Free Practice Questions for Amazon Web Services SAP-C02 Exam

https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/sqs-dead-letter-queues.html

Option A is correct because SageMaker AI Notebook Instances support Git repositories as SageMaker resources, and private repository credentials can be stored in AWS Secrets Manager. This satisfies the requirement with the least operational overhead because the notebook instances can remain isolated from direct internet access while SageMaker manages repository association and authentication. Placing credentials inside a URL is insecure and does not properly solve credential management. NAT gateway options introduce internet egress into the VPC, which conflicts with the isolation requirement and adds operational and security complexity. Network ACLs are not suitable for controlling access to a specific Git URL because they operate at the subnet and IP/port level, not at an application URL level. (AWS Documentation)

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Option C is correct because SageMaker AI real-time endpoints support multiple production variants behind a single endpoint. Each production variant can represent a different model version, and traffic distribution is controlled by variant weights. Setting each model variant to 0.5 produces an even split, allowing A/B testing while the existing ecommerce application continues invoking a single endpoint. AWS documentation explains that production variants are used for A/B testing and that traffic to variants is distributed according to configured weights. Option B and option D require application-side routing or round robin logic, which increases change impact and operational complexity. Option A is weaker because the standard documented pattern for controlled A/B testing with weighted model variants is a SageMaker real-time endpoint with production variants.

AWS Audit Manager is the managed service built for continuous audit evidence collection and audit-ready reporting. It can automatically collect evidence from AWS accounts and services, map evidence to controls and frameworks, and help generate reports for auditors. AWS Config provides the technical compliance signal for resource configuration, and conformance packs package managed or custom AWS Config rules into repeatable compliance templates. An AWS Config aggregator centralizes visibility across accounts. Option D therefore combines control evaluation, centralized aggregation, framework alignment, historical evidence, and audit reporting without custom pipelines. CloudTrail plus Athena or Config snapshots plus Lambda would require custom report generation. Security Hub is useful for security posture aggregation, but the question specifically requires evidence collection and auditor-ready reports mapped to regulatory frameworks.

A is correct because AWS recommends using oneconnection alias per Region, associated witheach directory. Then, configure a Route 53 failover policy so that if the primary Region becomes unhealthy, users are directed to the failover Region automatically. “Evaluate Target Health” ensures automatic detection and failover.

Comprehensive and Detailed in Depth Explanation:

A is correct because AWS CodeBuild is a fully managed build service that supports running tests and security scans. EventBridge can detect test failures and send notifications via SNS. AWS CDK with manifest files is a robust way to manage feature toggles. CodePipeline supports manual approval stages to allow lead developer intervention.

Amazon Route 53 Resolver is a managed DNS resolver service from Route 53 that helps to create conditional forwarding rules to redirect query traffic1. By associating the private hosted zone to all the VPCs, the solutions architect can enable DNS resolution for cloud.example.com within the VPCs. By creating a Route 53 inbound resolver in the shared services VPC, the solutions architect can enable DNS resolution for cloud.example.com from on-premises systems. By attaching all VPCs to the transit gateway, the solutions architect can enable connectivity between the VPCs and the on-premises network through AWS Direct Connect. By creating forwarding rules in the on-premises DNS server for cloud.example.com that point to the inbound resolver, the solutions architect can direct DNS queries for cloud.example.com to the Route 53 Resolver endpoint in AWS. This solution will provide the highest performance as it leverages Route 53 Resolver’s optimized routing and caching capabilities.

The workload is containerized, runs for hours, and is event-driven by nightly data arrival in Amazon S3. The current architecture uses EC2 instances and cron jobs, which results in operational overhead (managing instances, patching, scaling, scheduling) and idle compute between processing windows.

A key constraint is that the processing tasks can take hours. AWS Lambda has maximum execution duration limits that make it unsuitable for multi-hour batch processing. Even though Lambda can run container images, it still must complete within Lambda’s runtime limit. Packaging container images as Lambda layers is also not an appropriate pattern for long-running container workloads and adds complexity.

A modern, low-ops approach for long-running, containerized batch jobs is to run containers on AWS Fargate. Fargate removes the need to manage EC2 instances and allows tasks to run for extended periods as needed, scaling based on demand. Because the workload is composed of several data-processing services that likely need orchestration (for example, fan-out, sequencing, retries, parallelism), AWS Step Functions is well suited to coordinate the workflow and invoke the appropriate ECS tasks.

For triggering based on new S3 data, Amazon EventBridge provides a managed, scalable event bus for AWS service events, including S3 object events, and can route events to targets such as Step Functions state machines. Using EventBridge reduces the need for direct point-to-point notification wiring and provides centralized event routing, filtering, and monitoring.

Option C combines all the right elements: it runs the containers as ECS tasks on Fargate to eliminate EC2 management and idle capacity, uses Step Functions to orchestrate tasks that can run for hours, and uses EventBridge to trigger the state machine when new data is uploaded to S3. This replaces the per-instance cron scheduling with an event-driven serverless orchestration model and significantly reduces operational overhead.

Option B is close but is less appropriate as written because S3 Event Notifications are typically configured to send to Amazon SQS, Amazon SNS, or AWS Lambda. Triggering Step Functions directly is more naturally handled through EventBridge rules. EventBridge is also the recommended event routing layer for integrating service events into workflows.

Option A is not suitable because Lambda is not designed for multi-hour processing jobs due to runtime limits.

Option D is incorrect because Lambda layers are for sharing libraries and runtime dependencies, not for packaging multi-hour container workloads. It also still depends on Lambda runtime limits and does not match the operational model for long-running batch processing.

Therefore, option C is the best modernization approach with the least operational overhead.

The best solution is to create an RSA key pair that is dedicated to the data-handling microservice and upload the public key to the CloudFront distribution. Then, create a field-level encryption profile and a configuration, and add the configuration to the CloudFront cache behavior. This solution will ensure that the sensitive data is encrypted at the edge locations of CloudFront, close to the end users, and remains encrypted throughout the application stack. Only the data-handling microservice, which has access to the private key of the RSA key pair, can decrypt the data. This solution does not require any additional resources or code changes, and leverages the built-in feature of CloudFront field-level encryption. For more information about CloudFront field-level encryption, see Using field-levelencryption to help protect sensitive data.

Configuring the Aurora MySQL DB cluster to publish slow query and error logs to AmazonCloudWatch Logs will allow the solutions architect to monitor and troubleshoot the database performance by identifying slow or problematic queries1. CloudWatch Logs also provides features such as metric filters, alarms, and dashboards to analyze and visualize the log data2.

Implementing the AWS X-Ray SDK to trace incoming HTTP requests on the EC2 instances and implement tracing of SQL queries with the X-Ray SDK for Java will allow the solutions architect to measure and map the end-to-end latency and performance of the web application3. X-Ray traces show how requests travel through the application components, such as web servers, load balancers, microservices, and databases4. X-Ray also provides features such as service maps, annotations, histograms, and error rates to analyze and optimize the application performance.

Installing and configuring an Amazon CloudWatch Logs agent on the EC2 instances to send the Apache logs to CloudWatch Logs will allow the solutions architect to monitor and troubleshoot the web server performance by collecting and storing the Apache access and error logs. CloudWatch Logs also provides features such as metric filters, alarms, and dashboards to analyze and visualize the log data2.

Publishing Aurora MySQL logs to Amazon CloudWatch Logs

Working with log data in CloudWatch Logs

Instrumenting your application with the X-Ray SDK for Java

Tracing requests with AWS X-Ray

[Analyzing application performance with AWS X-Ray]

[Using CloudWatch Logs with your Apache web server]

https://aws.amazon.com/blogs/architecture/accelerating-your-migration-to-aws/ Build a business case with AWS Migration Evaluator The foundation for a successful migration starts with a defined business objective (for example, growth or new offerings). In order to enable the business drivers, the established business case must then be aligned to a technical capability (increased security and elasticity). AWS Migration Evaluator (formerly known as TSO Logic) can help you meet these objectives. To get started, you can choose to upload exports from third-party tools such as Configuration Management Database (CMDB) or install a collector agent to monitor. You will receive an assessment after data collection, which includes a projected cost estimate and savings of running your on-premises workloads in the AWS Cloud. This estimate will provide a summary of the projected costs to re-host on AWS based on usage patterns. It will show the breakdown of costs by infrastructure and software licenses. With this information, you can make the business case and plan next steps.

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.

Evaluate Target Health Setting:

Ensure that the " Evaluate Target Health " setting is enabled for the latency alias resource record sets in Route 53. This setting helps Route 53 determine the health of the resources associated with the alias record and redirect traffic appropriately.

HTTP Health Checks:

Configure HTTP health checks for all weighted resource record sets. Health checks monitor the availability and performance of the web servers, allowing Route 53 to reroute traffic to healthy servers in case of a failure.

Verify that the health checks are correctly set up and associated with the resource record sets. This ensures that Route 53 can detect server failures and redirect traffic to the servers in the other Region.

By enabling the " Evaluate Target Health " setting and configuring HTTP health checks, Route 53 can effectively manage traffic during failover scenarios, ensuring high availability and reliability.

References

AWS Route 53 Documentation on Latency-Based Routing【50】.

AWS Architecture Blog on Cross-Account and Cross-Region Setup【49】.