Amazon AWS Certified DevOps Engineer — Professional DOP-C02 Exam Dumps and Practice Test Questions Set 3 Q31-45

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Question 31

A DevOps engineer needs to deploy a highly available containerized application in AWS without managing servers. The solution should automatically scale based on demand. Which service combination is most appropriate?

A) Amazon ECS with AWS Fargate
B) Amazon EKS with EC2 worker nodes
C) AWS Lambda with API Gateway
D) Amazon EC2 Auto Scaling with Docker installed

Answer:  A) Amazon ECS with AWS Fargate

Explanation:

Amazon ECS with AWS Fargate provides a serverless container orchestration platform. Fargate allows containers to run without managing the underlying EC2 instances, which reduces operational overhead. ECS orchestrates container tasks and services, ensuring that containers are deployed reliably. By defining task definitions and services, DevOps teams can specify the required CPU and memory for each container and configure auto-scaling policies based on CloudWatch metrics. This ensures the application scales automatically in response to traffic spikes or reductions. Fargate abstracts cluster management and patching, allowing engineers to focus entirely on application logic and performance.

Amazon EKS with EC2 worker nodes provides Kubernetes-based orchestration, but it requires management of the EC2 worker nodes. While EKS offers high flexibility and Kubernetes features, operational overhead increases because the team must maintain node health, patch AMIs, and monitor cluster scaling. This does not meet the requirement of minimizing server management for a fully serverless deployment.

AWS Lambda with API Gateway is suitable for event-driven or short-lived workloads. While it is serverless and scales automatically, it is not ideal for long-running or persistent containerized applications that require networking, volumes, or complex orchestration. Lambda has execution time and memory constraints that limit its ability to fully replace container workloads for persistent applications.

Amazon EC2 Auto Scaling with Docker installed allows containers to run on EC2 instances, but the operational burden remains. The team must manage EC2 instances, apply patches, monitor health, and scale instances manually or via scaling policies. This increases complexity and reduces the benefit of serverless operations.

ECS with Fargate is the best choice because it provides full serverless container management, automated scaling, and high availability. Tasks can be distributed across multiple Availability Zones, ensuring fault tolerance. Fargate’s integration with CloudWatch allows monitoring of metrics and automatic scaling without manual intervention. Security is enhanced since each container runs in an isolated environment, and IAM roles can be assigned per task for least-privilege access. This combination enables DevOps teams to deploy containerized applications quickly, reliably, and with minimal operational overhead. It supports CI/CD pipelines, Blue/Green deployments, and can integrate with service discovery and networking features for microservices architectures. ECS with Fargate offers a complete solution for running scalable, highly available containerized applications in AWS without managing servers. The abstraction of infrastructure, combined with robust orchestration, automated monitoring, and seamless scaling, aligns perfectly with best practices for serverless container deployments. It also simplifies compliance, security auditing, and cost management, as resources are billed only for what is used.

Question 32

A company wants to automate the deployment of infrastructure and applications across multiple AWS accounts while ensuring compliance with corporate policies. Which AWS service is best suited to manage this multi-account environment?

A) AWS Control Tower
B) AWS CloudFormation
C) AWS Config
D) AWS Systems Manager

Answer:  A) AWS Control Tower

Explanation:

AWS Control Tower provides a managed multi-account setup for AWS organizations. It automates account provisioning, applies pre-configured security baselines, and sets up guardrails to ensure compliance with corporate policies. Control Tower integrates with AWS Organizations to create a structured landing zone, allowing centralized management of multiple accounts. Predefined guardrails enforce policies related to security, logging, and resource configurations, reducing the risk of misconfigurations. It also provides dashboards for visibility across all accounts, helping DevOps teams monitor compliance and operational health in real time.

AWS CloudFormation is excellent for provisioning infrastructure as code. It enables consistent deployment of resources and environments, but managing multiple accounts with CloudFormation requires manual orchestration and separate stacks per account. CloudFormation does not provide centralized multi-account governance or automated compliance enforcement across accounts, which is critical for enterprise environments.

AWS Config continuously monitors resource configurations and evaluates compliance against defined rules. While Config provides visibility and auditing, it does not automate account creation or enforce organizational structures across multiple AWS accounts. Config helps maintain compliance at the resource level, but is not a comprehensive solution for multi-account governance.

AWS Systems Manager automates operational tasks, including patching and configuration management. While it can manage resources across accounts using cross-account roles, it does not provide centralized account provisioning, policy enforcement, or landing zone setup. Systems Manager complements Control Tower but cannot replace its functionality for multi-account governance.

AWS Control Tower is the optimal solution because it provides automated account creation, guardrails enforcement, centralized logging, and governance across multiple AWS accounts. DevOps teams can deploy compliant infrastructure and applications quickly while adhering to corporate policies. Control Tower integrates with CloudTrail and Config for continuous monitoring, ensures secure baselines, and reduces operational overhead for managing multiple accounts. It simplifies scaling in multi-account environments and ensures that security and compliance are maintained automatically. By using Control Tower, organizations achieve a consistent, repeatable, and auditable multi-account structure that aligns with AWS best practices. This approach also reduces risks associated with human error, accelerates onboarding of new accounts, and ensures that corporate standards are enforced automatically from the moment accounts are provisioned.

Question 33

A DevOps engineer wants to implement centralized logging for applications running on EC2, Lambda, and ECS. The solution should provide search, visualization, and alerting capabilities. Which AWS service combination is most appropriate?

A) Amazon CloudWatch Logs + CloudWatch Dashboards + CloudWatch Alarms
B) AWS Config + Amazon S3
C) AWS CloudTrail + AWS Lambda
D) Amazon S3 + Amazon Athena

Answer:  A) Amazon CloudWatch Logs + CloudWatch Dashboards + CloudWatch Alarms

Explanation:

Amazon CloudWatch Logs centralizes log data from multiple AWS services, including EC2, Lambda, and ECS. Logs can be streamed directly to CloudWatch Logs using the CloudWatch agent, Lambda’s built-in logging, or ECS task logging drivers. Once centralized, logs can be indexed, filtered, and searched in real time. CloudWatch Dashboards allow visualization of metrics, logs, and custom queries in a single pane, providing a comprehensive overview of application and infrastructure health. CloudWatch Alarms can be configured to trigger notifications via SNS or automated actions if specific thresholds are exceeded, enabling proactive monitoring and operational response.

AWS Config tracks resource configuration changes and evaluates compliance, but does not provide real-time logging or alerting for application events. Config is focused on governance, auditing, and compliance rather than operational monitoring and visualization of logs.

AWS CloudTrail records API activity and captures audit trails for security and compliance. While CloudTrail logs are essential for governance and auditing, they do not include application-level logs or metrics from EC2, Lambda, or ECS. CloudTrail alone does not provide visualization or search capabilities for real-time operational monitoring.

Amazon S3 can store logs and raw data, and Amazon Athena allows querying logs stored in S3. While this combination enables analysis, it is batch-oriented and does not provide real-time alerting or immediate visualization. It requires additional configuration and lacks the operational immediacy needed for proactive monitoring of live applications.

CloudWatch Logs combined with Dashboards and Alarms is the most suitable solution because it provides real-time logging, visualization, and alerting across multiple AWS services. This enables DevOps teams to detect issues quickly, correlate logs with metrics, and respond to operational incidents proactively. Integration with SNS allows notifications to be sent automatically when errors or anomalies are detected. CloudWatch provides centralized operational insight, reduces the time to identify root causes, and supports scalable monitoring across multiple services and environments. By using CloudWatch, teams can maintain a single pane of observability, improve incident response, and ensure operational reliability across EC2, Lambda, and ECS deployments. Dashboards enable visual correlation between different metrics and logs, while alarms provide automated triggers for immediate remediation actions. This combination ensures comprehensive monitoring, faster troubleshooting, and continuous operational awareness.

Question 34

A DevOps engineer wants to implement automated rollback for a Lambda function if new code deployments increase error rates above a specific threshold. Which AWS service or combination should be used?

A) AWS CodeDeploy + CloudWatch Alarms
B) AWS CloudTrail + SNS
C) AWS Config + Lambda
D) Amazon CloudFront + CloudWatch

Answer:  A) AWS CodeDeploy + CloudWatch Alarms

Explanation:

AWS CodeDeploy supports automated deployment strategies, including blue/green and canary deployments for Lambda functions. Blue/green deployment allows new versions of a Lambda function to be deployed to a separate environment while the old version continues serving traffic. CodeDeploy monitors the health of the new version using CloudWatch metrics, such as error rates, duration, or throttles. By integrating CloudWatch Alarms, CodeDeploy can automatically roll back to the previous stable version if metrics exceed defined thresholds. This ensures minimal impact on end users and maintains service reliability. Developers can define alarms for critical errors or latency issues, and CodeDeploy will stop traffic to the new version and redirect it back to the previous version, providing a fully automated rollback mechanism.

AWS CloudTrail logs API calls and changes to AWS resources, which is valuable for auditing and compliance. While CloudTrail records deployment events, it does not provide real-time monitoring of application performance, metrics evaluation, or automated rollback capabilities. Using CloudTrail alone does not enable proactive responses to increased error rates.

AWS Config monitors resource configurations and evaluates compliance with defined rules. While Config helps detect non-compliant configurations, it does not monitor Lambda function metrics in real time or trigger automated rollbacks. Config is primarily for auditing and governance rather than application performance management.

Amazon CloudFront is a content delivery network that caches content at edge locations to improve performance. While CloudFront integrates with CloudWatch for monitoring, it does not orchestrate Lambda function deployments or manage rollbacks. CloudFront is irrelevant to the automated rollback of Lambda functions based on error thresholds.

CodeDeploy combined with CloudWatch Alarms is the correct solution because it automates deployment monitoring and rollback actions based on predefined performance thresholds. This ensures that any increase in error rates during deployment triggers an immediate rollback, maintaining service availability and reliability. The integration supports multiple deployment strategies, including canary and linear deployments, providing flexibility in traffic shifting while mitigating risk. It allows DevOps engineers to define precise thresholds for alarms, monitor multiple metrics, and ensure that only healthy Lambda versions continue serving traffic. By automating rollback procedures, CodeDeploy and CloudWatch reduce operational risk, minimize downtime, and maintain user confidence. This combination aligns with DevOps best practices for continuous deployment, proactive monitoring, and automated recovery. It also integrates with SNS to notify teams of deployment status or rollback actions, enabling timely incident management and ensuring that deployments are safe, predictable, and resilient.

Question 35

A company wants to automatically enforce encryption for all S3 buckets created in their AWS environment and receive alerts for unencrypted buckets. Which AWS service combination should be implemented?

A) AWS Config + Amazon SNS
B) Amazon CloudWatch + Lambda
C) AWS CloudTrail + Amazon S3
D) AWS Systems Manager + CloudFormation

Answer:  A) AWS Config + Amazon SNS

Explanation:

AWS Config allows organizations to define rules that enforce resource compliance, such as ensuring S3 buckets are encrypted using server-side encryption (SSE) with AWS-managed or customer-managed keys. Config continuously monitors all resources in the account and detects non-compliant S3 buckets. When a bucket is found to be unencrypted, Config can trigger an evaluation and, through integration with Amazon SNS, send automated notifications to the DevOps or security teams. This enables proactive monitoring and ensures compliance with security standards. Alerts can be routed to email addresses, Slack channels, or ticketing systems to ensure that violations are addressed promptly.

Amazon CloudWatch collects metrics and logs, and Lambda can be used for custom remediation. While this combination could detect unencrypted buckets via event-driven triggers, it requires more custom development to identify violations and implement notifications. Unlike Config, CloudWatch, and Lambda do not provide prebuilt compliance rules for AWS resources, making the setup more complex and less standardized.

AWS CloudTrail tracks API calls and provides a record of resource creation and modifications. While it can log the creation of unencrypted S3 buckets, CloudTrail alone does not enforce compliance or trigger automated notifications based on resource configuration. Using CloudTrail would require additional processing and custom alerts to identify non-compliant buckets, adding complexity to enforcement.

AWS Systems Manager and CloudFormation are used for operational automation and infrastructure provisioning. CloudFormation can define S3 buckets with encryption enabled during deployment, but it cannot automatically detect or remediate non-compliant resources created outside of CloudFormation templates. Systems Manager can manage configuration at the instance level, but it does not enforce bucket-level encryption automatically.

AWS Config combined with SNS is the optimal solution because Config provides continuous compliance monitoring with prebuilt and custom rules, and SNS enables real-time notification of violations. This combination allows DevOps teams to enforce security best practices automatically, respond quickly to non-compliance, and maintain a secure environment. Alerts can be monitored centrally, ensuring that unencrypted buckets are detected and remediated promptly. Config rules also provide detailed compliance reports and history, helping with auditing and regulatory requirements. By using Config and SNS, organizations gain automated enforcement of encryption policies, reduce the risk of data exposure, and simplify security governance across multiple accounts and regions. The prebuilt AWS managed rule for S3 bucket encryption ensures rapid deployment and immediate detection without complex custom scripting. This approach standardizes security controls, improves accountability, and aligns with DevSecOps practices for automated compliance and alerting.

Question 36

A DevOps team wants to implement centralized monitoring for application latency, error rates, and throughput across multiple microservices deployed on ECS, Lambda, and API Gateway. Which AWS service provides the most comprehensive solution?

A) Amazon CloudWatch Observability
B) AWS Config
C) AWS CloudTrail
D) Amazon S3 + Athena

Answer:  A) Amazon CloudWatch Observability

Explanation:

Amazon CloudWatch Observability provides a unified platform for monitoring metrics, logs, and traces across multiple AWS services. For ECS, Lambda, and API Gateway, CloudWatch can collect application metrics, logs, and distributed traces, enabling DevOps engineers to visualize performance, identify bottlenecks, and troubleshoot errors across microservices. Observability allows correlation between different service metrics and logs, helping teams understand end-to-end request flows and pinpoint root causes. CloudWatch dashboards allow real-time visualization, while CloudWatch Alarms can trigger automated notifications or remediation actions based on threshold violations.

AWS Config monitors resource configurations and compliance. While it provides valuable auditing and governance insights, it does not track operational metrics, error rates, or latency for applications. Config evaluates resource settings rather than application performance and is not suitable for end-to-end observability.

AWS CloudTrail records API activity for auditing purposes. While CloudTrail logs are essential for security monitoring, they do not provide real-time metrics, performance visualization, or tracing of requests across microservices. CloudTrail alone cannot correlate errors, latency, and throughput metrics for operational monitoring.

Amazon S3 stores logs, and Athena enables querying of stored data. While this combination can provide post-hoc analysis, it is batch-oriented and does not support real-time monitoring or automated alerting. Using S3 and Athena requires additional setup for ingestion, querying, and visualization, and lacks immediate operational observability for ongoing performance issues.

CloudWatch Observability is the correct solution because it provides centralized, real-time monitoring of metrics, logs, and traces for ECS, Lambda, and API Gateway. Engineers can detect performance anomalies, correlate data across services, and trigger automated notifications or actions. Observability enables microservice-level monitoring, end-to-end latency tracing, error analysis, and throughput measurement. Dashboards provide comprehensive visualization, while alarms ensure proactive incident response. By centralizing monitoring, teams can reduce MTTR, improve application reliability, and maintain operational visibility across multiple microservices environments. Observability also integrates with CloudWatch ServiceLens to provide service maps and distributed tracing, enhancing understanding of dependencies and bottlenecks. This solution supports CI/CD pipelines, automated alerting, and continuous improvement of application performance and reliability. CloudWatch Observability is essential for managing complex architectures with multiple interconnected services while providing a unified view of performance, availability, and operational health.

Question 37

A DevOps engineer wants to implement continuous integration and delivery for a serverless application that uses Lambda, API Gateway, and DynamoDB. The pipeline should automatically build, test, and deploy code whenever changes are pushed to the repository. Which AWS service combination is most appropriate?

A) AWS CodePipeline + AWS CodeBuild + AWS CodeDeploy
B) AWS CloudFormation + AWS Config
C) AWS Systems Manager + CloudWatch Alarms
D) Amazon S3 + Amazon Athena

Answer:  A) AWS CodePipeline + AWS CodeBuild + AWS CodeDeploy

Explanation:

AWS CodePipeline provides an orchestration service for continuous integration and continuous delivery. It enables automated workflows triggered by code changes in repositories such as AWS CodeCommit, GitHub, or Bitbucket. In a serverless architecture using Lambda, API Gateway, and DynamoDB, CodePipeline coordinates the build, test, and deployment stages. AWS CodeBuild is a fully managed build service that compiles source code, runs unit tests, and produces deployment artifacts, such as zipped Lambda packages. CodeDeploy integrates with Lambda to manage deployment strategies like blue/green deployments, monitor Lambda function health, and roll back automatically if errors are detected.

AWS CloudFormation automates resource provisioning and infrastructure as code, but does not provide CI/CD orchestration for application code. While CloudFormation templates can be integrated into pipelines, they do not inherently manage builds, tests, or automated deployments for serverless applications.

AWS Config monitors resource configurations and enforces compliance. While valuable for auditing and governance, it does not automate code deployments or manage build and test workflows. Config is focused on configuration tracking rather than end-to-end CI/CD pipelines.

AWS Systems Manager provides operational automation, such as patching, run commands, and configuration management. While Systems Manager can automate operational tasks, it is not designed for orchestrating builds, tests, and deployments for serverless applications. It complements CI/CD workflows but does not replace the core functionality provided by CodePipeline, CodeBuild, and CodeDeploy.

The combination of CodePipeline, CodeBuild, and CodeDeploy is optimal because it provides a fully automated CI/CD solution for serverless applications. Changes pushed to the repository trigger the pipeline, which builds, tests, and deploys code with minimal manual intervention. CodeDeploy ensures reliable deployment strategies, monitors Lambda function performance, and supports automatic rollback if metrics indicate failures. This approach reduces human error, accelerates deployment frequency, improves reliability, and ensures alignment with DevOps best practices. CodePipeline dashboards and CloudWatch integration provide monitoring, notifications, and auditability across the CI/CD process, enabling teams to maintain operational visibility while accelerating innovation.

Question 38

A company wants to enforce centralized tagging for all AWS resources to improve cost allocation and operational visibility. The tagging policy should automatically detect untagged resources and notify the operations team. Which AWS service combination is most suitable?

A) AWS Config + Amazon SNS
B) AWS CloudTrail + Lambda
C) AWS Systems Manager + CloudWatch Logs
D) Amazon S3 + Athena

Answer:  A) AWS Config + Amazon SNS

Explanation:

AWS Config allows organizations to define custom rules to enforce tagging policies across AWS resources. A rule can detect resources that do not comply with the defined tagging requirements and trigger compliance evaluation. When Config detects an untagged or mis-tagged resource, it can initiate a notification through Amazon SNS, which alerts the operations team immediately. This ensures consistent tagging practices, enables accurate cost allocation using AWS Cost Explorer, and provides operational visibility across accounts. Config rules can be customized to enforce mandatory tags such as environment, project, or owner, ensuring resources are aligned with governance and accounting policies.

AWS CloudTrail logs API activity for auditing purposes. While CloudTrail records resource creation and modifications, it does not actively evaluate compliance against tagging policies or trigger notifications. Using CloudTrail alone would require additional custom automation to detect untagged resources, increasing operational complexity.

AWS Systems Manager provides operational management for resources, including patching and configuration. While Systems Manager can automate operational tasks, it is not designed for enforcing tagging policies or alerting teams about missing tags. Systems Manager complements Config but cannot replace it for automated governance and notification workflows.

Amazon S3 stores logs or structured data, and Athena allows querying stored data. While this setup can analyze tags retrospectively, it is batch-oriented and cannot provide real-time notifications or proactive compliance enforcement. Athena queries are run manually or on scheduled intervals, which may delay detection of untagged resources.

AWS Config combined with Amazon SNS is the best solution because it continuously monitors resource compliance against tagging policies and provides immediate notifications. Config rules enforce consistency, reduce human error, improve cost allocation accuracy, and support operational visibility across all AWS resources. By automating detection and alerting, teams can remediate untagged resources quickly and maintain organizational governance. Integration with other AWS services ensures that monitoring is continuous and scalable across multiple accounts, regions, and services. The combination of Config and SNS provides centralized control, automated enforcement, and real-time alerts, helping organizations maintain compliance, optimize costs, and reduce operational overhead effectively.

Question 39

A DevOps engineer needs to deploy a multi-region highly available database that automatically replicates data and provides low-latency reads to users globally. Which AWS service is most appropriate?

A) Amazon DynamoDB Global Tables
B) Amazon RDS Multi-AZ
C) Amazon Aurora Single Region
D) Amazon ElastiCache

Answer:  A) Amazon DynamoDB Global Tables

Explanation:

Amazon DynamoDB Global Tables is a fully managed, multi-region, multi-master NoSQL database solution. It automatically replicates data across multiple regions, providing low-latency access for globally distributed users. Updates to any table in one region are propagated to other regions in near real time, ensuring eventual consistency or strong consistency based on the application requirements. Global Tables eliminate the need to build custom replication solutions, reduce operational overhead, and provide high availability even if one region experiences an outage. With DynamoDB Streams and AWS Lambda integration, developers can implement additional data processing or event-driven workflows across regions.

Amazon RDS Multi-AZ provides high availability within a single region. It automatically synchronizes data between a primary and standby instance in different Availability Zones, ensuring failover capability within the same region. However, RDS Multi-AZ does not provide low-latency reads for globally distributed users because it is confined to a single region. Cross-region replication is possible with read replicas, but setup is manual and lacks full multi-master capabilities.

Amazon Aurora Single Region provides high availability within one region and supports read replicas for scaling reads. While it offers performance improvements and failover capabilities, it does not provide multi-region automatic replication or global low-latency access. Global Aurora requires additional configuration for cross-region replication, and it is not as seamless as DynamoDB Global Tables for distributed workloads.

Amazon ElastiCache provides in-memory caching to accelerate application performance. While ElastiCache reduces latency for frequently accessed data, it is not a database and does not provide persistent storage or automated global replication. It cannot serve as a primary data store for globally distributed applications requiring multi-region availability.

DynamoDB Global Tables is the optimal solution because it ensures automatic multi-region replication, low-latency reads, and multi-master write capabilities. It supports fault tolerance, high availability, and strong performance for globally distributed applications. By using Global Tables, DevOps teams can deploy applications that serve users worldwide without building complex replication logic, while also benefiting from a fully managed, serverless NoSQL solution. Global Tables simplify operational management, reduce infrastructure complexity, and allow developers to focus on application logic rather than database replication or failover mechanisms. Integration with IAM, CloudWatch, and CloudTrail provides monitoring, auditing, and security capabilities, ensuring compliance and operational visibility. This approach provides a seamless, scalable, and highly available global data solution that meets the demands of modern multi-region applications.

Question 40

A DevOps team wants to implement real-time anomaly detection for application metrics such as CPU utilization, memory usage, and request latency. The solution should automatically send notifications when anomalies occur. Which AWS service combination is most suitable?

A) Amazon CloudWatch Anomaly Detection + Amazon SNS
B) AWS Config + AWS CloudTrail
C) AWS Systems Manager + CloudWatch Logs
D) Amazon S3 + Amazon Athena

Answer:  A) Amazon CloudWatch Anomaly Detection + Amazon SNS

Explanation:

Amazon CloudWatch Anomaly Detection automatically analyzes historical metric data to create a model of expected behavior. It continuously evaluates incoming metrics such as CPU utilization, memory usage, and request latency against this model. When the actual metrics deviate from predicted values, CloudWatch flags the event as an anomaly. This is particularly useful for identifying unusual patterns that might indicate operational issues, performance degradation, or potential security incidents. Integrating CloudWatch Anomaly Detection with Amazon SNS ensures that notifications are sent immediately to the operations team or relevant stakeholders. SNS supports multiple endpoints, including email, SMS, and HTTP endpoints, providing flexible alerting options.

AWS Config monitors resource configurations and evaluates compliance against predefined rules. While Config provides auditing and governance, it does not detect operational anomalies or trigger real-time alerts based on dynamic metric deviations. It focuses on ensuring resource compliance rather than detecting performance or operational anomalies.

AWS Systems Manager provides operational automation and management for EC2 instances and other AWS resources. It is not designed to perform anomaly detection on application metrics. While it can automate remediation actions, it lacks the statistical modeling and predictive capabilities necessary for detecting unexpected deviations in real-time operational metrics.

Amazon S3 and Amazon Athena enable storage and query of log and metric data. While this combination is useful for post-hoc analysis and reporting, it does not provide real-time anomaly detection or automatic notifications. Athena queries are run manually or on schedules, which delays detection of anomalies and prevents immediate operational response.

CloudWatch Anomaly Detection combined with SNS is the optimal solution because it continuously monitors metrics, detects unexpected deviations in real time, and automatically notifies the operations team. This allows rapid response to issues before they escalate, reducing downtime and improving application reliability. By leveraging anomaly detection, DevOps teams can detect subtle trends that traditional threshold-based alarms might miss. This approach supports predictive monitoring, enabling proactive operations rather than reactive troubleshooting. The integration with SNS ensures timely delivery of alerts across multiple channels, maintaining situational awareness across distributed teams. Additionally, anomaly detection models are continuously updated with new data, improving accuracy over time. CloudWatch provides operational visibility through dashboards, visualizations, and detailed metrics correlation, allowing teams to quickly diagnose the root cause of anomalies. This combination aligns with modern DevOps best practices, ensuring operational resilience, minimizing downtime, and improving the overall user experience through automated monitoring and alerting for anomalies.

Question 41

A DevOps engineer wants to enforce centralized secrets management for multiple applications running in AWS Lambda. Secrets should be rotated automatically without redeploying the functions. Which AWS service is most suitable?

A) AWS Secrets Manager
B) AWS Systems Manager Parameter Store
C) AWS Config
D) Amazon S3

Answer:  A) AWS Secrets Manager

Explanation:

AWS Secrets Manager provides a secure mechanism to store, retrieve, and rotate secrets such as API keys, database credentials, or other sensitive configuration information. For Lambda functions, Secrets Manager can store secrets as environment variables or programmatically retrieve them at runtime. Automatic rotation is built into Secrets Manager, allowing secrets to be updated periodically according to defined schedules without redeploying Lambda functions. This ensures that credentials remain secure and reduces operational overhead. Access can be controlled through IAM roles, ensuring that only authorized Lambda functions or users can retrieve the secrets. Additionally, Secrets Manager integrates with CloudWatch for auditing and monitoring access events.

AWS Systems Manager Parameter Store can store configuration values and secure strings encrypted with KMS. While Parameter Store supports versioning and encryption, it does not provide built-in automatic rotation of secrets. Implementing rotation with Parameter Store requires custom Lambda automation or scripts, which increases operational complexity and risk.

AWS Config monitors resource configurations and enforces compliance with rules. While useful for auditing and detecting misconfigurations, Config does not manage secrets, rotation, or runtime retrieval for Lambda functions. It focuses on governance rather than secure secret management.

Amazon S3 provides object storage, which could store encrypted secrets, but it does not provide automatic rotation, access control tailored for secrets, or seamless Lambda integration. Using S3 for secret management requires custom development and increases security risk.

AWS Secrets Manager is the optimal solution because it provides centralized, automated, and secure secret management. Secrets are rotated automatically according to policies, reducing the risk of compromised credentials. Lambda functions retrieve secrets at runtime without redeployment, minimizing operational interruptions. IAM policies control access, ensuring least-privilege security. Integration with CloudWatch and CloudTrail provides logging and auditing for compliance. Secrets Manager simplifies operational workflows, reduces human error, and enhances security posture for multi-application deployments, aligning with DevOps best practices for secrets management in serverless architectures. It is fully managed, scalable, and supports enterprise-grade security features like encryption, access logging, and automatic rotation, making it ideal for modern applications deployed across multiple environments.

Question 42

A company wants to implement an automated CI/CD pipeline for containerized applications deployed on Amazon ECS that includes build, test, image scanning, and deployment. Which AWS service combination is best suited for this workflow?

A) AWS CodePipeline + AWS CodeBuild + Amazon ECR + AWS CodeDeploy
B) AWS CloudFormation + AWS Config
C) Amazon CloudWatch + AWS Lambda
D) Amazon S3 + Amazon Athena

Answer:  A) AWS CodePipeline + AWS CodeBuild + Amazon ECR + AWS CodeDeploy

Explanation:

AWS CodePipeline provides orchestration for CI/CD workflows, enabling automation of building, testing, and deploying containerized applications. CodeBuild serves as the build and test environment, allowing Docker images to be built and tested before deployment. Amazon ECR stores container images securely, and ECR image scanning can be integrated into the pipeline to identify vulnerabilities in container images. AWS CodeDeploy manages the deployment of ECS services, supporting rolling or blue/green deployment strategies to reduce downtime and ensure stability during updates. This combination ensures a fully automated and secure pipeline for ECS applications.

AWS CloudFormation is primarily for infrastructure provisioning and cannot orchestrate the full CI/CD workflow. While CloudFormation can define ECS clusters and services, it does not handle building, testing, or deployment of containerized applications, nor does it integrate automated security scanning.

AWS Config monitors resource compliance but does not provide CI/CD orchestration, build, or deployment capabilities. Config is suitable for auditing infrastructure and ensuring compliance rules are met, but it cannot automate end-to-end pipeline tasks.

Amazon CloudWatch combined with Lambda could implement custom monitoring and automation scripts. However, this would require significant custom development to replicate the CI/CD pipeline, including image builds, tests, security scanning, and ECS deployment. It lacks the streamlined integration provided by CodePipeline, CodeBuild, ECR, and CodeDeploy.

The combination of CodePipeline, CodeBuild, ECR, and CodeDeploy is optimal because it automates the entire lifecycle of containerized applications, from source code to secure deployment. CodePipeline orchestrates each stage, CodeBuild builds and tests images, ECR stores and scans images, and CodeDeploy deploys containers safely to ECS. Automated scanning ensures vulnerabilities are detected before production deployment. This approach reduces human error, accelerates delivery cycles, and provides full observability with integration to CloudWatch. It supports rollbacks, scaling, and monitoring of ECS services, aligning with DevOps best practices. Using this combination, DevOps teams can achieve secure, repeatable, and automated deployment pipelines, improving reliability and operational efficiency for containerized applications in AWS.

Question 43

A DevOps engineer wants to implement a centralized metrics and log aggregation solution for a microservices architecture running across multiple AWS accounts and regions. The solution should provide visualization, alerting, and anomaly detection. Which AWS service or combination is most suitable?

A) Amazon CloudWatch Cross-Account Observability + Amazon SNS
B) AWS Config + AWS CloudTrail
C) Amazon S3 + Amazon Athena
D) AWS Systems Manager + CloudWatch Logs

Answer:  A) Amazon CloudWatch Cross-Account Observability + Amazon SNS

Explanation:

Amazon CloudWatch Cross-Account Observability allows centralized collection and visualization of metrics, logs, and traces from multiple AWS accounts and regions. This is particularly useful for microservices architectures where resources are distributed across accounts and regions. Cross-account observability consolidates telemetry data into a single account, enabling holistic monitoring of application performance, latency, error rates, and throughput. CloudWatch dashboards provide visualization, while CloudWatch Alarms allow threshold-based alerts. Integration with anomaly detection identifies unusual patterns in metrics, enabling proactive responses. Amazon SNS ensures that alerts are delivered immediately to operational teams via multiple communication channels, such as email, SMS, or HTTP endpoints.

AWS Config monitors resource configurations and enforces compliance rules. While Config provides valuable auditing for infrastructure and ensures governance policies are followed, it does not aggregate metrics or logs from multiple accounts or provide anomaly detection for operational monitoring. Config focuses on configuration compliance rather than real-time observability or alerting.

AWS CloudTrail records API calls and resource changes across AWS accounts, which is essential for auditing. While CloudTrail logs can be analyzed for trends or security events, it does not provide operational metrics, visualization, or real-time anomaly detection. Using CloudTrail alone would not meet the requirements of monitoring application performance across multiple accounts and regions.

Amazon S3 combined with Amazon Athena can store logs and perform batch analysis through SQL queries. While this is useful for historical analysis and reporting, it is not suitable for real-time monitoring, alerting, or anomaly detection. The batch-oriented nature of Athena queries limits its usefulness for operational response and proactive incident management.

AWS Systems Manager provides operational automation and configuration management but does not consolidate metrics and logs across multiple accounts or regions. It is better suited for operational tasks such as patching, inventory, or automation of operational procedures. Systems Manager alone cannot provide holistic observability across a distributed microservices architecture.

CloudWatch Cross-Account Observability combined with SNS is optimal because it provides centralized metrics, logs, and traces with visualization, alerting, and anomaly detection. Operational teams can monitor multiple accounts and regions from a single pane of glass, detect anomalies proactively, and receive real-time notifications via SNS. This approach reduces mean time to resolution (MTTR), improves operational efficiency, and ensures compliance with SLAs. By leveraging CloudWatch dashboards, teams can correlate logs and metrics from multiple services and identify dependencies or bottlenecks. Anomaly detection models automatically adjust to normal behavioral patterns, providing intelligent alerts for deviations without manual tuning. This solution simplifies operational management for complex multi-account and multi-region deployments while ensuring reliability, scalability, and proactive monitoring. It aligns with DevOps best practices for observability, alerting, and centralized operational intelligence.

Question 44

A company wants to implement continuous compliance monitoring for its AWS resources. They need to ensure that all security groups follow the principle of least privilege and that S3 buckets are encrypted by default. Which AWS service combination is most appropriate?

A) AWS Config + Amazon SNS
B) Amazon CloudWatch + Lambda
C) AWS CloudTrail + Amazon S3
D) AWS Systems Manager + CloudFormation

Answer:  A) AWS Config + Amazon SNS

Explanation:

AWS Config enables continuous assessment of AWS resources against compliance rules. DevOps teams can define custom or managed Config rules to enforce security group policies, such as restricting inbound ports or IP ranges to adhere to least privilege principles. Config also supports S3 bucket encryption rules, ensuring that all new and existing buckets comply with default encryption policies. When a resource is non-compliant, Config can trigger evaluations and automatically send notifications through Amazon SNS. SNS allows messages to be sent via email, SMS, or to ticketing and incident response systems, ensuring immediate awareness of violations and enabling prompt remediation.

Amazon CloudWatch provides monitoring of metrics and logs, and Lambda can implement automated remediation. While this combination can be customized for compliance checks, it requires more manual development to evaluate security group and S3 encryption compliance. CloudWatch does not provide prebuilt compliance rules or integrated auditing and reporting, making it less efficient for continuous compliance monitoring.

AWS CloudTrail records API activity for auditing purposes. While CloudTrail is essential for tracking changes and actions in AWS accounts, it does not actively monitor compliance against security rules or enforce encryption policies. CloudTrail is reactive rather than proactive, and additional tooling would be required to detect and respond to non-compliant resources.

AWS Systems Manager provides operational automation and configuration management. While it can execute scripts to check resource configurations or enforce policies, it does not provide centralized compliance monitoring with automated notifications and reporting. CloudFormation can define compliant infrastructure as code, but it does not continuously evaluate existing resources for compliance post-deployment.

AWS Config with SNS is the optimal solution because it continuously monitors resources, evaluates compliance against pre-defined rules, and provides real-time alerts. DevOps teams can enforce security best practices, automatically detect non-compliance, and respond promptly. Config also maintains a historical record of compliance states, providing auditability for regulatory or internal governance purposes. This combination enables centralized management of security and compliance policies across multiple accounts and regions, ensuring that S3 buckets are encrypted, security groups follow least privilege principles, and organizational standards are maintained. By integrating Config and SNS, teams achieve automated governance, operational efficiency, and risk reduction, aligning with DevOps best practices for secure, compliant AWS deployments.

Question 45

A DevOps engineer needs to deploy a highly available, low-latency API service across multiple AWS regions while minimizing operational overhead. Which service combination is best suited for this requirement?

A) Amazon API Gateway + AWS Lambda + Amazon CloudFront
B) AWS Systems Manager + EC2 Auto Scaling
C) AWS CloudFormation + AWS Config
D) Amazon S3 + Amazon CloudTrail

Answer:  A) Amazon API Gateway + AWS Lambda + Amazon CloudFront

Explanation:

Amazon API Gateway, combined with AWS Lambda, provides a fully serverless solution for building APIs. Lambda handles application logic without requiring server management, while API Gateway provides secure, scalable endpoint management. To achieve global low-latency access and high availability, Amazon CloudFront can be used as a content delivery network (CDN) to cache API responses at edge locations worldwide. This combination minimizes operational overhead, automatically scales with demand, and ensures resilient performance across multiple regions. API Gateway integrates with Lambda, supports request throttling, and provides built-in monitoring via CloudWatch. CloudFront improves response times for geographically distributed users and reduces the load on Lambda functions.

AWS Systems Manager and EC2 Auto Scaling provide operational automation and scalability for EC2-based applications. While this solution can support highly available workloads, it requires manual management of servers, patching, scaling policies, and deployments, increasing operational overhead compared to serverless architectures.

AWS CloudFormation automates resource provisioning, and AWS Config ensures compliance, but neither provides runtime execution for API requests, low-latency global access, or fully managed serverless scaling. CloudFormation and Config are infrastructure and compliance tools, not application execution services.

Amazon S3 and CloudTrail are not suitable for serving dynamic APIs. S3 stores static objects, and CloudTrail logs API activity for auditing purposes. This combination cannot provide dynamic API execution, low-latency global performance, or automated scaling for traffic demands.

The combination of API Gateway, Lambda, and CloudFront is optimal because it delivers a serverless, highly available, and low-latency API solution with minimal operational overhead. CloudFront caches responses at edge locations, ensuring fast global access, while Lambda scales automatically to handle traffic spikes. API Gateway manages API endpoints, security, throttling, and monitoring. This architecture eliminates the need for server management, reduces operational complexity, supports automated scaling, and ensures reliable, cost-effective API delivery across multiple regions. It aligns with DevOps best practices for serverless application deployment, observability, and global performance optimization.