Amazon Web Services AWS Certified Solutions Architect - Professional SAP-C02 Exam Dumps: Updated Questions & Answers (July 2026)

The correct answer is B. This option uses IAM Identity Center to create permission sets that map to the existing IAM roles in each account. This way, the company can leverage the existing policies and roles that are already configured for the expected activities of each group of users in each account. The company also needs to create a customer managed policy that allows the group to assume the roles and attach it to thepermission set. This policy grants the necessary permissions for IAM Identity Center to assume the roles on behalf of the users. Finally, the company can assign user access to the AWS accounts in IAM Identity Center, which will automatically create IAM users and groups in each account based on the permission sets.

Option A is incorrect because it requires creating new policies in each account and giving them the same name. This is not necessary and adds complexity and overhead. The company can use the existing IAM roles and policies that are already configured for each account.

Option C is incorrect because it requires creating new policies in each account and giving them unique names. This is also not necessary and adds complexity and overhead. The company can use the existing IAM roles and policies that are already configured for each account.

Option D is incorrect because it requires adding the OU to the accounts configuration in IAM Identity Center. This is not supported by IAM Identity Center, which only allows adding individual accounts or all accounts in an organization.

The company should use Migration Evaluator to generate a list of servers and build a report for a business case. The company should use AWS Migration Hub to view the portfolio and use AWS Application Discovery Service to gain an understanding of application dependencies. This solution will meet the requirements because Migration Evaluator is a migration assessment service that helps create a data-driven business case for AWS cloud planning and migration. Migration Evaluator provides a clear baseline of what the company is running today and projects AWS costs based on measured on-premises provisioning and utilization1. Migration Evaluator can use an agentless collector to conduct broad-based discovery or securely upload exports from existinginventory tools2. Migration Evaluator integrates with AWS Migration Hub, which is a service that provides a single location to track the progress of applicationmigrations across multiple AWS and partner solutions3. Migration Hub also supports AWS Application Discovery Service, which is a service that helps systems integrators quickly and reliably plan application migration projects by automatically identifying applications running in on-premises data centers, their associated dependencies, and their performance profile4.

https://aws.amazon.com/migration-evaluator/

https://docs.aws.amazon.com/migration-evaluator/latest/userguide/what-is.html

https://aws.amazon.com/migration-hub/

https://aws.amazon.com/application-discovery/

https://aws.amazon.com/server-migration-service/

https://aws.amazon.com/dms/

https://docs.aws.amazon.com/controltower/latest/userguide/what-is-control-tower.html

https://aws.amazon.com/application-migration-service/

https://aws.amazon.com/storagegateway/

Option D provisions a new Amazon Connect instance with all existing users and contact flows in a second Region. It also sets up an Amazon Route 53 health check for the URL of the Amazon Connect instance, an Amazon CloudWatch alarm for failed health checks, and an AWS Lambda function to deploy an AWS CloudFormation template that provisions claimed phone numbers. This option allows for the fastest recovery time because all the necessary components are already provisioned and ready to go in the second Region. In the event of a disaster, the failed health check will trigger the AWS Lambda function to deploy the CloudFormation template to provision the claimed phone numbers, which is the only missing component.

https://aws.amazon.com/blogs/architecture/create-dynamic-contact-forms-for-s3-static-websites-using-aws-lambda-amazon-api-gateway-and-amazon-ses/

By creating an AMI that has Grafana pre-installed and storing the existing dashboards in Amazon Elastic File System (Amazon EFS) it allows for faster and more efficient scaling, and by creating an Auto Scaling group that uses the new AMI and setting the Auto Scaling group ' s minimum, desired, and maximum number of instances to one and creating an Application Load Balancer that serves at least two Availability Zones, it ensures high availability and minimized downtime.

This solution will allow the external auditors to have read-only access to the company ' s AWS account while being compliant with AWS security best practices. By creating an IAM role, which is a secure and flexible way of granting access to AWS resources, and trusting the auditors ' AWS account, the company can ensure that the auditors only have the permissions that are required for their role and nothing more. Assigning a unique external ID to the role ' s trust policy, it will ensure that only the auditors ' AWS account can assume the role.

Option C is correct because using Auto Scaling groups for the backend services allows the company to scale up or down the number of EC2 instances based on the demand and traffic. This way, the backend services can handle more requests during peak seasons without compromising performance or availability. Using DynamoDB auto scaling allows the company to adjust the provisioned read and write capacity of the table or index automatically based on the actual traffic patterns. This way, the table or index can handle sudden increases or decreases in workload without throttling or overprovisioning1.

Option A is incorrect because migrating the backend services to AWS Lambda may require significant development effort to rewrite the code and test the functionality. Moreover, increasing the read and write capacity of DynamoDB manually may not be efficient or cost-effective, as it does not account for the variability of the workload. The company may end up paying for unused capacity or experiencing throttling if the workload exceeds the provisioned capacity1.

Option B is incorrect because migrating the backend services to AWS Lambda may require significant development effort to rewrite the code and test the functionality. Moreover, configuring DynamoDB to use global tables may not be necessary or beneficial for the company, as global tables are mainly used for replicating data across multiple AWS Regions for fast local access and disaster recovery. Global tables do not automatically scale the provisioned capacity of each replica table; they still require manual or auto scaling settings2.

Option D is incorrect because using Amazon Simple Queue Service (Amazon SQS) and an AWS Lambda function to write to DynamoDB may introduce additional complexity and latency to the application architecture. Amazon SQS is a message queue service that decouples and coordinates the components of a distributed system. AWS Lambda is a serverless compute service that runs code in response to events. Using these services may require significant development effort to integrate them with the backend services and DynamoDB. Moreover, they may not improve the read performance of DynamoDB, which may also be affected by high traffic3.

Auto Scaling groups

DynamoDB auto scaling

AWS Lambda

DynamoDB global tables

AWS Lambda vs EC2: Comparison of AWS Compute Resources - Simform

Managing throughput capacity automatically with DynamoDB auto scaling - Amazon DynamoDB

AWS Aurora Global Database vs. DynamoDB Global Tables

Amazon Simple Queue Service (SQS)

The data scientists’ EC2 instances in a separate account must access S3 data in a controlled way that satisfies the policy: only authorized networks may access the IoT data. “Authorized networks” in this context means traffic must originate from approved VPCs and must not traverse the public internet.

First, traffic from the EC2 instances to S3 must stay on the AWS network without using public endpoints. A gateway VPC endpoint for S3 in the data scientists’ VPC (option A) allows EC2 instances in that VPC to reach S3 over private connectivity. When using a gateway VPC endpoint, the route tables of the subnets associated with the endpoint automatically route S3 traffic through the endpoint. This ensures that access to S3 is from an authorized network (the VPC) instead of from the public internet.

Second, access must be constrained such that only calls made through the intended S3 access mechanism are allowed. The data lake bucket already has an S3 access point. S3 access points can be restricted by policy and can be referenced in bucket policies through the s3:DataAccessPointArn condition key. By using an S3 bucket policy that allows s3:GetObject only when s3:DataAccessPointArn matches the expected access point ARN (option E), the company can enforce that all valid access uses the approved access point configuration. Combined with the access point’s own network configuration and the VPC endpoint, access is limited to authorized networks.

Option B, blocking public access on the access point, is a good general security practice but does not by itself guarantee that access is restricted to authorized VPC networks. The main enforcement must be through VPC endpoints and bucket/access point policies.

Option C denies s3:GetObject when s3:DataAccessPointArn is a valid access point ARN, which is the opposite of what is required. This would prevent intended access via the access point rather than allow it.

Option D is not correct because gateway VPC endpoints for S3 are configured via endpoint associations with route tables, not by directly routing to an access point in the route table. Traffic to S3 is routed to the VPC endpoint, and the endpoint plus S3 policies determine access.

Therefore, creating a gateway VPC endpoint for S3 in the data scientists’ VPC (option A) and using a bucket policy condition on s3:DataAccessPointArn to allow access only through the authorized access point (option E) together meet the requirement of secure, network-restricted access from authorized networks.

This solution uses a combination of AWS Resource Access Manager (RAM) and automated backups to migrate the Lambda functions and the Aurora database to theTarget account while minimizing downtime. In this solution, the Lambda function deployment package is downloaded from the Source account and used to create new Lambda functions in the Target account. The Aurora DB cluster is shared with the Target account using AWS RAM and the Target account is granted permission to clone the Aurora DB cluster, allowing for a new copy of the Aurora database to be created in the Target account. This approach allows for the data to be migrated to the Target account while minimizing downtime, as the Target account can use the cloned Aurora database while the original Aurora database continues to be used in the Source account.

The modernized application needs to store and serve video files up to 4 GB. Large binary content should not be stored directly in a database because it negatively affects database performance, scaling, and backup/restore times. The standard AWS pattern is to store large objects in Amazon S3 and keep only references (such as keys or URLs) in the database.

User profile data is simple, text-based, and currently lives in a single table. This is a good fit for a key-value or document-style data model such as Amazon DynamoDB, which provides fully managed horizontal scaling and predictable performance at scale.

Option B migrates the profile table to DynamoDB using AWS DMS with AWS Schema Conversion Tool (SCT). Video files are stored as objects in Amazon S3, which is designed for large, durable, and highly scalable object storage. The DynamoDB item stores only the S3 key that corresponds to each user’s video, keeping the database small and responsive. S3 can scale rapidly and independently of the database, and offloads the heavy I/O load of large objects away from the transactional store, so overall application performance is not negatively impacted by video storage or retrieval.

Option A stores the videos as base64-encoded strings in a TEXT column in a relational database. Storing multi-gigabyte objects directly in a relational database table leads to large row sizes, larger indexes, longer backup times, and degraded performance as the table grows. This does not meet the requirement to avoid affecting application performance.

Option C uses Amazon Keyspaces (for Apache Cassandra) as the profile store and S3 for video. While storing videos in S3 is correct, Keyspaces is typically chosen when there is already a Cassandra-based workload or a need for Cassandra-compatible interfaces. For a simple single-table user profile store being migrated from MySQL, DynamoDB is the more natural, managed choice and aligns better with AWS migration and modernization guidance.

Option D stores the videos as base64-encoded strings in DynamoDB items. DynamoDB has a maximum item size of 400 KB, which is far smaller than the 4 GB video size requirement. This makes option D technically infeasible.

Therefore, migrating user profile data to DynamoDB and storing video files in Amazon S3, with S3 keys stored in the corresponding DynamoDB items (option B), provides scalable video storage and protects application performance.

Answer B (AWS Global Accelerator) best meets both requirements: fastest failover from application failures and the lowest response time for global users. Global Accelerator is designed for global, latency-sensitive, highly available applications. It uses static Anycast IP addresses so users enter the AWS network at the nearest edge location, then traffic is carried over the AWS global backbone to the closest healthy regional endpoint (in this case, each Region’s ALB). It continuously performs health checks and can rapidly shift traffic away from unhealthy endpoints, which helps “handle application failures quickly” without being constrained by DNS caching behavior.

Why the other options are less suitable:

C (Route 53 latency-based routing + health checks) can improve routing and failover, but it is still DNS-based. DNS-based failover can be delayed by DNS resolver caching and TTL propagation, meaning failover may not be as fast or as consistent as Global Accelerator for rapid recovery.

A (CloudFront with origin group) can provide origin failover and performance benefits mainly for cacheable or edge-optimizable content. For a payment processing API (typically dynamic and not cache-friendly), CloudFront is not the primary service for achieving the least response time globally with fast multi-Region failover compared with Global Accelerator’s Anycast + edge routing model.

D (AWS Cloud WAN) addresses private network connectivity and centralized network policy across Regions/VPCs. It does not directly optimize public user-to-application latency or provide the same kind of edge-based, Anycast-driven rapid failover for internet-facing API endpoints.

The existing application already runs in containers on a self-managed container platform, and compliance requires proprietary agent software on each container platform node. Amazon EKS with managed node groups is the best fit because it preserves the Kubernetes operating model and allows custom AMIs for worker nodes, where the agent can be pre-installed. This minimizes migration effort because Kubernetes manifests and container deployment patterns can remain largely intact. AWS DMS is appropriate for migrating the existing MySQL database to Amazon RDS for MySQL, and a Multi-AZ RDS deployment improves availability with less operational overhead than managing source-replica VMs. Option D is invalid because Fargate does not allow custom AMIs or node-level agents. Option A requires more self-management. Option C changes the application architecture too aggressively. (AWS Documentation)

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The requirement is to reduce application latency for a globally distributed user base, with users primarily in the United States but increasing usage in Europe and the Middle East. The application is read-heavy and uses Amazon DynamoDB as its data store. Reducing latency for global users requires placing both the application compute and the data closer to users in each Region.

Option D uses the correct multi-Region architecture. Deploying the application to multiple AWS Regions places Amazon EKS workloads closer to end users. Converting the DynamoDB table to a global table creates fully managed, multi-Region replicas of the data with low-latency replication. This allows reads to be served locally from the nearest DynamoDB replica, significantly reducing read latency for users outside the primary Region. Because the workload is read-heavy, this design is particularly effective.

Route 53 geolocation routing directs users to the Region closest to them based on their geographic location. Combined with health checks and Evaluate Target Health, this ensures users are routed to the nearest healthy Region, improving both latency and availability.

Option A is incorrect because global secondary indexes do not replicate data across Regions. GSIs operate within a single Region and are used to support alternative query patterns, not global data distribution. Additionally, Route 53 failover routing is designed for disaster recovery, not for performance optimization or global load balancing.

Option B uses DynamoDB Accelerator (DAX), which improves read latency by caching data in memory, but DAX is Region-specific and does not reduce cross-Region network latency for users in Europe and the Middle East accessing a US-based application. It also does not address application-level latency caused by distance to the EKS cluster.

Option C is not valid because DynamoDB does not support read replicas in the same way relational databases do. DynamoDB replication for latency and global access is achieved through global tables, not through read replicas in a single Region. CloudFront can reduce latency for static or cacheable HTTP content, but it does not solve backend application latency or DynamoDB access latency for dynamic, read-heavy requests.

Therefore, deploying the application and DynamoDB as global tables across Regions and using Route 53 geolocation routing is the correct solution to minimize latency for all global users.

Transit GW + Direct Connect GW + Transit VIF + enabled SiteLink if two different DX locationshttps://aws.amazon.com/blogs/networking-and-content-delivery/introducing-aws-direct-connect-sitelink/

https://docs.aws.amazon.com/sns/latest/dg/sns-cross-region-delivery.html