Free Practice Questions for Amazon Web Services SAP-C02 Exam

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.

The company should configure Amazon FSx for Lustre with an import and export policy. The company should link the new file system to an S3 bucket. The company should install the Lustre client and mount the document store to an Amazon EC2 instance by using NFS. This solution will meet the requirements with the least amount of effort because Amazon FSx for Lustre is a fully managed service that provides a high-performance filesystem optimized for fast processing of workloads such as machine learning, highperformance computing, video processing, financial modeling, and electronic design automation1. Amazon FSx for Lustre can be linked to an S3 bucket and can import data from and export data to the bucket2. The import and export policy can be configured to automatically import new or changed objects from S3 and export new or changed files to S33. This will ensure that the files are available to the public for download within 30 minutes. Amazon FSx for Lustre supports NFS version 3.0 protocol for Linux clients.

The other options are not correct because:

Migrating the application to an AWS Lambda function would require a lot of effort and may not be feasible for the existing server that generates many documents. Lambda functions have limitations on execution time, memory, disk space, and network bandwidth.

Setting up an Amazon S3 File Gateway would not work because S3 File Gateway does not support write-back caching, which means that files written to the file share are uploaded to S3 immediately and are not available locally until they are downloaded again. This would not provide fast local access to the files that the server generates and modifies.

Configuring AWS DataSync to connect to an Amazon EC2 instance would not meet the requirement of making the files available to the public for download within 30 minutes. DataSync is a service that transfers data between on-premises storage systems and AWS storage services over the internet or AWS Direct Connect. DataSync tasks can be scheduled to run at specific times or intervals, but they are not triggered by file changes.

Utilizing an IP access control group rule with the list of public addresses from branch offices and associating it with the Amazon WorkSpaces directory is the most operationally efficient solution. This method ensures that access to WorkSpaces is restricted to specified locations, aligning with the corporate security policy. This approach offers simplicity and flexibility, especially with the potential addition of a new branch office, as updating the IP access control group is straightforward.

AWS Documentation on Amazon WorkSpaces and IP Access Control Groups provides detailed instructions on how to implement access restrictions based on IP addresses. This solution aligns with best practices for securing virtual desktops while maintaining operational efficiency.

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

For a development environment that requires frequent resource recreation and connectivity to applications hosted in a shared services account, the most efficient solution involves using AWS Resource Access Manager (RAM) and the transit gateway in the shared services account. By turning on automatic acceptance for the transit gateway in the shared services account and sharing it with the development account through AWS RAM, the development team can easily recreate their connection as needed without manual intervention. This setup allows for scalable, flexible connectivity between accounts while minimizing operational overhead and ensuring consistent access to shared services.

AWS Documentation on AWS Resource Access Manager and Transit Gateway provides guidance on sharing network resources across AWS accounts and enabling automatic acceptance for transit gateway attachments. This approach is also supported by AWS best practices for multi-account strategies using AWS Organizations and network architecture.

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

https://aws.amazon.com/blogs/compute/implementing-canary-deployments-of-aws-lambda-functions-with-alias-traffic-shifting/

Customer-managed prefix lists — Sets of IP address ranges that you define and manage. You can share your prefix list with other AWS accounts, enabling those accounts to reference the prefix list in their own resources.https://docs.aws.amazon.com/vpc/latest/userguide/managed-prefix-lists.html

a VPC prefix list is created in the transit account with all of the internal IP address ranges, and then shared to all of the other accounts using AWS Resource Access Manager. This allows for central management of the IP address ranges, and eliminates the need for manual updates to security group rules in each account. This solution also allows for compliance checks to be run using AWS Config and for any non-compliant security groups to be automatically remediated.

https://docs.aws.amazon.com/apigateway/latest/developerguide/api-gateway-overview-developer-experience.html

The company wants three things: minimize client-to-broker latency within the same Availability Zone, increase available bandwidth, and increase the scaling speed of the MSK cluster. The current brokers are Standard brokers (two per AZ). Clients use default settings, which means they are not explicitly configured for rack awareness or AZ affinity.

A common way to reduce latency in multi-AZ Kafka deployments is to enable rack awareness on clients and brokers so clients prefer brokers in the same “rack,” which can map to an Availability Zone. In Kafka, the client.rack setting allows the client to include rack information so the broker can return metadata that helps the client select replicas that are closest, reducing cross-AZ traffic and improving latency.

To increase bandwidth and improve scaling speed, the most direct approach in the choices is to move from Standard brokers to Express brokers. Express brokers are designed to provide higher throughput and faster scaling characteristics compared to standard broker types. Since the question explicitly calls out increasing available bandwidth and scaling speed, the broker type change is the key lever, and it can be combined with client.rack configuration to minimize cross-AZ latency.

Option C matches these requirements: it replaces Standard brokers with Express brokers (to improve throughput/bandwidth and scaling speed) and sets client.rack to the Availability Zone identifier (az_id) to improve locality and reduce latency between clients and brokers in the same AZ.

Option A is not appropriate because MSK does not use EC2 Auto Scaling predictive scaling in that manner, and Kafka clients/brokers are not “associated” with an EC2 placement group as a primary latency solution in MSK. Placement groups are for EC2 instance placement; MSK broker placement is managed by the service.

Option B introduces a proxy layer and MSK Connect in a way that increases complexity and does not directly guarantee lower latency or higher bandwidth. MSK Connect is for Kafka Connect workloads, not as a general-purpose low-latency routing proxy for Kafka clients. Cruise Control is used for partition rebalancing and cluster optimization, but it does not replace the benefits of higher-throughput broker types and client rack awareness for AZ locality.

Option D increases broker size and introduces PrivateLink endpoints. PrivateLink is about private connectivity from VPCs to services and does not inherently ensure AZ-local broker selection or reduce latency between clients and brokers in the same AZ. Also, resizing to xlarge increases capacity but does not address scaling speed and locality as directly as express brokers plus rack configuration.

Therefore, option C best meets all requirements.

"The S3 buckets have millions of objects" If there are million of objects then you should use Batch operations. https://aws.amazon.com/blogs/storage/encrypting-objects-with-amazon-s3-batch-operations/

https://docs.aws.amazon.com/systems-manager/latest/userguide/systems-manager-patch.html

C: Multipart uploads can leave incomplete parts behind, which incur storage costs. Expiring them after 7 days minimizes waste and saves cost.

E: Since objects are infrequently accessed after 180 days, transitioning toS3 Standard-IAis cost-effective, especially for large files >128 KB (your 100 MB+ files qualify).

Ais for shifting download cost, not reducing your S3 storage expenses.

Bhelps with upload speed butincreases cost.

Dis too aggressive; Glacier is not suited for access patterns within the first few days.

The workload uses EC2 Spot Instances, which are subject to capacity availability constraints. Using a single instance type significantly increases the risk of launch failures when Spot capacity for that instance type is constrained. This directly impacts application reliability and increases user wait times.

Attribute-based instance type selection allows Auto Scaling to choose from multiple compatible instance types based on defined requirements such as vCPU count, memory, and architecture. This improves the probability that Spot capacity is available at launch time because Auto Scaling can select from a broader pool of instance types instead of a single one.

Option D correctly applies attribute-based instance type selection within a new version of the existing launch template. Auto Scaling groups are designed to work with launch templates, and updating the template version is the modern and recommended approach. This change increases Spot placement flexibility and reliability without altering application behavior or introducing additional infrastructure.

Option A is incorrect because launch configurations are an older construct and do not support attribute-based instance type selection. They are also not recommended for new or updated Auto Scaling designs.

Option B does not address the root cause of Spot launch failures. Simply using a larger instance type may actually reduce available Spot capacity and worsen reliability.

Option C increases the number of placement groups, but placement groups do not solve Spot capacity shortages. They influence instance placement for networking or latency characteristics, not Spot availability across instance types.

Therefore, updating the launch template to use attribute-based instance type selection is the most effective way to improve reliability for a Spot-based Auto Scaling workload.