Free Amazon Web Services SAP-C02 Practice Exam with Questions & Answers | Set: 3

https://aws.amazon.com/s3/features/access-points/

https://aws.amazon.com/blogs/storage/managing-amazon-s3-access-with-vpc-endpoints-and-s3-access-points/

Comprehensive and Detailed Explanation From Exact Extract:

D is required because the only reliable way to ensure newly launched Auto Scaling instances are patched is to make the launch template reference an AMI that already includes the latest security updates (an immutable image approach). AWS Systems Manager can automate building and maintaining patched AMIs (for example, through automated image creation workflows), after which the launch template is updated to the new AMI and the fleet is updated using Instance Refresh. Instance Refresh performs a controlled rolling replacement of instances so that the Auto Scaling group converges to the new AMI baseline.

C complements D by ensuring safe replacement and availability during the refresh/replacement process. Placing an ALB in front of the Auto Scaling group with health checks ensures that only healthy, fully bootstrapped/patched instances receive traffic, and that traffic is drained away from instances being replaced. Monitoring target health confirms the rollout is successful and minimizes risk during patch-driven reboots or instance replacement.

Why the other options are incorrect:

A: A termination policy setting does not ensure new instances are patched. It only affects which instances are terminated first. It does not solve the “launch patched instances” requirement.

B: Running two Auto Scaling groups and continuing in-place patching increases operational overhead and still risks drift and unpatched capacity when scaling occurs outside the maintenance window. It also does not address the core issue: the launch template AMI baseline.

E: NLB + termination protection does not ensure instances are patched at launch. Termination protection can interfere with Auto Scaling’s ability to replace instances, and NLB does not inherently provide the same application-layer health check behavior and deployment safety patterns typically used for rolling replacements (compared to ALB target group health checks).

https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/lambda-examples.html

Create an S3 Bucket:

Log in to the AWS Management Console and navigate to Amazon S3.

Create a new S3 bucket that will serve as the destination for the application data.

Deploy AWS Storage Gateway:

Download and deploy the AWS Storage Gateway virtual machine (VM) on your on-premises environment. This VM can be deployed on VMware ESXi, Microsoft Hyper-V, or Linux KVM.

Configure the File Gateway:

Configure the deployed Storage Gateway as a file gateway. This will enable it to present Amazon S3 buckets as SMB file shares to your on-premises applications.

Create a New File Share:

Within the Storage Gateway configuration, create a new file share that is associated with the S3 bucket you created earlier. This file share will use the SMB protocol, allowing your on-premises applications to access the S3 bucket as if it were a local SMB file share.

Copy Data to the File Gateway:

Use your preferred method (such as robocopy, rsync, or similar tools) to copy data from the on-premises storage to the newly created file gateway endpoint. This data will be stored in the S3 bucket, maintaining accessibility through SMB.

Ensure Secure and Efficient Data Transfer:

AWS Storage Gateway ensures that all data in transit is encrypted using TLS, providing secure data transfer to AWS. It also provides local caching for frequently accessed data, improving access performance for on-premises applications.

This approach allows your existing on-premises applications to continue accessing data via SMB while leveraging the scalability and durability of Amazon S3.

References

AWS Storage Gateway Overview【67】.

AWS DataSync and Storage Gateway Hybrid Architecture【66】.

AWS S3 File Gateway Details【68】.

(https://stackoverflow.com/questions/50250114/athena-vs-redshift-spectrum)

Extend the system with an application tier that uses AWS Step Functions and AWS Lambda. Configure this tier to use Amazon Textract and Amazon Comprehend to perform optical character recognition (OCR) on the forms when forms are uploaded. Store the output in Amazon S3. Parse this output by extracting the data that is required within the application tier. Submit the data to the target system's API. This solution meets the requirements of accurate form extraction, minimal time to market, and minimal long-term operational overhead. Amazon Textract and Amazon Comprehend are fully managed and serverless services that can perform OCR and extract relevant data from the forms, which eliminates the need to develop custom libraries or train and host models. Using AWS Step Functions and Lambda allows for easy automation of the process and the ability to scale as needed.

For an application requiring low-latency access to financial information and high availability with a Recovery Time Objective (RTO) of 30 seconds, using Amazon DynamoDB for data storage and Amazon QuickSight for reporting is the most suitable solution. DynamoDB offers fast, consistent, and single-digit millisecond latency for data retrieval, meeting the latency requirements. QuickSight's ability to directly query DynamoDB datasets and provide embedded dashboards for reporting enables real-time financial report generation. This combination ensures high availability and meets the RTO requirement, providing a robust solution for the application's needs.

Amazon DynamoDB Documentation: Describes the features and benefits of DynamoDB, emphasizing its performance and scalability for applications requiring low-latency access to data.

Amazon QuickSight Documentation: Provides information on using QuickSight for creating and embedding interactive dashboards, including direct querying of DynamoDB datasets for real-time data visualization.

The best option is to use an Auto Scaling group, a Network Load Balancer, Amazon Route 53, and Amazon DynamoDB to create a scalable, highly available, and low-latency online game application. An Auto Scaling group can automatically adjust the number of EC2 instances based on the demand and traffic in each Region. A Network Load Balancer can distribute the incoming traffic across the EC2 instances and handle millions of requests per second. Amazon Route 53 can use latency-based routing to direct the users to the Region that provides the best performance. Amazon DynamoDB global tables can replicate the game metadata across multiple Regions, ensuring consistency and availability of thedata. This approach minimizes the operational overhead and cost, as it leverages fully managed services and avoids custom logic or replication.

Option A is not optimal because using an EC2 Spot Fleet can introduce the risk of losing the EC2 instances if the Spot price exceeds the bid price, which can affect the availability and performance of the game. Using AWS Global Accelerator can improve the network performance, but it is not necessary if Amazon Route 53 can already route the users to the closest Region. Using Amazon RDS for MySQL can store the game metadata, but it requires setting up read replicas and managing the replication lag across Regions, which can increase the complexity and cost.

Option B is not optimal because using geoproximity routing can direct the users to the Region that is geographically closer, but it does not account for the network latency or performance. Using MySQL databases on EC2 instances can store the game metadata, but it requires managing the EC2 instances, the database software, the backups, the patches, and the replication across Regions, which can increase the operational overhead and cost.

Option D is not optimal because using EC2 Global View is not a valid service. Using a custom DNS server on an EC2 instance can redirect the user to the Region that provides the lowest latency, but it requires developing and maintaining the custom logic, as well as managing the EC2 instance, which can increase the operational overhead and cost. Using Amazon Aurora global database can store the game metadata, but it is more expensive and complex than using Amazon DynamoDB global tables.

Auto Scaling groups

Network Load Balancer

Amazon Route 53

Amazon DynamoDB global tables

 The company should configure a cross-Region read replica for the RDS database in the new Region. The company should change the Route 53 record to latency-based routing to connect to the API Gateway API. This solution will meet the requirements because a cross-Region read replica is a feature that enables you to create a MariaDB, MySQL, Oracle, PostgreSQL, or SQL Server read replica in a different Region from the source DB instance. You can use cross-Region read replicas to improve availability and disaster recovery, scale out globally, or migrate an existing database to a new Region1. By creating a cross-Region read replica for the RDS database in the new Region, the company can have a standby copy of its primary database that can serve read-only traffic from users in Europe. A latency-based routing policy is a feature that enables you to route traffic based on the latency between your users and your resources. You can use latency-based routing to route traffic to the resource that provides the best latency2. By changing the Route 53 record to latency-based routing, the company can minimize latency for users who download reports by connecting them to the API Gateway API in the Region that provides the best response time.

The other options are not correct because:

Using AWS Database Migration Service (AWS DMS) to replicate the primary database in the original Region to the database in the new Region would not be as cost-effective or simple as using a cross-Region read replica. AWS DMS is a service that enables you to migrate relational databases, data warehouses, NoSQL databases, and other types of data stores. You can use AWS DMS to perform one-time migrations or continuous data replication with high availability and consolidate databases intoa petabyte-scale data warehouse3. However, AWS DMS requires more configuration and management than creating a cross-Region read replica, which is fully managed by Amazon RDS. AWS DMS also incurs additional charges for replication instances and tasks.

Creating an Amazon API Gateway Data API service integration with Amazon Redshift would not help with disaster recovery or minimizing latency. 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 client. It is useful forbuilding applications that interact with Amazon Redshift, but not for replicating or recovering data from an RDS database.

Creating an AWS Data Exchange datashare by connecting AWS Data Exchange to the Redshift cluster would not help with disaster recovery or minimizing latency. AWS Data Exchange is a service that makes it easy for AWS customers to exchange data in the cloud. You can use AWS Data Exchange to subscribe to a diverse selection of third-party data products or offer your own data products to other AWS customers. 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 data. It is useful for sharing query results and views with other users, but not for replicating or recovering data from an RDS database.

This solution will meet the requirements with the least operational overhead because it allows the company to modify the existing environment's capacity configuration, so it becomes a load-balanced environment type. By selecting all availability zones, the company can ensure that the application is running in multiple availability zones, which can help to improve the availability and scalability of the application. The company can also add a scale-out rule that will run if the average CPU utilization is over 85% for 5 minutes, which can help to mitigate the performance issues. This solution does not require creating new Elastic Beanstalk environments or rebuilding the existing one, which reduces the operational overhead.

You can refer to the AWS Elastic Beanstalk documentation for more information on how to use this service:https://aws.amazon.com/elasticbeanstalk/You can refer to the AWS documentation for more information on how to use autoscaling:https://aws.amazon.com/autoscaling/

https://aws.amazon.com/rds/aurora/global-database/

Creating a quota request template in the us-east-1 Region of the management account ensures it applies to all newly provisioned accounts in the organization.

By specifying the required ml.p2.xlarge training job and endpoint usage quotas in ap-southeast-2, the accounts will automatically receive these higher quotas upon creation, supporting the student lab environments without additional manual quota requests.

This process integrates seamlessly with AWS Organizations for automated and standardized account provisioning.