Free Amazon Web Services SAP-C02 Practice Exam with Questions & Answers | Set: 8

This option allows the company to use AWS Client VPN to enable secure and private access to the Amazon ES cluster from any location1. By configuring and setting up an AWS Client VPN endpoint, the company can create a secure tunnel between the developers’ devices and the VPC2. By associating the Client VPN endpoint with a subnet in the VPC, the company can ensure that the trafficfrom the developers’ devices is routed to the Amazon ES cluster within the VPC3. By configuring a Client VPN self-service portal, the company can enable the developers to download and install the client for Client VPN, which is based on OpenVPN4. By instructing the developers to connect by using the client for Client VPN, the company can allow them to access Amazon ES to analyze and visualize logs directly from their local development machines.

What is AWS Client VPN?

Creating a Client VPN endpoint

Associating a target network with a Client VPN endpoint

Configuring a self-service portal

B is correct because ALB access logs contain the exact IP addresses of all incoming client requests, and storing these logs in S3 enables historical analysis. Using Amazon Athena to query them offers an efficient, serverless way to check when each IP last accessed the application.

D is correct because parsing ALB access logs gives direct evidence of activity from IP addresses — ideal for implementing the "last accessed in 90 days" rule.

A and C refer to VPC flow logs, which only capture network metadata, not HTTP request-level details like the ALB logs do.

E is incorrect — CloudTrail logs AWS API actions, not web requests through an ALB.

S3 Event Notifications is a feature that allows users to receive notifications when certain events happen in an S3 bucket, such as object creation or deletion1. Users can configure S3 Event Notifications to invoke an AWS Lambda function when a user stores an image in the bucket. Lambda is a serverless compute service that runs code in response to events and automatically manages the underlying compute resources2. The Lambda function can resize the image in place and store the original file in the same S3 bucket. This way, the solution can handle significant variance in demand and be reliable at enterprise scale. Thesolution can also rerun processing jobs in the event of failure by using the retry and dead-letter queue features of Lambda2.

S3 Lifecycle is a feature that allows users to manage their objects so that they are stored cost-effectively throughout their lifecycle3. Users can create an S3 Lifecycle policy to move all stored images to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. S3 Standard-IA is a storage class designed for data that is accessed less frequently, but requires rapid access when needed4. It offers a lower storage cost than S3 Standard, but charges a retrieval fee. Therefore, moving the images to S3 Standard-IA after 6 months can reduce the storage cost for the solution.

Option A is incorrect because using AWS Step Functions to process the S3 event that occurs when a user stores an image is not necessary or cost-effective. AWS Step Functions is a service that lets users coordinate multiple AWS services into serverless workflows. However, for this use case, a single Lambda function can handle the image resizing task without needing Step Functions.

Option B is incorrect because using Amazon EventBridge to process the S3 event that occurs when a user uploads an image is not necessary or cost-effective. Amazon EventBridge is a serverless event bus service that makes it easy to connect applications with data from a variety of sources. However, for this use case, S3 Event Notifications can directly invoke the Lambda function without needing EventBridge.

Option D is incorrect because using Amazon Simple Queue Service (Amazon SQS) to process the S3 event that occurs when a user stores an image is not necessary or cost-effective. Amazon SQS is a fully managed message queuing service that enables users to decouple and scale microservices, distributed systems, and serverless applications. However, for this use case, S3 Event Notifications can directly invoke the Lambda function without needing SQS. Moreover, storing the resized file in an S3 bucket that uses S3 Standard-IA will incur a retrieval fee every time the file is accessed, which may not be cost-effective for frequently accessed files.

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)

Amazon RDS Proxy is a fully managed database proxy service for Amazon Relational Database Service (RDS) that makes applications more scalable, resilient, and secure. It allows applications to pool and share connections to an RDS database, which can help reduce database connection overhead, improve scalability, and provide automatic failover and high availability.

Migrate to Amazon Aurora:

Amazon Aurora is a MySQL-compatible, high-performance database designed to provide higher throughput than standard MySQL. Migrating the database to Aurora will enhance the performance and scalability of the database, especially under heavy workloads.

Add Aurora Replica:

Aurora Replicas provide read scalability and improve availability. Adding an Aurora Replica allows read operations to be distributed, thereby reducing the load on the primary instance and improving response times during peak periods.

Configure Amazon RDS Proxy:

Amazon RDS Proxy acts as an intermediary between the application and the Aurora database, managing connection pools efficiently. RDS Proxy reduces the overhead ofopening and closing database connections, thus maintaining fewer active connections to the database and handling surges in database connections from the Lambda functions more effectively.

This configuration reduces the database's resource usage and improves its ability to handle high volumes of concurrent connections.

References

AWS Database Blog on RDS Proxy​(Amazon Web Services, Inc.)​.

AWS Compute Blog on Using RDS Proxy with Lambda​(Amazon Web Services, Inc.)​.

On-demand capacity mode is the function of Dynamodb.https://aws.amazon.com/blogs/news/running-spiky-workloads-and-optimizing-costs-by-more-than-90-using-amazon-dynamodb-on-demand-capacity-mode/

Amazon DocumentDB Elastic Clustershttps://aws.amazon.com/blogs/news/announcing-amazon-documentdb-elastic-clusters/

Deploy Amazon EC2 instances in an Auto Scaling group across multiple Availability Zones for the Java backend application. This will provide high availability and scalability, while allowing the company to retain the same database structure as the original application.

Amazon Connect is a cloud-based contact center service that allows you to set up a virtual call center for your business. It provides an easy-to-use interface for managing customer interactions through voice and chat. Amazon Connect integrates with other AWS services, such as Amazon S3 and Amazon Kinesis, to help you collect, store, and analyze customer data for insights into customer behavior and trends. On the other hand, Amazon Pinpoint is a marketing automation and analytics service that allows you to engage with your customers across different channels, such as email, SMS, push notifications, and voice. It helps you create personalized campaigns based on userbehavior and enables you to track user engagement and retention. While both services allow you to communicate with your customers, they serve different purposes. Amazon Connect is focused on customer support and service, while Amazon Pinpoint is focused on marketing and engagement.

https://docs.aws.amazon.com/AmazonS3/latest/userguide/replication-time-control.html

The company should create a second target group that is referenced by the ALB. The company should deploy the new logic to EC2 instances in this new target group. The company should update the ALB listener rule to use weighted target groups. The company should configure ALB target group stickiness. This solution will meet the requirements because weighted target groups are a feature that enables you to distribute traffic across multiple target groups using a single listener rule. You can specify a weight for each target group, which determines the percentage of requests that are routed to that target group. For example, if you specify two target groups, each with a weight of 10, each target group receives half the requests1. By creating a second target group and deploying the new logic to EC2 instances in this new target group, the company can have two versions of its business logic running in parallel. By updating the ALB listener rule to use weighted target groups,the company can control how much traffic is sent to each version. By configuring ALB target group stickiness, the company can ensure that a customer uses the same version of the business logic during the testing window. Target group stickiness is a feature that enables you to bind a user’s session to a specific target within a target group for the duration of the session2.

The other options are not correct because:

Creating a second ALB and deploying the new logic to a set of EC2 instances in a new Auto Scaling group would not be as cost-effective or simple as using weighted target groups. A second ALB would incur additional charges and require more configuration and management. Updating the Route 53 record to use weighted routing would not ensure that a customer uses the same version of the business logic during the testing window, as DNS caching could affect how requests are routed.

Creating a new launch configuration for the Auto Scaling group and replacing it on the Auto Scaling group would not allow for gradual traffic shifting between versions. A launch configuration is a template that an Auto Scaling group uses to launch EC2 instances. You can specify information such as the AMI ID, instance type, key pair, security groups, and block device mapping for your instances3. However, replacing the launch configuration on an Auto Scaling group would affect all instances in that group, not just 10% of customers.

Creating a second Auto Scaling group and changing the ALB routing algorithm to least outstanding requests (LOR) would not allow for controlled traffic shifting between versions. A second Auto Scaling group would require more configuration and management. The LOR routing algorithm is a feature that enables you to route traffic based on how quickly targets respond to requests. The load balancer selects a target from the target group with the fewest outstanding requests4. However, this algorithm does not take into account customer sessions or weights.

This explanation is based on AWS documentation and best practices but is paraphrased, not a literal extract.

The scenario describes a central event broker and multiple microservices running in separate AWS accounts that all need to consume the same events. The requirement is to distribute events from a central location to many microservices across accounts in a scalable and loosely coupled way, as part of modernizing to a microservices architecture.

Amazon EventBridge is a serverless event bus service designed for event-driven architectures. It supports centralized event buses, rich content-based filtering with rules, and cross-account event routing. With EventBridge, you can create an event bus in a central account and define rules that match specific event patterns (for example, by microservice or event type). Each rule can have one or more targets, including event buses in other AWS accounts. This supports the pattern of having a central event bus in one account and distributing relevant events to other accounts, where each microservice consumes events either directly from its own event bus or through additional rules and targets in its own account.

In this solution, you create a new EventBridge event bus in the central account and grant the appropriate permissions for cross-account access (option B). You then define EventBridge rules on the central event bus, filtered per microservice or per event category, and configure the rules to send events to the respective event buses or targets in the microservices’ accounts. EventBridge handles the fan-out and delivery of events across accounts in a managed, scalable way, which aligns with the modernization goal and reduces the operational overhead of managing custom routing or polling logic.

Option A uses an SNS topic with SQS queues in each account. This is a valid fan-out pattern and supports cross-account subscriptions, but it is more suited to traditional pub/sub messaging and does not provide the event routing, filtering, and observability features that EventBridge offers for modern event-driven microservices. In scenarios that explicitly mention an event broker and modernization, EventBridge is the recommended service.

Option C is incorrect because Kinesis Data Streams is designed for high-throughput streaming data and requires building and managing consumer applications. The description in the option is also technically inaccurate; Kinesis does not “invoke” microservices directly as event targets in the same way as EventBridge or SNS does. Instead, applications must read from the stream.

Option D uses a single central SQS queue that all microservices read from. SQS provides at-least-once delivery to competing consumers, which means multiple consumers reading from the same queue will typically share messages rather than each getting all messages. This does not satisfy the requirement for multiple microservices to each receive the same events independently. It also reduces decoupling and observability compared to an event bus model.

Therefore, creating an Amazon EventBridge event bus in the central account with rules to distribute events across accounts (option B) best meets the requirements for distributing events from a central broker to multiple microservices across accounts in a modernized architecture.

This option allows the company to use DAX to improve the performance and reduce the latency of the DynamoDB queries by caching the results in memory1. By updating the application to use DAX, the company can reduce the load on the DynamoDB tables and avoid throttling errors1. By creating an Auto Scaling group for the EC2 instances, the company can adjust the number of instances based on the demand and ensure high availability2. By creating an ALB, the company can distribute the incoming traffic across multiple EC2 instances and improve fault tolerance3. By updating the Route 53 record to use a simple routing policy that targets the ALB’s DNS alias, the company can route users to the ALB endpoint and leverage its health checks and load balancing features4. By configuring scheduled scaling for the EC2 instances before the content updates, the company can anticipate and handle traffic spikes during peak periods5.

What is Amazon DynamoDB Accelerator (DAX)?

What is Amazon EC2 Auto Scaling?

What is an Application Load Balancer?

Choosing a routing policy

Scheduled scaling for Amazon EC2 Auto Scaling

This option allows the solutions architect to use a DeletionPolicy attribute to specify how AWS CloudFormation handles the deletion of an S3 bucket when the stack is deleted1. By setting the value of Delete, the solutions architect can instruct CloudFormation to delete the bucket and all of itscontents1. This option does not require any major changes to the application’s architecture or any additional resources.

Deletion policies