

Option B is the best answer because separate regional Aurora PostgreSQL clusters keep raw customer PII in the Region where the data is collected, while AWS Glue DataBrew can transform, mask, or redact PII before datasets are used for cross-Region analytics. AWS documentation states that DataBrew can identify and mask PII using techniques such as substitution and shuffling, and AWS Glue sensitive-data transforms can detect, mask, or remove PII entities. Amazon Redshift Serverless then provides a managed analytics layer without requiring the company to operate data warehouse infrastructure. Option D is incorrect because Amazon Macie discovers and helps secure sensitive data in S3; it does not anonymize the data. Option C relies on custom Lambda anonymization and an awkward cross-Region QuickSight database access pattern.

The workload is a real-time leaderboard that needs extremely low latency: microsecond reads and single-digit millisecond writes. It also needs rapid failover (accepting writes in less than a minute after a primary node failure) and minimal operational overhead. Additionally, the data must be durable enough to persist and be used later for analytics through a data pipeline.

Amazon MemoryDB for Redis is a Redis-compatible, durable, in-memory database service designed for microsecond read latency and low-millisecond write latency while also providing durability through a distributed transaction log. MemoryDB is designed to be a primary database rather than a cache and supports Multi-AZ configurations with fast failover. These characteristics match the leaderboard’s latency requirements and the availability requirement to resume writes quickly after a failure. Because MemoryDB is fully managed, it has less operational overhead than running Redis on EC2.

Option C is the best choice because MemoryDB provides Redis compatibility, in-memory performance, Multi-AZ high availability, and durability that supports data persistence for downstream processing. The durability aspect is especially important for feeding analytics pipelines, because the data is not only cached but is retained as a database record.

Option A is not the best choice because Amazon ElastiCache for Redis is primarily used as a cache. While it can be configured for replication and can achieve low latency, it is not positioned as a durable primary data store in the same way as MemoryDB for Redis. For requirements that explicitly combine extremely low latency with persistence and database-like durability, MemoryDB is the more appropriate managed service with lower operational overhead than building custom durability mechanisms.

Option B is not suitable because Amazon RDS for MySQL typically cannot meet microsecond read latency, and write latencies are usually higher than single-digit milliseconds for non-trivial workloads. Also, read replicas do not provide multi-writer capability and do not ensure sub-minute write availability in all failure scenarios without additional architecture and failover handling.

Option D has the highest operational overhead because it requires the company to operate Redis on EC2: instance management, patching, scaling, replication, failover automation, monitoring, and backup/restore processes. This does not meet the requirement for the least operational overhead compared to fully managed services.

Therefore, an Amazon MemoryDB cluster in Multi-AZ mode provides the required ultra-low latency, rapid failover for write availability, durability for persistence, and least operational overhead.

This solution will give the Lambda function access to the internet by routing its outbound traffic through the NAT gateway, which has a public Elastic IP address. This will allow the external provider to whitelist the single public IP address associated with the NAT gateway, and enable the application to access the new service

Deploying a NAT gateway and associating an Elastic IP address with it, and then configuring the VPC to use the NAT gateway, will give the application the ability to access the new service. This is because the NAT gateway will be the single public IP address that the external provider needs for the allow list. The NAT gateway will allow the application to access the service, while keeping the underlying Lambda functions private.

When configuring NAT gateways, you should ensure that the route table associated with the NAT gateway has a route to the internet gateway with a target of the internet gateway. Additionally, youshould ensure that the security group associated with the NAT gateway allows outbound traffic from the Lambda functions.

The best solution is to turn on the Concurrency Scaling feature for the Amazon Redshift cluster. This feature allows the cluster to automatically add additional capacity to handle bursts of read queries without affecting the performance of write queries. The additional capacity is transparent to the users and is billed separately based on the usage. This solution meets the business requirements of servicing read and write queries at all times andis also cost-effective compared to the other options, which involve provisioning additional resources or resizing the cluster. References: Amazon Redshift Documentation, Concurrency Scaling in Amazon Redshift

AWS Application Migration Service:

AWS Application Migration Service (MGN) facilitates the migration of virtual machines (VMs) to AWS without installing additional software on the VMs. This agentless service helps in seamlessly migrating workloads to AWS.

AWS Migration Hub Strategy Recommendations:

AWS Migration Hub Strategy Recommendations offer insights and guidance for planning and implementing migration strategies. It helps in generating a Total Cost of Ownership (TCO) report by automatically importing discovery data from the VMs.

Generating the TCO Report:

The combined use of AWS Application Migration Service and Migration Hub Strategy Recommendations enables the automatic import of discovery data and the generation of an accurate TCO report, ensuring a smooth and cost-effective migration process.

References

AWS Migration Hub Strategy Recommendations​(AWS Documentation)​.

Option A is correct because placing the EC2 instances behind an Application Load Balancer (ALB) and associating an SSL certificate from AWS Certificate Manager (ACM) with the ALB enables encryption in transit between the client and the ALB. Exporting the SSL certificate and installing it on each EC2 instance enables encryption in transit between the ALB and the web server. Configuring the ALB to listen on port 443 and to forward traffic to port 443 on the instances ensures that HTTPS is used for both connections. This solution achieves end-to-end encryption in transit for the web application12

https://aws.amazon.com/kinesis/data-firehose/faqs/

The company should use AWS Amplify to create a static website for uploads of media files. The company should use Amplify Hosting to serve the website through Amazon CloudFront. The company should use Amazon S3 to store the uploaded media files. The company should use Amazon Cognito to authenticate users. This solution will meet the requirements with the least operational overhead because AWS Amplify is a complete solution that lets frontend web and mobile developers easily build, ship, and host full-stack applications on AWS, with the flexibility to leverage the breadth of AWS services as use cases evolve. No cloud expertise needed1. By using AWS Amplify, the company can refactor the application to a serverless architecture that reduces operational complexity and costs. AWS Amplify offers the following features and benefits:

Amplify Studio: A visual interface that enables you to build and deploy a full-stack app quickly, including frontend UI and backend.

Amplify CLI: A local toolchain that enables you to configure and manage an app backend with just a few commands.

Amplify Libraries: Open-source client libraries that enable you to build cloud-powered mobile and web apps.

Amplify UI Components: Open-source design system with cloud-connected components for building feature-rich apps fast.

Amplify Hosting: Fully managed CI/CD and hosting for fast, secure, and reliable static and server-side rendered apps.

By using AWS Amplify to create a static website for uploads of media files, the company can leverage Amplify Studio to visually build a pixel-perfect UI and connect it to a cloudbackend in clicks. By using Amplify Hosting to serve the website through Amazon CloudFront, the company can easily deploy its web app or website to the fast, secure, and reliable AWS content delivery network (CDN), with hundreds of points of presence globally. By using Amazon S3 to store the uploaded media files, the company can benefit from a highly scalable, durable, and cost-effective object storage service that can handle any amount of data2. By using Amazon Cognito to authenticate users, the company can add user sign-up, sign-in, and access control to its web app with a fully managed service that scales to support millions of users3.

The other options are not correct because:

Using AWS Application Migration Service to migrate the application server to Amazon EC2 instances would not refactor the application or accelerate development. AWS Application Migration Service (AWS MGN) is a service that enables you to migrate physical servers, virtual machines (VMs), or cloud servers from any source infrastructure to AWS without requiring agents or specialized tools. However, this would not address the challenges of overutilization and data uploads failures. It would also not reduce operational overhead or costs compared to a serverless architecture.

Creating a static website for uploads of media files and using AWS AppSync to create an API would not be as simple or fast as using AWS Amplify. AWS AppSync is a service that enables you to create flexible APIs for securely accessing, manipulating, and combining data from one or more data sources. However, this would require more configuration and management than using Amplify Studio and Amplify Hosting. It would also not provide authentication features like Amazon Cognito.

Setting up AWS IAM Identity Center (AWS Single Sign-On) to give users the ability to sign in to the application would not be as suitable as using Amazon Cognito. AWS Single Sign-On (AWS SSO) is a service that enables you to centrally manage SSO access and user permissions across multiple AWS accounts and business applications. However, this service is designed for enterprise customers who need to manage access for employees or partners across multiple resources. It is not intended for authenticating end users of web or mobile apps.

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/high_availability_origin_failover.html

This solution will meet the requirements of making the application highly available with the least development time. Creating a new AMI that is configured with SSM Agent will enable the company to use AWS Systems Manager to manage and patch the EC2 instances automatically, reducing downtime and human errors. Using a launch template for an Auto Scaling group will allow the company to launch multiple instances of the same configuration and scale them up or down based on demand. Using smaller instances in the Auto Scaling group will reduce the cost and improve the performance of the application tier. Creating an Application Load Balancer to distribute traffic across the instances in the Auto Scaling group will increase the availability and fault tolerance of the application tier. Migrating the database to Amazon Aurora MySQL will provide a fully managed, compatible, and scalable relational database service that can handle high throughput and concurrent connections.

https://docs.aws.amazon.com/compute-optimizer/latest/APIReference/API_ExportLambdaFunctionRecommendations.html

Since the company is already using Kubernetes manifests,Amazon EKSis a natural fit. It’s a managed Kubernetes control plane and works withAmazon Aurora PostgreSQL, a highly available DB service.

What is EKS?

https://docs.aws.amazon.com/cur/latest/userguide/billing-cur-limits.html

The requirement is to allow each OU (which contains multiple accounts) to view a cost breakdown across its accounts. In an AWS Organizations setup with consolidated billing, the management account is the central place where organization-level cost and usage reporting is configured and aggregated. The Cost and Usage Report is designed to produce the most detailed cost and usage dataset, and it can include charges for linked (member) accounts. A single CUR created from the management account can include all accounts in the organization, and then filtering can be applied to show cost breakdowns per OU or per team.

Once the CUR is delivered to Amazon S3, analytics tools such as Amazon Athena and Amazon QuickSight can be used to create dashboards. Teams can be provided access to the dataset or curated views and can filter by linked account, tags, and other dimensions. With appropriate cost allocation tags and organizational mapping (for example, account-to-OU mapping maintained in reporting logic), each OU can see its consolidated view.

Option B meets the requirements because it creates one CUR centrally from the management account and uses QuickSight for visualization. This approach is scalable for hundreds of accounts and avoids duplicating CUR configuration in each member account.

Option A is incorrect because AWS Resource Access Manager is not used to create CURs per OU. CUR is not an OU-scoped resource created via RAM.

Option C is not operationally efficient because it requires creating and managing CURs in each member account, producing fragmented datasets and significantly increasing overhead at hundreds of accounts. It also makes it harder to generate a single view per OU across accounts.

Option D is incorrect because Systems Manager does not create CURs, and OpsCenter dashboards are not the standard tool for cost and usage reporting.

Therefore, a centrally created CUR in the management account with QuickSight visualization is the correct solution.

RDS Proxyoptimizes and pools DB connections. By combining this withPrivateLink + NLB, other accounts can securely access the proxy endpoint without going over the internet or setting up peering for every new account.

RDS Proxy Docs

Bring your own IP addresses (BYOIP) You can bring part or all of your publicly routable IPv4 or IPv6 address range from your on-premises network to your AWS account. You continue toown the address range, but AWS advertises it on the internet by default. After you bring the address range to AWS, it appears in your AWS account as an address pool. https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-byoip.html AWS Transfer for SFTP enables you to easily move your file transfer workloads that use the Secure Shell File Transfer Protocol (SFTP) to AWS without needing to modify your applications or manage any SFTP servers.https://aws.amazon.com/about-aws/whats-new/2018/11/aws-transfer-for-sftp-fully-managed-sftp-for-s3/

Updating the ingestion process to use Amazon Kinesis Data Firehose to save data to Amazon S3 will reduce the need for EC2 instances and EBS volumes for data storage1. Using AWS Batch with Spot Instances to perform nightly processing will leverage the cost savings of Spot Instances, which are up to 90% cheaper than On-Demand Instances2. AWS Batch will also handle the scheduling and scaling of the processing jobs. Setting the maximum Spot price to 50% of the On-Demand price will reduce the chances of interruption and ensure that the processing is cost-effective.

A. High performance computing (HPC) workload cluster should be in a single AZ.

C. Elastic Fabric Adapter (EFA) is a network device that you can attach to your Amazon EC2 instances to accelerate High Performance Computing (HPC)

F. Amazon FSx for Lustre - Use it for workloads where speed matters, such as machine learning, high performance computing (HPC), video processing, and financial modeling.

Cluster – packs instances close together inside an Availability Zone. This strategy enables workloads to achieve the low-latency network performance necessary for tightly-coupled node-to-node communication that is typical of HPC applications.

https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/placement-groups.html

https://aws.amazon.com/es/blogs/mt/implement-aws-resource-tagging-strategy-using-aws-tag-policies-and-service-control-policies-scps/

This option allows the solutions architect to use session tags to pass additional information about the federated user, such as the development unit name, to AWS1. Session tags are key-value pairs that you can define in your identity provider (IdP) and pass in your SAML assertion1. By using a deny action and a StringNotEquals condition in the IAM policy, you can prevent developers from accessing or modifying EC2 instances that belong to a different development unit2. This way, you can enforce fine-grained access control and prevent accidental or malicious incidents.

Passing session tags in SAML assertions

Using tags for attribute-based access control

The correct answer would be options A, C and D, because they address the requirements outlined in the question. A. Deploying a landing zone environment using AWS Control Tower and enrolling accounts in an organization in AWS Organizations allows for a centralized management of access to all accounts and applications. C. Creating transit gateways and transit gateway VPC attachments in each account and configuring appropriate route tables allows for private network traffic, and ensures that the production account and shared network account have connectivity to all accounts, while the development and staging accounts have access only to each other. D. Setting up and enabling AWS IAM Identity Center (AWS Single Sign-On) and creating appropriate permission sets with required MFA for existing accounts allows for multi-factor authentication at login and specific roles to be assigned to user groups.

C is correct:AWS Compute Optimizeruses machine learning to analyze usage patterns and recommends rightsizing.Trusted Advisoradds further insights. Combining these withSavings Plansgives the best cost optimizationwithout reducing availability.

A is risky due to using Lambda for termination.

B and D offer partial or manual solutions.

The company should create a new AWS WAF web ACL. The company should add a new rule that blocks requests that match the SQL database rule group. The company should set the web ACL to allow all other traffic that does not match those rules. The company should attach the web ACL to the ALB in front of the ECS tasks. This solution will meet the requirements because AWS WAF is a web application firewall that lets you monitor and control web requests that are forwarded to your web applications. You can use AWS WAF to define customizable web security rules that control which traffic can access your web applications and which traffic should be blocked1. By creating a new AWS WAF web ACL, the company can create a collection of rules that define the conditions for allowing or blocking web requests. By adding a new rule that blocks requests that match the SQL database rule group, the company can prevent SQL injection attacks from reaching the ECS API service. The SQL database rule group is a managed rule group provided by AWS that contains rules to protect against common SQL injection attack patterns2. By setting the web ACL to allow all other traffic that does not match those rules, the company can ensure that legitimate traffic can access the API service. By attaching the web ACL to the ALB in front of the ECS tasks, the company can apply the web security rules to all requests that are forwarded by the load balancer.

The other options are not correct because:

Creating a new AWS WAF Bot Control implementation would not prevent SQL injection attacks from reaching the ECS API service. AWS WAF Bot Control is a feature that gives you visibility and control over common and pervasive bot traffic that can consume excess resources, skew metrics, cause downtime, or perform other undesired activities. However, it does not protect against SQL injection attacks, which are malicious attempts to execute unauthorized SQL statements against your database3.

Creating a new AWS WAF web ACL to monitor the HTTP requests and HTTPS requests that are forwarded to the ALB in front of the ECS tasks would not prevent SQL injection attacks from reaching the ECS API service. Monitoring mode is a feature that enables you to evaluate how your rules would perform without actually blocking any requests. However, this mode does not provide any protection against attacks, as it only logs and counts requests that match your rules4.

Creating a new AWS WAF web ACL and creating a new empty IP set in AWS WAF would not prevent SQL injection attacks from reaching the ECS API service. An IP set is a feature that enables you to specify a list of IP addresses or CIDR blocks that you want to allow or block based on their source IP address. However, this approach would not be effective or efficient against SQL injection attacks, as it would require constantly updating the IP set with new IP addresses of attackers, and it would not block attackers who use proxies or VPNs.

Option A is correct because AWS Compute Optimizer provides right-sizing recommendations for Amazon ECS services on AWS Fargate, including task CPU, task memory, and container-level CPU and memory values. Since the workload is deployed through CloudFormation, the correct operational path is to update the ECS task definition in the CloudFormation template and redeploy the stack. That keeps infrastructure as code as the source of truth. Target tracking policies adjust the number of running tasks, not the CPU and memory size assigned to each Fargate task, so option B does not directly fix over-provisioned task resources. Cost Explorer Reserved Instance reports are not the right tool for Fargate task right-sizing because Fargate does not use EC2 Reserved Instances in the same way.

The company has migrated an application to EC2 and will deploy it in an additional Region. The usage is expected to be stable for 3 years, but parts of the application will be redesigned over the next 2 years to use AWS Lambda. Therefore, the company will gradually shift a portion of its compute usage from EC2 to Lambda.

Savings Plans are a flexible pricing model that provide lower prices in exchange for a commitment to a consistent amount of compute usage (measured in dollars per hour) for a 1- or 3-year term. There are two main types of Savings Plans:

• Compute Savings Plans: Apply to any EC2 instance usage regardless of Region, instance family, OS, tenancy, and also apply to AWS Fargate and AWS Lambda usage.

• EC2 Instance Savings Plans: Apply only to a specific instance family in a specific Region.

In this scenario, because there will be a gradual migration from EC2-based compute to Lambda-based compute, a plan that only applies to EC2 (EC2 Instance Savings Plan) risks underutilization as more usage moves to Lambda. Compute Savings Plans, by contrast, can cover EC2 now and Lambda later, maintaining high utilization of the commitment and maximizing effective discount.

Option A recommends evaluating Savings Plans recommendations each year in AWS Cost Management and purchasing a 1-year Compute Savings Plan based on those recommendations. The Cost Management tools and Cost Explorer provide Savings Plans recommendations based on actual usage patterns. Buying 1-year Compute Savings Plans and re-evaluating annually allows the company to adjust its committed spend each year as portions of the application move from EC2 to Lambda. This approach maximizes the chance that the Savings Plans coverage matches the actual combined EC2 and Lambda usage over time and avoids being locked into an EC2-only commitment that becomes increasingly underutilized.

Option B is not optimal because it recommends a 3-year EC2 Instance Savings Plan. That commitment applies only to specific EC2 instance families in a Region and does not apply to Lambda. As the company migrates portions of its workloads to Lambda, the EC2 Instance Savings Plan may become underutilized, reducing the effective discount and potentially increasing overall cost. Compute Optimizer also does not provide Savings Plans purchase recommendations; Savings Plans recommendations come from Cost Management and Cost Explorer.

Option C uses 1-year EC2 Instance Savings Plans and adjusts the commitment each year. While this gives some flexibility, EC2 Instance Savings Plans still do not apply to Lambda usage, so over time there is a risk of underutilizing the EC2 commitment as the Lambda portion grows. This does not maximize savings relative to Compute Savings Plans, which can cover both services.

Option D proposes a 3-year EC2 Instance Savings Plan and suggests decreasing the hourly commitment during the term as workloads move to Lambda. However, Savings Plans commitments cannot be decreased during the term. The company would remain obligated to the original 3-year commitment even if EC2 usage drops, leading to wasted commitment.

Therefore, purchasing 1-year Compute Savings Plans each year based on Savings Plans recommendations from AWS Cost Management (option A) is the best strategy to maximize cost savings while the company transitions part of the workload from EC2 to Lambda.

Converting VPC flow logs to store in Apache Parquet format and specifying hourly partitions significantly improves query performance and reduces storage space usage. Apache Parquet is a columnar storage file format optimized for analytical queries, allowing Athena to scan less data and improve query performance. Partitioning logs by hour further enhances query efficiency by limiting the amount of data scanned during queries, addressing the issue of degrading performance over time due to the growing volume of ingested logs.

AWS Documentation on VPC Flow Logs and Amazon Athena provides insights into configuring VPC flow logs in Apache Parquet format and using Athena for querying log data. This approach is recommended for efficient log analysis and storage optimization.

The company should download the data from the Amazon Redshift tables to an Amazon S3 bucket periodically and use AWS Data Exchange for S3 to share data with customers. The company should configure subscription verification and require the data customers to subscribe to the data product. This solution will meet the requirements with the least operational overhead because AWS Data Exchange for S3 is a feature that enables data subscribers to access third-party data files directly from data providers’ Amazon S3 buckets. Subscribers can easily use these files for their data analysis with AWS services without needing to create or manage data copies. Data providers can easily set up AWS Data Exchange for S3 on top of their existing S3 buckets to share direct access to an entire S3 bucket or specific prefixes and S3 objects. AWS Data Exchange automatically manages subscriptions, entitlements, billing, and payment1.

The other options are not correct because:

Using AWS Data Exchange for APIs to share data with customers would not work because AWS Data Exchange for APIs is a feature that enables data subscribers to access third-party APIs directly from data providers’ AWS accounts. Subscribers can easily use these APIs for their data analysis with AWS services without needing to manage API keys or tokens. Data providers can easily set up AWS Data Exchangefor APIs on top of their existing API Gateway resources to share direct access to an entire API or specific routes and stages2. However, this feature is not suitable for sharing data from Amazon Redshift tables, which are not exposed as APIs.

Creating an Amazon API Gateway Data API service integration with Amazon Redshift would not work because the Data API is a feature that enables you to query your Amazon Redshift cluster using HTTP requests, without needing a persistent connection or a SQL client3. It is useful for building applications that interact with Amazon Redshift, but not for sharing data files with customers.

Creating an AWS Data Exchange datashare by connecting AWS Data Exchange to the Redshift cluster would not work because AWS Data Exchange does not support datashares for Amazon Redshift clusters. A datashare is a feature that enables you to share live and secure access to your Amazon Redshift data across your accounts or with third parties without copying or moving the underlying data4. It is useful for sharing query results and views with other users, but not for sharing data files with customers.

Publishing the Amazon Redshift data to an Open Data on AWS Data Exchange would not work because Open Data on AWS Data Exchange is a feature that enables you to find and use free and public datasets from AWS customers and partners. It is useful for accessing open and free data, but not for confirming the identities of the customers or charging them for the data.