Amazon AWS Certified DevOps Engineer — Professional DOP-C02 Exam Dumps and Practice Test Questions Set 12 Q166-180
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Question 166
A company is deploying multiple microservices using ECS Fargate and wants to minimize deployment downtime while updating services frequently. Which deployment strategy allows zero-downtime updates with minimal operational overhead?
A) Blue/Green Deployment using CodeDeploy
B) Rolling Update Deployment
C) Recreate Deployment
D) Canary Deployment without automation
Answer: A) Blue/Green Deployment using CodeDeploy
Explanation:
Blue/Green deployment is a strategy in which two separate environments are maintained: one is the live production environment (Blue), and the other is the new version of the application (Green). Using AWS CodeDeploy, the ECS service is updated by shifting traffic from the Blue environment to the Green environment once the new version is verified as stable. This approach allows for zero-downtime deployment because users continue interacting with the Blue environment while the Green environment is being tested. Traffic switching can be instantaneous or gradual depending on defined policies, ensuring minimal impact on end users.
Rolling Update deployment updates a subset of tasks incrementally by replacing old tasks with new ones while the service continues running. While this reduces downtime, it may not fully isolate the new version, potentially causing compatibility issues if a new bug is introduced. During high-traffic periods, rolling updates may cause partial service degradation, which is not ideal for mission-critical microservices.
Recreate deployment stops the existing tasks entirely and launches new ones. This approach introduces downtime because users have no live application while the update occurs. Although simple to implement, it is not suitable for high-availability applications, as it interrupts service availability entirely.
Canary deployment without automation releases the new version to a small subset of users manually. While it allows testing with limited exposure, manual management introduces operational overhead and risk of inconsistent configurations. Automated traffic management and monitoring are limited, increasing the chance of errors during deployment.
Blue/Green deployment with CodeDeploy integrates tightly with ECS, CloudWatch, and load balancers. It allows traffic switching, automated health checks, and rollback in case of issues. Metrics such as CPU usage, response time, or error rates can be monitored in the Green environment before fully shifting traffic. If the new version fails health checks, CodeDeploy automatically rolls back to the Blue environment, ensuring business continuity. This strategy minimizes operational overhead, reduces risk, and ensures a reliable zero-downtime deployment, making it the optimal choice.
Question 167
An organization needs to monitor Lambda functions for performance and operational issues, including invocation errors, latency, and throttling. Which AWS service combination provides the most complete monitoring and alerting solution?
A) CloudWatch Metrics + CloudWatch Logs + CloudWatch Alarms
B) S3 + Athena
C) Config + Systems Manager
D) QuickSight + SNS
Answer: A) CloudWatch Metrics + CloudWatch Logs + CloudWatch Alarms
Explanation:
Amazon CloudWatch is the core service for monitoring Lambda functions. CloudWatch Metrics provides performance and operational metrics such as invocation count, duration, error count, and throttles. These metrics are automatically available for each Lambda function, providing a real-time view of function behavior. For example, tracking the duration metric allows teams to identify functions that are slower than expected, and monitoring the error metric highlights failing invocations. CloudWatch Metrics enables granular monitoring for both individual functions and aggregated service-level views.
CloudWatch Logs captures detailed execution logs for Lambda invocations. Logs include function start and end times, custom application messages, exceptions, and stack traces. This data is critical for troubleshooting operational issues, understanding the root cause of failures, and verifying business logic correctness. Logs can also be analyzed using filters and queries to extract performance trends or detect anomalies.
CloudWatch Alarms allow proactive notification and automation. Alarms can monitor metrics such as error rate exceeding 5% or function duration surpassing a threshold. When a metric breaches a threshold, alarms trigger notifications via SNS or automated actions like invoking a Lambda function for remediation. This ensures that teams are immediately informed of operational issues and can take corrective action quickly, minimizing downtime and impact.
S3 and Athena provide storage and query capabilities. While Lambda logs could be exported to S3 and analyzed with Athena for historical insight, this is not real-time monitoring. It cannot proactively trigger alerts or provide continuous operational visibility.
AWS Config combined with Systems Manager monitors configuration compliance and provides automation for resource management but does not provide performance metrics or real-time monitoring for Lambda functions.
QuickSight combined with SNS can visualize data trends and provide notifications but is primarily for reporting and not real-time operational monitoring. Alerts are limited in granularity and automation for Lambda performance issues.
Combining CloudWatch Metrics, Logs, and Alarms provides an end-to-end monitoring solution. Metrics offer performance insights, Logs provide detailed operational context, and Alarms deliver proactive alerting. Together, these services provide continuous observability, rapid issue detection, and automated operational responses, making this the most comprehensive solution for monitoring Lambda functions.
Question 168
A company needs to deploy a multi-region web application with high availability and disaster recovery requirements. They must minimize RTO (Recovery Time Objective) and ensure consistent global performance. Which approach is most suitable?
A) Multi-region active-active architecture using Route 53, CloudFront, and DynamoDB Global Tables
B) Single-region deployment with frequent backups to S3
C) Active-passive deployment with manual failover
D) On-premises disaster recovery site without cloud integration
Answer: A) Multi-region active-active architecture using Route 53, CloudFront, and DynamoDB Global Tables
Explanation:
A multi-region active-active architecture provides high availability, low latency, and minimal recovery time. Route 53 latency-based routing directs user requests to the closest healthy region, optimizing performance. Health checks monitor endpoint availability, and traffic is automatically routed away from unhealthy regions. CloudFront accelerates content delivery by caching content at edge locations, reducing latency for global users. DynamoDB Global Tables allow multi-region, fully replicated databases that maintain data consistency across regions. This ensures that all regions have the latest data, providing seamless failover and minimal recovery time.
Single-region deployment with frequent S3 backups is insufficient for global applications. While backups provide recoverability, restoring from backup in a different region introduces high RTO and cannot provide continuous availability. Users in distant regions also experience higher latency.
Active-passive deployment with manual failover introduces downtime during recovery. Traffic must be manually rerouted, and database replication may lag, leading to potential data loss or inconsistency. This does not meet strict RTO or low-latency requirements for global applications.
An on-premises disaster recovery site without cloud integration increases complexity and operational overhead. Replication across on-premises sites is slower and more error-prone compared to managed cloud services. Maintaining consistent global performance and minimal recovery time is challenging without cloud-native solutions.
The combination of Route 53, CloudFront, and DynamoDB Global Tables provides a fully managed, highly available multi-region solution. Route 53 ensures low-latency traffic routing, CloudFront improves content delivery performance globally, and DynamoDB Global Tables maintain consistent, replicated data across regions. This architecture meets high availability, disaster recovery, and low-latency requirements, making it the correct solution for globally distributed web applications.
Question 169
A company is using AWS CodePipeline to deploy a microservices application. They want to automatically roll back deployments when integration tests fail. Which service or combination of services enables this behavior?
A) CodePipeline + CodeBuild + CloudWatch Alarms
B) S3 + Lambda
C) CloudFront + WAF
D) DynamoDB + SNS
Answer: A) CodePipeline + CodeBuild + CloudWatch Alarms
Explanation:
AWS CodePipeline is a fully managed continuous integration and continuous delivery service that automates the build, test, and deployment phases of application releases. CodePipeline can be configured with multiple stages, such as source, build, test, and deploy. Each stage contains actions, such as invoking a CodeBuild project or deploying to ECS, which allows the pipeline to automate the deployment workflow efficiently.
CodeBuild integrates with CodePipeline to execute build and test steps. For instance, after building the microservices artifacts, CodeBuild can run integration tests to verify that the application functions correctly. Test results are reported back to CodePipeline, enabling conditional transitions between pipeline stages. If tests fail, the pipeline can automatically prevent deployment to the production environment.
CloudWatch Alarms complement this setup by monitoring metrics emitted by the pipeline or deployed resources. For example, CloudWatch can track the success rate of CodeBuild projects or ECS service health metrics. When a failure metric surpasses a predefined threshold, an alarm triggers automated actions such as rolling back to the previous stable deployment. This integration ensures continuous monitoring and proactive handling of deployment failures, preventing faulty code from reaching production.
S3 and Lambda provide storage and compute capabilities but do not inherently support automated pipeline rollback based on test failures. While Lambda could be scripted to handle some rollback tasks, it requires custom orchestration and does not integrate seamlessly with CodePipeline or CodeBuild for automated CI/CD workflows.
CloudFront with WAF protects web applications from attacks and enhances content delivery but does not provide CI/CD automation, testing, or rollback capabilities for microservices deployments.
DynamoDB and SNS provide data storage and messaging services but cannot handle automated deployment pipelines, integration testing, or rollback workflows.
The combination of CodePipeline, CodeBuild, and CloudWatch Alarms provides a complete CI/CD automation solution. CodePipeline orchestrates the workflow, CodeBuild runs tests and reports outcomes, and CloudWatch monitors performance and triggers automated rollback if needed. This ensures that deployments are tested thoroughly, reduces operational risk, and maintains application stability, making it the correct solution.
Question 170
A company needs to maintain consistent database reads and writes across multiple AWS regions for a globally distributed application. Which AWS service configuration ensures low-latency access and high availability?
A) DynamoDB Global Tables
B) RDS Multi-AZ
C) S3 Cross-Region Replication
D) EBS Snapshots
Answer: A) DynamoDB Global Tables
Explanation:
DynamoDB Global Tables provide fully managed, multi-region, multi-master database replication. Each region contains a complete copy of the table, allowing read and write operations locally. Updates made in one region are asynchronously replicated to all other regions within milliseconds, ensuring eventual consistency while maintaining low-latency access for global users. This architecture supports globally distributed applications by minimizing response times and maintaining high availability even if a single region becomes unavailable.
RDS Multi-AZ deployments provide high availability and automatic failover for relational databases within a single region. While Multi-AZ ensures data redundancy and disaster recovery for a specific region, it does not provide multi-region writes or low-latency access for users in distant regions. Applications requiring low-latency global access cannot rely solely on Multi-AZ replication.
S3 Cross-Region Replication (CRR) enables automatic replication of objects between buckets in different regions. CRR ensures durability and geographic redundancy for stored data but is intended for storage-level replication, not database read/write operations. It cannot provide real-time transactional consistency or low-latency reads/writes for database workloads.
EBS snapshots provide point-in-time backups of volumes. They can be copied to other regions, but this is a backup mechanism rather than a solution for live, globally consistent reads and writes. Using snapshots does not enable active multi-region database access or automatic replication.
DynamoDB Global Tables provide the combination of high availability, low-latency access, and fully managed replication across regions. It allows global users to read and write locally while the system maintains consistency across all regions. Automatic conflict resolution, fault tolerance, and fully managed infrastructure reduce operational overhead. This configuration ensures high availability, resilience, and global performance, making it the correct solution for multi-region distributed applications.
Question 171
A company requires centralized logging and analysis for all AWS Lambda functions, including real-time alerting for error thresholds. Which AWS service combination provides this capability?
A) CloudWatch Logs + CloudWatch Metrics + CloudWatch Alarms
B) S3 + Athena
C) Config + Lambda
D) QuickSight + SNS
Answer: A) CloudWatch Logs + CloudWatch Metrics + CloudWatch Alarms
Explanation:
CloudWatch Logs captures detailed execution logs from Lambda functions, including invocation details, function output, custom messages, and stack traces for errors. These logs allow teams to troubleshoot issues, understand operational behavior, and identify root causes of failures. Logging provides detailed visibility into each function’s performance and helps in analyzing both expected and unexpected events.
CloudWatch Metrics provides prebuilt metrics for Lambda, including invocation count, duration, error rate, and throttling. These metrics allow real-time monitoring of function performance. Teams can identify trends, anomalies, or sudden spikes in errors or latency. Metrics can be aggregated across multiple functions for service-level visibility or examined individually for detailed analysis.
CloudWatch Alarms enable proactive alerting based on metrics thresholds. For example, if the error rate exceeds 5% for five consecutive minutes, an alarm triggers notifications via SNS or automated remediation actions such as invoking another Lambda function. This integration ensures that operational teams are informed promptly about issues and can take immediate action. Alarms can also integrate with automated workflows for incident response or rollback procedures.
S3 combined with Athena can store and query historical logs but does not provide real-time metrics or alerting. While effective for retrospective analysis, it lacks the immediate monitoring and automated responses needed for proactive operations.
Config with Lambda monitors resource configuration compliance and can trigger custom actions but does not provide performance monitoring, centralized logging, or real-time alerting for function execution.
QuickSight combined with SNS is useful for visualizing trends and sending notifications but does not provide detailed logging, real-time monitoring, or metric-based alarms for operational Lambda functions.
The combination of CloudWatch Logs, Metrics, and Alarms provides a centralized and real-time observability solution. Logs capture detailed execution events, metrics track performance, and alarms trigger alerts or automated responses. Together, these services ensure proactive monitoring, rapid issue detection, and operational resilience for Lambda functions, making it the correct solution.
Question 172
A company wants to ensure that its ECS services deployed on Fargate are highly available across multiple Availability Zones while minimizing deployment complexity. Which configuration provides the best solution?
A) ECS Service with an Application Load Balancer and tasks spread across multiple AZs
B) Single ECS task in one AZ
C) ECS service with Network Load Balancer in one AZ
D) ECS task scheduled manually with fixed IP
Answer: A) ECS Service with an Application Load Balancer and tasks spread across multiple AZs
Explanation:
Deploying ECS services across multiple Availability Zones (AZs) ensures high availability by reducing the risk of application downtime due to a single AZ failure. An Application Load Balancer (ALB) distributes incoming traffic across tasks in different AZs, providing fault tolerance and even load distribution. This configuration ensures that if tasks in one AZ become unavailable, the ALB redirects traffic to tasks running in healthy AZs, maintaining continuous service availability.
Single ECS tasks in one AZ are highly vulnerable to failure. If the AZ experiences an outage, the application becomes entirely unavailable. While simple to deploy, this approach does not meet high availability requirements for production workloads.
Using an ECS service with a Network Load Balancer (NLB) in a single AZ provides fast, layer-4 traffic routing, but it lacks multi-AZ redundancy. While NLB is suitable for high-performance or TCP-based applications, without multiple AZs, failure of the AZ or underlying tasks will result in downtime.
Manually scheduling ECS tasks with fixed IPs introduces operational complexity and does not provide automated traffic distribution or failover capabilities. Without an ALB, tasks cannot scale dynamically or handle failures efficiently.
By combining ECS services with an ALB and distributing tasks across multiple AZs, the architecture achieves high availability, automatic failover, and load balancing. Tasks in different AZs ensure resilience, while the ALB handles health checks and dynamic traffic routing. This approach minimizes downtime, simplifies deployment, and supports scaling, making it the optimal solution.
Question 173
A company wants to automate deployment of Lambda functions with minimal manual intervention, while ensuring rollback on deployment failures and proper versioning. Which AWS service combination should they use?
A) CodePipeline + Lambda + CloudWatch Alarms
B) S3 + CloudFront
C) DynamoDB + SNS
D) QuickSight + CloudTrail
Answer: A) CodePipeline + Lambda + CloudWatch Alarms
Explanation:
AWS CodePipeline provides continuous integration and delivery for Lambda functions. Pipelines define a sequence of stages including source, build, test, and deploy, enabling automated, repeatable deployments without manual intervention. Using CodePipeline ensures that each code change is automatically propagated through the pipeline, allowing faster release cycles and reduced risk of human error.
Lambda natively supports versioning and aliases. Each deployment can create a new version of the function, allowing traffic to be directed to stable versions while the new version is tested. This ensures rollback capability, as the previous version remains available if issues are detected. Versioning allows multiple stages of deployment without overwriting the existing production function, supporting controlled and auditable releases.
CloudWatch Alarms integrate with Lambda and CodePipeline to provide real-time monitoring. Alarms can track metrics such as invocation errors, duration, and throttles. If deployment fails tests or results in performance degradation, alarms trigger notifications or automated actions, such as rolling back to a previous Lambda version. This combination ensures operational visibility, automated remediation, and continuity of service.
S3 and CloudFront are primarily used for static content storage and delivery. While S3 can host Lambda deployment packages, it does not provide CI/CD automation, rollback mechanisms, or deployment monitoring. CloudFront accelerates content delivery but is unrelated to Lambda deployment automation.
DynamoDB and SNS provide storage and messaging services, but they do not orchestrate automated deployment pipelines, manage function versioning, or enforce rollback strategies.
QuickSight and CloudTrail enable auditing and reporting but do not provide automated deployment or rollback capabilities. CloudTrail logs activities, and QuickSight visualizes data, but they cannot proactively manage Lambda deployments or respond to failures in real-time.
Using CodePipeline with Lambda and CloudWatch Alarms automates deployments, provides version control, and ensures rollback in case of failure. This integrated solution reduces operational overhead, maintains high reliability, and enforces continuous delivery best practices, making it the correct choice.
Question 174
A company wants to maintain compliance for its AWS infrastructure by continuously monitoring resource configurations, detecting unauthorized changes, and triggering automated remediation. Which AWS service or combination meets this requirement?
A) AWS Config + Systems Manager
B) CloudFront + WAF
C) CloudWatch + SNS
D) DynamoDB + Lambda
Answer: A) AWS Config + Systems Manager
Explanation:
AWS Config continuously monitors and records AWS resource configurations. It allows organizations to define rules to evaluate compliance with corporate or regulatory policies. Whenever a resource configuration drifts from the expected state, Config detects it and triggers an evaluation. Config provides detailed historical records, enabling auditing and analysis of configuration changes over time. This ensures that any unauthorized modifications are detected and logged for compliance reporting.
Systems Manager complements Config by enabling automated remediation. Using Systems Manager Automation documents, organizations can define workflows to correct noncompliant resources automatically. For example, if a security group allows an unauthorized inbound port, a remediation action can modify the rule to restore compliance. Combining Config with Systems Manager allows continuous enforcement of policies without manual intervention, reducing the risk of misconfigurations and maintaining compliance posture.
CloudFront and WAF protect web applications by filtering traffic and mitigating attacks, but they do not monitor general AWS resource configurations or enforce compliance. They focus on security at the application layer, not infrastructure compliance.
CloudWatch and SNS provide monitoring and alerting capabilities. CloudWatch can track metrics and logs, and SNS can notify teams of operational events. However, this combination does not evaluate compliance against configuration rules or trigger automated remediation for unauthorized changes.
DynamoDB and Lambda provide data storage and compute services but do not monitor infrastructure compliance or enforce policy adherence. Lambda can execute custom scripts, but without a monitoring framework like Config, it cannot automatically detect configuration drift or trigger remediation reliably.
AWS Config combined with Systems Manager provides a complete compliance management solution. Config detects changes and evaluates them against predefined rules, while Systems Manager enforces corrective actions automatically. Historical records provide a clear audit trail, supporting regulatory requirements. Continuous monitoring, automatic remediation, and detailed reporting ensure that the company maintains compliance across its AWS infrastructure, making this the correct solution.
Question 175
A company is running a microservices application on ECS Fargate. They want to ensure that the application can scale automatically based on both CPU and memory usage without manual intervention. Which AWS service combination should they implement?
A) CloudWatch Metrics + ECS Service Auto Scaling
B) Lambda + DynamoDB
C) S3 + Athena
D) CloudFront + WAF
Answer: A) CloudWatch Metrics + ECS Service Auto Scaling
Explanation:
Amazon CloudWatch is a powerful monitoring and observability service that provides real-time metrics, dashboards, and alarms for ECS services running on Fargate. CloudWatch collects metrics such as CPU utilization, memory consumption, task counts, and service health. These metrics allow operations teams to monitor performance trends and detect potential bottlenecks. Alarms can be configured to notify teams when thresholds are breached, for example, if CPU usage exceeds 80% over several minutes. Dashboards provide visibility into performance across all services, helping teams plan capacity, troubleshoot issues, and optimize resource allocation.
ECS Service Auto Scaling works in tandem with CloudWatch metrics to automatically adjust the number of running tasks based on observed CPU and memory utilization. Scaling policies define conditions for scaling out (adding tasks) or scaling in (removing tasks). When metrics exceed thresholds, Auto Scaling launches additional tasks to maintain application responsiveness. When usage drops below thresholds, tasks are terminated to optimize costs. This automated scaling ensures that applications remain highly available and perform optimally without manual intervention.
Lambda and DynamoDB provide serverless compute and database services but do not inherently support automatic ECS task scaling based on performance metrics. While Lambda could be used to trigger custom scaling actions, it requires additional orchestration and is not natively integrated with ECS Auto Scaling.
S3 and Athena provide storage and query capabilities. ECS logs could be exported to S3 and analyzed using Athena, but this approach provides only retrospective insights rather than real-time monitoring. It cannot trigger automated scaling or alarms in response to performance metrics.
CloudFront and WAF enhance content delivery and protect web applications from attacks, but they do not monitor ECS workloads or trigger automatic scaling.
Combining CloudWatch Metrics with ECS Service Auto Scaling provides an integrated solution for continuous performance monitoring and dynamic resource management. CloudWatch captures metrics, triggers alarms, and feeds data to Auto Scaling, which adjusts task counts automatically. This ensures consistent application performance, cost optimization, and minimal operational overhead, making it the correct solution.
Question 176
A company wants to deploy a global web application with minimal latency for users worldwide and automatic failover if a region becomes unhealthy. Which AWS service combination is best suited?
A) Route 53 latency-based routing + health checks + CloudWatch
B) CloudFront + S3
C) Direct Connect + VPC Peering
D) Lambda + DynamoDB
Answer: A) Route 53 latency-based routing + health checks + CloudWatch
Explanation:
Amazon Route 53 provides latency-based routing to direct users to the AWS region with the lowest network latency. By configuring multiple endpoints across regions, Route 53 evaluates the origin of each user request and routes it to the fastest healthy region. This improves application performance for global users and enhances the overall user experience. Latency-based routing dynamically adapts to changes in network conditions, ensuring consistent performance.
Health checks integrated with Route 53 continuously monitor endpoint availability and responsiveness. If an endpoint becomes unhealthy, Route 53 automatically reroutes traffic to healthy endpoints, providing automated failover. Health checks can monitor HTTP/S responses, TCP connections, or application-level indicators, allowing precise control over failover behavior. Automated failover ensures high availability and reduces downtime without manual intervention.
CloudWatch complements Route 53 by providing real-time monitoring of endpoint metrics, including latency, error rates, request counts, and throughput. Dashboards enable teams to visualize performance trends, detect anomalies, and troubleshoot issues proactively. Alarms notify operations teams when endpoints become unhealthy or metrics exceed predefined thresholds. CloudWatch can also integrate with EventBridge to automate operational workflows, such as scaling resources or executing remediation scripts. Historical data supports trend analysis and capacity planning, enabling proactive optimization.
CloudFront and S3 improve static content delivery through caching and replication but do not provide latency-based routing, automated failover, or endpoint monitoring for dynamic content. They enhance static asset performance but cannot manage global routing decisions.
Direct Connect and VPC Peering improve private network connectivity but do not provide global traffic routing, failover, or real-time monitoring.
Lambda and DynamoDB provide serverless compute and storage but cannot route traffic, monitor endpoints, or provide automatic failover for global applications.
Combining Route 53 latency-based routing, health checks, and CloudWatch ensures that users are routed to the fastest healthy endpoints, endpoints are continuously monitored, and operations teams receive actionable insights. This fully managed solution provides low latency, high availability, and operational visibility, making it the correct solution.
Question 177
A company processes sensitive financial transactions using AWS Lambda and requires encryption of environment variables, prevention of unauthorized code changes, and a fully auditable compliance trail. Which AWS service combination meets these requirements?
A) AWS Config + Lambda + KMS
B) S3 + Athena
C) CloudFront + WAF
D) QuickSight + CloudTrail
Answer: A) AWS Config + Lambda + KMS
Explanation:
AWS Config continuously monitors and records AWS resource configurations. For Lambda functions handling sensitive financial data, Config ensures that environment variables are encrypted using AWS Key Management Service (KMS). If a function becomes noncompliant, Config logs detailed information and provides a complete audit trail. Continuous monitoring ensures that security policies and regulatory requirements are consistently enforced, which is essential for financial applications subject to PCI DSS, SOC 2, or SOX compliance. Historical configuration records allow retrospective auditing and verification of compliance.
KMS provides centralized key management and encryption capabilities. Environment variables containing sensitive information can be encrypted with KMS-managed keys, ensuring that only authorized principals can access them. All key usage events are logged in CloudTrail, providing a comprehensive audit trail of decryption and access events. Automated key rotation reduces the risk of compromise and ensures continuous protection.
Lambda itself enforces role-based access controls to prevent unauthorized code modifications. Integrating Lambda with Config and KMS establishes a multi-layered security approach. Config detects noncompliant configurations, KMS enforces encryption, and Lambda roles restrict access to code. Automated remediation workflows via EventBridge or Systems Manager can re-encrypt variables, disable noncompliant functions, or roll back code changes, ensuring operational security without manual intervention.
S3 and Athena provide storage and analytics but do not enforce encryption of Lambda environment variables, prevent unauthorized code changes, or provide continuous compliance monitoring.
CloudFront and WAF enhance web application security but cannot enforce Lambda encryption, protect code integrity, or maintain an auditable compliance trail.
QuickSight and CloudTrail allow visualization and auditing of AWS activity, but while CloudTrail logs API calls, this combination does not enforce encryption or prevent unauthorized changes proactively. It is primarily a post-event auditing solution.
The combination of AWS Config, Lambda, and KMS ensures encryption, code integrity, and full auditability. This integrated solution provides continuous monitoring, automated enforcement of security policies, and operational visibility. Multi-layered enforcement reduces risk, ensures compliance, and protects sensitive financial transactions, making it the correct solution.
Question 178
A company wants to deploy ECS services using Fargate and automatically scale them based on custom application metrics, such as the number of requests in a queue. Which AWS service combination enables this behavior?
A) CloudWatch custom metrics + ECS Service Auto Scaling
B) Lambda + DynamoDB
C) S3 + Athena
D) CloudFront + WAF
Answer: A) CloudWatch custom metrics + ECS Service Auto Scaling
Explanation:
Amazon CloudWatch allows organizations to create custom metrics in addition to the default metrics provided by ECS. These custom metrics can be based on application-specific indicators, such as the number of messages in an SQS queue, transaction counts, or user requests per second. By publishing these custom metrics to CloudWatch, operations teams can monitor real-time application behavior and detect workload surges that may require scaling actions. Custom metrics provide more granular visibility into the operational state of the application, which is particularly important for microservices or specialized workloads that rely on business-specific indicators rather than just CPU or memory usage.
ECS Service Auto Scaling can consume both default and custom CloudWatch metrics to make scaling decisions. Scaling policies define thresholds for scaling out or scaling in. For example, a custom metric indicating the queue length in a message broker could trigger ECS to launch additional tasks when the queue exceeds a certain limit. Conversely, when the queue length decreases below the threshold, tasks can be terminated to optimize costs. This automation ensures that application performance is maintained, reduces latency for end users, and avoids over-provisioning.
Lambda and DynamoDB provide serverless compute and database functionality but do not natively scale ECS services based on custom metrics. While Lambda could be used to process custom metrics and trigger scaling actions, it requires manual orchestration and does not provide the seamless integration and automation of CloudWatch with ECS Auto Scaling.
S3 and Athena provide storage and analytical capabilities. They can store logs or historical application metrics, which can be queried for trends or reports, but they cannot trigger real-time scaling actions. Using them alone does not satisfy the need for automated task scaling based on custom application workloads.
Amazon CloudFront, when combined with AWS Web Application Firewall (WAF), enhances both content delivery and application security. CloudFront acts as a global content delivery network, caching static assets at edge locations worldwide, which reduces latency and improves load times for end users. WAF adds a layer of protection by allowing teams to define rules that block common web exploits, such as SQL injection or cross-site scripting, helping safeguard applications from malicious traffic. Together, these services ensure faster and more secure delivery of web content. However, while CloudFront and WAF optimize delivery and security, they do not provide performance monitoring or automated scaling for backend services like Amazon ECS. CloudFront focuses on caching and traffic distribution, and WAF focuses on threat mitigation, but neither tracks the utilization, health, or performance metrics of ECS containers. They cannot automatically adjust the number of running tasks in response to load changes or monitor CPU, memory, or request latency at the container level. For ECS performance monitoring and scaling, services like CloudWatch for metrics and alarms, along with ECS Service Auto Scaling, are required. In summary, CloudFront and WAF improve speed and security but do not address the operational management or elasticity needs of containerized applications running in ECS.
By combining CloudWatch custom metrics with ECS Service Auto Scaling, the company gains a fully automated, scalable, and resilient architecture. Metrics accurately reflect workload demand, and Auto Scaling ensures ECS services dynamically adjust task counts based on real-time conditions. This reduces operational overhead, maintains performance, and optimizes costs, making it the correct solution.
Question 179
A company wants to deploy a web application globally with minimal latency and automatic failover for unhealthy regions. Which AWS service combination ensures both high performance and reliability?
A) Route 53 latency-based routing + health checks + CloudWatch
B) CloudFront + S3
C) Direct Connect + VPC Peering
D) Lambda + DynamoDB
Answer: A) Route 53 latency-based routing + health checks + CloudWatch
Explanation:
Amazon Route 53 offers latency-based routing, which ensures that user requests are directed to the AWS region with the lowest network latency. This is critical for global applications where performance is directly impacted by distance and network conditions. Multiple endpoints across regions can be configured, allowing Route 53 to dynamically select the fastest, healthy endpoint for each user request. This minimizes response times, enhancing the user experience.
Health checks integrated with Route 53 continuously monitor endpoints to ensure availability. If a region becomes unhealthy due to infrastructure issues or application failures, Route 53 automatically reroutes traffic to a healthy region. Health checks can monitor HTTP/S responses, TCP ports, or custom application-level indicators, providing precise failover behavior. This automated failover ensures high availability and business continuity without manual intervention.
CloudWatch complements this setup by providing real-time monitoring of endpoint metrics, including latency, error rates, request counts, and throughput. Dashboards allow operations teams to visualize trends, identify anomalies, and troubleshoot performance issues proactively. Alarms can notify teams of degraded performance, automatically triggering workflows or notifications to maintain service reliability. Historical data stored in CloudWatch supports capacity planning and proactive optimization.
Amazon CloudFront and Amazon S3 are commonly used together to enhance the delivery of static content, such as images, videos, JavaScript, CSS files, and other assets, across the globe. S3 provides durable, scalable, and highly available object storage for static content, while CloudFront acts as a content delivery network (CDN) that caches these objects at edge locations distributed worldwide. By caching content closer to end users, CloudFront reduces latency, improves load times, and decreases the load on the origin S3 bucket. This combination is highly effective for serving static assets efficiently, ensuring users experience faster access to frequently requested files, regardless of their geographic location. CloudFront also provides additional benefits, such as HTTPS support, access control through signed URLs, and integration with AWS Shield for DDoS protection, enhancing both security and performance. However, while CloudFront and S3 are optimized for static content delivery, they do not inherently provide global routing for dynamic endpoints. Requests that require server-side computation, database queries, or real-time API responses are routed to the origin servers without the benefit of caching, meaning latency improvements may be limited for dynamic content. Additionally, neither service includes automated failover across multiple regions. If the origin S3 bucket or a CloudFront edge location becomes unavailable, requests may fail or experience delays, as there is no built-in mechanism to redirect traffic to a healthy region automatically. Similarly, these services do not include health monitoring for regions or endpoints, so they cannot proactively detect and route traffic away from unhealthy or overloaded resources. While they optimize performance for static assets, they do not guarantee consistent low latency or high availability for all types of traffic, particularly for dynamic, transactional, or real-time workloads. To achieve these capabilities, organizations typically combine CloudFront and S3 with additional services such as Route 53 for global DNS-based routing, Elastic Load Balancing for distributing dynamic requests across healthy instances, and monitoring tools like CloudWatch or third-party observability platforms to detect and respond to failures. CloudFront and S3 excel at improving performance for static content by leveraging caching at edge locations, reducing latency, and offloading traffic from origin storage. However, they are not designed to replace the mechanisms required for global traffic routing, automated failover, or health monitoring, which are essential for ensuring reliability and performance for dynamic or critical workloads.
Direct Connect and VPC Peering improve private network connectivity between data centers and AWS regions or between VPCs. While valuable for low-latency private connections, these services do not provide global routing, health checks, or automatic failover.
Lambda and DynamoDB provide serverless compute and database storage but cannot route traffic globally, monitor endpoint health, or handle failover for a multi-region web application.
Combining Route 53 latency-based routing, health checks, and CloudWatch ensures global users are directed to the fastest, healthy endpoints, endpoints are continuously monitored, and operations teams receive actionable insights. This fully managed solution provides low latency, high availability, and operational visibility, making it the correct choice.
Question 180
A company processes sensitive transactions using AWS Lambda. They require encryption of environment variables, prevention of unauthorized code changes, and a fully auditable compliance trail. Which AWS service combination fulfills these requirements?
A) AWS Config + Lambda + KMS
B) S3 + Athena
C) CloudFront + WAF
D) QuickSight + CloudTrail
Answer: A) AWS Config + Lambda + KMS
Explanation:
AWS Config continuously monitors and evaluates AWS resources against predefined compliance rules. For Lambda functions processing sensitive transactions, Config ensures that environment variables are encrypted with KMS keys and alerts when configurations drift from compliance policies. Config logs detailed information on noncompliant changes, providing a fully auditable trail. Historical configuration records allow organizations to verify compliance retrospectively, which is critical for regulated industries like finance. Continuous monitoring also ensures that security and governance standards are consistently enforced.
AWS Key Management Service (KMS) provides centralized key management and encryption capabilities. Lambda environment variables containing sensitive data can be encrypted using KMS-managed keys, ensuring that only authorized users or roles can decrypt and access them. KMS logs all key usage in CloudTrail, providing a complete record of encryption and decryption operations. Automated key rotation reduces the risk of compromise and ensures continuous protection of sensitive information.
Lambda itself enforces role-based access to prevent unauthorized code modifications. Integrating Lambda with Config and KMS provides a multi-layered security approach. Config continuously evaluates compliance, KMS enforces encryption, and Lambda roles restrict access to function code. Automated remediation workflows can re-encrypt variables, disable noncompliant functions, or roll back unauthorized code changes. This ensures operational security without requiring manual intervention.
S3 and Athena provide storage and analytics, but do not enforce encryption of Lambda environment variables, prevent unauthorized code changes, or maintain continuous compliance monitoring.
CloudFront and WAF enhance application security and delivery, but cannot enforce Lambda encryption, protect code integrity, or provide a compliance audit trail.
QuickSight and CloudTrail allow visualization and auditing, but do not actively enforce encryption or prevent unauthorized changes. CloudTrail logs API cal,l s, and QuickSight visualizes trends, but they provide post-event insights rather than preventive compliance enforcement.
Using AWS Config with Lambda and KMS provides encryption, code integrity, and a complete audit trail. Continuous monitoring, automated remediation, and historical records ensure regulatory compliance, operational security, and protection of sensitive transaction data, making it the correct solution.