Amazon AWS Certified DevOps Engineer — Professional DOP-C02 Exam Dumps and Practice Test Questions Set 5 Q61-75
Visit here for our full Amazon AWS Certified DevOps Engineer — Professional DOP-C02 exam dumps and practice test questions.
Question 61
A company wants to implement a CI/CD pipeline for deploying multi-container applications to Amazon ECS using Docker Compose. The pipeline should automatically build images, run tests, and deploy updates with minimal downtime. Which AWS service combination is most suitable?
A) AWS CodePipeline + AWS CodeBuild + AWS CodeDeploy + Amazon ECS
B) AWS CloudFormation + AWS Config
C) Amazon S3 + Lambda
D) AWS Systems Manager + CloudWatch
Answer: A) AWS CodePipeline + AWS CodeBuild + AWS CodeDeploy + Amazon ECS
Explanation:
AWS CodePipeline is a fully managed continuous integration and continuous delivery (CI/CD) service that orchestrates the build, test, and deployment stages of application delivery. It is designed to integrate seamlessly with source control systems such as CodeCommit, GitHub, or Bitbucket. In a multi-container application deployment using Docker Compose, CodePipeline automates the end-to-end workflow, ensuring that any code changes trigger the appropriate actions.
AWS CodeBuild provides a fully managed build environment for compiling source code, building Docker images, running unit and integration tests, and packaging artifacts. CodeBuild ensures that only verified, tested images progress to the deployment stage. When integrated with CodePipeline, it automates artifact creation and image building, reducing manual intervention and ensuring consistency in the CI/CD process.
AWS CodeDeploy orchestrates deployments to Amazon ECS, supporting rolling updates, blue/green deployments, and traffic shifting. CodeDeploy monitors container health during deployment using ECS health checks. If any issues arise, CodeDeploy can automatically roll back to the previous version, ensuring minimal downtime and maintaining application availability. This is critical for production-grade deployments of multi-container applications, where downtime or failed updates can have a significant operational impact.
AWS CloudFormation is primarily an infrastructure-as-code tool, enabling the provisioning of ECS clusters, networking components, and associated resources. While it is essential for defining infrastructure, CloudFormation does not handle the CI/CD workflow, automated testing, or deployment health monitoring. Using CloudFormation alone would require additional scripts and manual integration to implement full CI/CD capabilities.
Amazon S3 and Lambda are designed for object storage and serverless computing, respectively. While they can play supporting roles in CI/CD pipelines, they do not provide integrated orchestration for containerized deployments, build automation, or automated rollback. S3 could store artifacts, and Lambda could trigger custom actions, but the setup would be complex and less reliable than the fully managed AWS CI/CD services.
AWS Systems Manager provides operational management and automation capabilities, and CloudWatch offers monitoring and alerting. While useful for operational tasks, they do not manage containerized deployments, CI/CD orchestration, or automated rollback processes. Using this combination would require significant custom automation.
The combination of CodePipeline, CodeBuild, CodeDeploy, and ECS is optimal because it fully automates the build, test, and deployment of multi-container applications. CodePipeline ensures repeatable workflows, CodeBuild validates and packages images, ECS orchestrates container execution, and CodeDeploy manages traffic shifts and health monitoring. This integration allows DevOps teams to maintain high availability, rapid deployment frequency, and operational efficiency. Automated rollback reduces risk, ensuring that failed deployments do not impact users. Health checks provide real-time feedback, and integration with CloudWatch enables monitoring and alerting for deployment events and metrics. By using this combination, teams can implement reliable, fully managed, and scalable CI/CD pipelines that align with DevOps best practices, reduce manual overhead, and support rapid iterative development while maintaining application stability and availability.
Question 62
A company wants to implement automated backups and point-in-time recovery for its Amazon RDS PostgreSQL databases across multiple AWS accounts. Which AWS service combination is most suitable?
A) Amazon RDS Automated Backups + AWS Backup
B) AWS Config + Amazon SNS
C) Amazon S3 + Athena
D) AWS Systems Manager + CloudWatch
Answer: A) Amazon RDS Automated Backups + AWS Backup
Explanation:
Amazon RDS supports automated backups and point-in-time recovery for relational databases, including PostgreSQL. Automated backups capture snapshots of the database and transaction logs, allowing restoration to any point within the retention window. This feature ensures high availability and data durability while enabling recovery from accidental deletions or corruption. Automated backups also include system-generated snapshots for cross-AZ and cross-account recovery scenarios, improving disaster recovery readiness.
AWS Backup provides centralized backup management for multiple AWS services, including RDS, across accounts and regions. It allows the creation of backup plans with retention policies, scheduling, and lifecycle management, ensuring consistent and automated protection of database resources. AWS Backup simplifies compliance and operational overhead by providing reporting, auditing, and automated monitoring of backup activity across the organization.
AWS Config continuously evaluates resource configurations for compliance against predefined rules, but it does not manage backups or provide point-in-time recovery. Config is focused on auditing and governance rather than data protection and recovery.
Amazon SNS can notify teams when certain events occur, but it does not manage backup operations or provide recovery capabilities. It could be integrated to alert on backup failures, but additional services are required for actual backup and recovery processes.
Amazon S3 and Athena are suitable for storing and querying data, but S3 alone is not a managed backup service for relational databases. While database snapshots could be exported to S3, point-in-time recovery and automated backup scheduling would require significant custom automation.
AWS Systems Manager automates operational tasks, and CloudWatch provides monitoring, but they do not perform backup and recovery for RDS databases. Using these services alone would require additional scripting and orchestration to ensure reliable backups.
The combination of RDS Automated Backups and AWS Backup is optimal because it provides fully managed, reliable, and scalable backup and recovery solutions for relational databases. RDS automated backups ensure point-in-time recovery, high availability, and durability, while AWS Backup centralizes backup management, simplifies cross-account and cross-region operations, and provides lifecycle management and compliance reporting. Integration with CloudWatch or SNS can alert teams of backup failures or completion events, ensuring operational visibility. This approach reduces operational overhead, supports disaster recovery planning, and aligns with DevOps best practices for automated, reliable, and auditable database management. The solution enables rapid recovery from failures, protects critical data, ensures compliance, and allows teams to focus on application development rather than manual backup processes.
Question 63
A DevOps engineer wants to implement centralized logging for microservices deployed across multiple AWS accounts and regions, with real-time search and visualization capabilities. Which AWS service combination is most suitable?
A) Amazon CloudWatch Logs + CloudWatch Cross-Account Observability + Amazon OpenSearch Service
B) AWS Config + AWS CloudTrail
C) Amazon S3 + Athena
D) AWS Systems Manager + CloudWatch
Answer: A) Amazon CloudWatch Logs + CloudWatch Cross-Account Observability + Amazon OpenSearch Service
Explanation:
Amazon CloudWatch Logs allows the collection, storage, and management of logs generated by AWS services, applications, and containers. Logs can be streamed from multiple services such as Lambda, ECS, or EC2, providing a centralized repository for operational data. CloudWatch Logs supports real-time search, filtering, and metric extraction, enabling DevOps teams to analyze application behavior and performance.
CloudWatch Cross-Account Observability enables centralized collection of logs, metrics, and traces from multiple AWS accounts and regions. This is crucial for organizations operating multi-account environments, ensuring that logs from distributed microservices are aggregated into a single management account for unified monitoring and operational intelligence.
Amazon OpenSearch Service provides a scalable search and analytics engine for structured and unstructured logs. Integration with CloudWatch Logs allows logs to be streamed directly into OpenSearch, where they can be indexed and visualized using dashboards. OpenSearch Dashboards supports real-time queries, visualizations, and alerting, providing actionable insights into operational patterns, anomalies, or errors across services.
AWS Config evaluates resource configurations for compliance and governance, but does not collect, store, or visualize application logs. CloudTrail records API activity but is focused on auditing and does not provide operational logging, centralized search, or visualization.
Amazon S3 combined with Athena can store logs and perform SQL-based queries for analysis. While useful for historical log analysis, this approach is batch-oriented, not real-time, and does not provide dashboards or alerting for live operational data.
AWS Systems Manager with CloudWatch can automate operational tasks and monitor resources, but does not offer centralized log aggregation, real-time search, or advanced visualization for multi-account, multi-region microservices.
The combination of CloudWatch Logs, CloudWatch Cross-Account Observability, and OpenSearch is optimal because it provides a fully managed, scalable, and centralized logging solution with real-time search, analysis, and visualization. CloudWatch collects logs from all accounts and regions, Cross-Account Observability consolidates them for centralized monitoring, and OpenSearch provides powerful search and dashboarding capabilities. This setup allows DevOps teams to detect anomalies, troubleshoot issues quickly, and gain operational insights across complex microservices architectures. Integration with CloudWatch Alarms and SNS enables real-time notifications for critical events, further supporting proactive incident response. By leveraging this combination, organizations maintain operational visibility, improve mean time to resolution, reduce downtime, and support DevOps best practices for observability, monitoring, and incident management.
Question 64
A DevOps engineer wants to deploy an application using containers on Amazon ECS with minimal operational overhead. The service should automatically scale based on CPU and memory utilization while maintaining high availability. Which AWS service combination is most suitable?
A) Amazon ECS Fargate + Application Auto Scaling + CloudWatch
B) AWS CloudFormation + AWS Config
C) Amazon EC2 + Elastic Load Balancer
D) AWS Systems Manager + CloudTrail
Answer: A) Amazon ECS Fargate + Application Auto Scaling + CloudWatch
Explanation:
Amazon ECS Fargate is a serverless compute engine for containers that eliminates the need to provision or manage EC2 instances. DevOps teams can deploy containerized applications without worrying about the underlying infrastructure. Fargate automatically provisions the necessary compute resources to run containers, scales to meet demand, and handles patching and capacity management, significantly reducing operational overhead.
Application Auto Scaling integrates with ECS to scale tasks based on defined metrics such as CPU or memory utilization. Scaling policies allow the service to automatically increase or decrease the number of running tasks, maintaining optimal performance and resource utilization. This ensures that applications remain responsive during peak loads while minimizing costs during low traffic periods.
CloudWatch provides real-time monitoring of ECS metrics, including CPU, memory, and task counts. It also allows the creation of alarms to trigger scaling actions or notifications when thresholds are crossed. CloudWatch logs can capture container output, enabling troubleshooting and observability of the application environment.
AWS CloudFormation is primarily used for provisioning infrastructure as code. While it can define ECS clusters, services, and tasks, it does not provide automated task scaling, runtime health monitoring, or serverless execution. CloudFormation must be supplemented with additional automation scripts to achieve dynamic scaling and operational simplicity.
AWS Config evaluates resource configurations against organizational rules, which is valuable for governance and compliance, but does not provide runtime scaling, monitoring, or operational management for ECS containers.
Amazon EC,2 combined with Elastic Load Balanc,er can host ECS tasks on self-managed instances. While it allows scaling through Auto Scaling groups, it increases operational overhead, including provisioning, patching, and cluster maintenance. This approach is less efficient compared to serverless ECS Fargate, which handles these tasks automatically.
AWS Systems Manager and CloudTrail provide operational automation and auditing capabilities, respectively. While useful for operational tasks and compliance, they do not manage container deployments, scaling, or runtime availability.
The combination of ECS Fargate, Application Auto Scaling, and CloudWatch is optimal because it provides a fully managed, serverless solution for running containerized applications with automatic scaling and high availability. Fargate eliminates the need for instance management, Auto Scaling ensures optimal resource allocation based on utilization metrics, and CloudWatch provides visibility, monitoring, and alerting. This setup aligns with DevOps best practices by reducing operational overhead, improving reliability, and enabling rapid deployment. It ensures applications can handle fluctuating workloads efficiently, minimizes costs, and provides operational insights for proactive troubleshooting. By using this approach, teams achieve scalability, high availability, and simplified operations while focusing on application development rather than infrastructure management. Integration with CloudWatch alarms and metrics ensures that performance issues are detected and addressed automatically, maintaining service quality and improving operational efficiency.
Question 65
A DevOps team wants to implement automated notifications for infrastructure drift in AWS resources, including alerts when configurations deviate from defined policies. Which AWS service combination is most suitable?
A) AWS Config + Amazon SNS
B) AWS CloudTrail + Lambda
C) Amazon S3 + Athena
D) AWS CodePipeline + CodeBuild
Answer: A) AWS Config + Amazon SNS
Explanation:
AWS Config continuously monitors AWS resources and evaluates them against predefined compliance rules. Config rules can include built-in or custom policies, such as requiring encryption on S3 buckets, limiting security group access, or enforcing tagging standards. Config tracks historical configurations, enabling auditing and identification of drift when resources diverge from the expected state. This capability allows DevOps teams to detect misconfigurations or unauthorized changes proactively.
Amazon SNS integrates with Config to provide real-time notifications whenever resources become non-compliant. Notifications can be sent to email addresses, SMS, or HTTP endpoints, ensuring that relevant teams are immediately informed of configuration drift. This integration supports operational efficiency and rapid response to potential security risks or operational issues, maintaining resource integrity and compliance across environments.
AWS CloudTrail records API calls for auditing purposes. While it captures changes made to resources, it does not actively evaluate compliance against rules or provide automated notifications for configuration drift. CloudTrail is more suitable for forensic analysis rather than real-time drift detection and alerting.
Amazon S3 and Athena are suitable for storing logs and performing batch queries. This approach can detect drift retrospectively but lacks real-time evaluation and notifications, delaying response to misconfigurations. Continuous monitoring and immediate alerts are critical for proactive drift management, which S3 and Athena alone cannot provide.
AWS CodePipeline and CodeBuild are CI/CD tools that automate application build, test, and deployment workflows. While these services automate delivery, they are not designed for configuration monitoring or drift detection across AWS resources. Custom scripting would be required to emulate Config functionality, increasing operational complexity.
The combination of AWS Config and SNS is optimal because it enables continuous, automated detection of infrastructure drift and real-time alerting to the responsible teams. Config evaluates resource configurations against organizational policies, providing detailed insights into compliance status and historical drift. SNS ensures that notifications are delivered promptly, enabling immediate remediation and reducing operational risk. This solution aligns with DevOps best practices by integrating governance into the operational workflow, automating detection, and supporting proactive infrastructure management. Continuous monitoring allows organizations to maintain compliance, prevent configuration drift, reduce security vulnerabilities, and ensure consistent operational standards across multiple accounts and regions. By leveraging Config and SNS together, teams gain visibility, control, and automation in managing resource configurations, enhancing both security and operational reliability.
Question 66
A company wants to implement centralized secrets management for microservices deployed across multiple AWS accounts, with automatic rotation and secure retrieval at runtime. 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 fully managed solution for storing, managing, and automatically rotating sensitive information such as database credentials, API keys, and passwords. It allows applications to retrieve secrets programmatically at runtime, removing the need to hardcode sensitive data in code or configuration files. This reduces the risk of credential exposure and enhances security practices for microservices architectures.
Secrets Manager supports automatic rotation of secrets through integration with AWS Lambda, allowing credentials to be updated without manual intervention. This ensures that applications always have access to valid credentials while maintaining security standards. IAM policies control access to secrets, enabling fine-grained permissions and ensuring that only authorized services or users can retrieve or modify them.
AWS Systems Manager Parameter Store allows storing configuration values and secure strings, but it does not natively support automatic rotation. Custom automation is required to update secrets, increasing operational complexity and the risk of human error. Parameter Store is suitable for simpler use cases but lacks the full lifecycle management capabilities of Secrets Manager.
AWS Config evaluates resource compliance against defined rules, which is useful for auditing and governance, but does not manage secrets or provide secure runtime retrieval. Config focuses on resource monitoring and compliance rather than secret management.
Amazon S3 is a storage service that can store encrypted secrets, but it lacks automated rotation, fine-grained access control for runtime retrieval, and lifecycle management. Using S3 for secret management increases operational overhead and security risk, requiring additional custom solutions.
AWS Secrets Manager is optimal because it centralizes secret management, enables automated rotation, enforces secure runtime access, and integrates with monitoring and auditing tools. This reduces operational risk, enhances security, and aligns with DevOps best practices for secure microservices deployments. Secrets Manager ensures that credentials are rotated regularly, stored securely, and accessible only to authorized services, supporting compliance and reducing the risk of credential compromise. By providing automation, fine-grained access, and secure retrieval at runtime, Secrets Manager simplifies secrets management, improves operational efficiency, and maintains consistent security standards across multiple accounts and environments. Integration with logging and monitoring tools ensures visibility into secret access and rotation events, allowing teams to maintain a secure and auditable secrets management process.
Question 67
A DevOps engineer wants to implement automated canary deployments for a Lambda-based application, allowing gradual traffic shifting and automatic rollback in case of failures. Which AWS service combination is most suitable?
A) AWS CodeDeploy + AWS Lambda + CloudWatch
B) AWS CloudFormation + AWS Config
C) Amazon S3 + AWS Systems Manager
D) Amazon API Gateway + CloudFront
Answer: A) AWS CodeDeploy + AWS Lambda + CloudWatch
Explanation:
AWS CodeDeploy supports automated deployment strategies, including canary, linear, and all-at-once deployments for AWS Lambda. In a canary deployment, a small portion of traffic is initially directed to the new Lambda version, while the majority continues to use the previous version. This allows the application to be tested in production with minimal risk. CodeDeploy monitors the health of the new version during the deployment window. If issues such as increased errors, latency, or failed invocations are detected, CodeDeploy can automatically roll back to the previous stable version, maintaining application availability and reducing operational risk.
AWS Lambda executes the application code without requiring server provisioning or management. When combined with CodeDeploy, Lambda supports versioning and aliases, which are critical for traffic shifting during canary deployments. Lambda automatically scales to handle varying traffic, and integration with CodeDeploy ensures that versioned deployments are performed safely, reliably, and efficiently.
CloudWatch provides metrics and alarms for Lambda invocations, errors, duration, and throttles. These metrics are crucial for monitoring the health of the canary deployment. CloudWatch can trigger alarms to initiate automated rollback if specified thresholds are breached. Real-time logging in CloudWatch Logs also enables troubleshooting of any issues detected during the deployment, providing actionable insights for operational teams.
AWS CloudFormation allows provisioning of Lambda functions, aliases, and associated resources, but it does not orchestrate traffic-shifting deployments or monitor runtime performance. CloudFormation is an infrastructure-as-code tool, suitable for defining resources but not for dynamic, gradual deployment strategies.
Amazon S3 combined with Systems Manager can store artifacts and automate operational tasks, but it does not manage Lambda deployments, implement canary strategies, or monitor traffic and application health. This setup lacks automated rollback and real-time monitoring capabilities.
Amazon API Gateway with CloudFront can provide API endpoints and global caching, but it does not manage Lambda function deployment strategies or perform traffic shifting. It is more suitable for serving APIs rather than orchestrating application releases.
The combination of CodeDeploy, Lambda, and CloudWatch is optimal because it provides automated, reliable, and safe deployment of serverless applications. Canary deployments reduce operational risk, allow gradual testing in production, and ensure that failures trigger automatic rollback. Lambda provides scalable execution without infrastructure management, CodeDeploy manages deployment strategies and traffic shifting, and CloudWatch monitors performance metrics and triggers rollback actions. This setup aligns with DevOps best practices by automating deployments, reducing human error, and improving application reliability and uptime. By integrating automated monitoring, logging, and rollback, teams can maintain operational confidence while releasing frequent updates to production environments. Canary deployments also allow teams to validate new features with real user traffic, enabling safer incremental releases, faster recovery from failures, and continuous improvement without impacting the majority of users.
Question 68
A company wants to implement real-time cost anomaly detection and alerting for AWS resources to identify unexpected spikes in usage and spending. Which AWS service combination is most suitable?
A) AWS Cost Anomaly Detection + Amazon SNS
B) AWS Config + CloudWatch
C) AWS CloudTrail + Lambda
D) Amazon S3 + Athena
Answer: A) AWS Cost Anomaly Detection + Amazon SNS
Explanation:
AWS Cost Anomaly Detection leverages machine learning to analyze historical spending patterns and establish baselines for expected usage costs. It continuously monitors AWS usage and detects deviations from expected behavior. This allows organizations to identify unexpected spikes in resource consumption or costs before they become significant issues. Cost Anomaly Detection can target individual services, linked accounts, or organizational units, making it scalable for multi-account environments.
Amazon SNS integrates with Cost Anomaly Detection to provide real-time notifications when anomalies are detected. Notifications can be delivered via email, SMS, or HTTP endpoints, ensuring that finance teams, DevOps engineers, and management are immediately aware of unusual spending patterns. This allows rapid investigation and remediation of misconfigured resources, runaway costs, or operational inefficiencies.
AWS Config is valuable for auditing resource compliance and detecting configuration drift, but it does not provide real-time cost monitoring or anomaly detection. Config is focused on governance and compliance rather than financial operations.
CloudWatch provides operational monitoring and alerting based on metrics such as CPU utilization, memory, or network throughput. While CloudWatch could track usage metrics indirectly related to costs, it does not natively analyze cost trends or detect anomalies in AWS billing data. Custom solutions would be required, increasing complexity.
AWS CloudTrail logs API activity for auditing and compliance purposes. While it records resource usage, it does not provide automated anomaly detection for costs. CloudTrail is retrospective and does not proactively notify teams of unexpected spending.
Amazon S3 and Athena allow storage and querying of historical billing or usage data. While this approach can be used for post-hoc analysis, it is batch-oriented and does not support real-time alerts or automated anomaly detection. Queries must be executed manually or scheduled, delaying detection and response.
The combination of AWS Cost Anomaly Detection and SNS is optimal because it enables automated, real-time detection of unusual cost patterns and immediate notifications to relevant stakeholders. Cost Anomaly Detection uses machine learning to identify deviations dynamically, reducing false positives compared to static thresholds. Integration with SNS ensures rapid response, allowing teams to investigate root causes, stop unnecessary resources, or optimize configurations proactively. This approach supports DevOps best practices for financial monitoring and operational efficiency, providing actionable insights into resource utilization, cost trends, and potential misconfigurations. It also enhances accountability, governance, and proactive budget management by automating alerts and facilitating faster decision-making in complex multi-account AWS environments. By using these services together, organizations can prevent unexpected financial surprises, improve visibility into cloud spending, and maintain cost optimization strategies while maintaining operational agility.
Question 69
A DevOps engineer needs to implement centralized logging for a multi-account, multi-region AWS environment, enabling search, visualization, and alerting for application errors and performance issues. Which AWS service combination is most suitable?
A) Amazon CloudWatch Logs + CloudWatch Cross-Account Observability + Amazon OpenSearch Service
B) AWS Config + AWS CloudTrail
C) Amazon S3 + Athena
D) AWS Systems Manager + CloudWatch
Answer: A) Amazon CloudWatch Logs + CloudWatch Cross-Account Observability + Amazon OpenSearch Service
Explanation:
Amazon CloudWatch Logs allows the collection and storage of logs from AWS services, applications, and containers. It provides filtering, metric extraction, and query capabilities to analyze logs in real-time. Logs can be collected from Lambda, ECS, EC2, and other services, providing a centralized location for operational data.
CloudWatch Cross-Account Observability enables aggregation of metrics, logs, and traces from multiple AWS accounts and regions into a central management account. This is essential for organizations managing complex multi-account environments, as it ensures operational visibility and unified monitoring of distributed applications. By consolidating logs, teams can detect patterns, troubleshoot issues, and perform analytics across the entire infrastructure.
Amazon OpenSearch Service provides search and analytics capabilities for structured and unstructured log data. Integration with CloudWatch Logs allows logs to be streamed into OpenSearch, where they can be indexed and visualized using OpenSearch Dashboards. This provides rich insights into application behavior, performance, and errors. Dashboards can be configured for real-time monitoring, anomaly detection, and trend analysis, enabling teams to respond proactively to operational issues.
AWS Config monitors configuration compliance but does not provide operational logging, search, or visualization. Config is focused on auditing and governance rather than performance monitoring or error analysis.
AWS CloudTrail captures API activity for auditing purposes. While valuable for security and operational auditing, CloudTrail does not provide real-time log analytics, centralized visualization, or alerting for application performance issues.
Amazon S3 and Athena allow storing and querying logs for historical analysis. However, this approach is batch-oriented, lacks real-time visibility, and does not provide dashboards or alerting capabilities. It is useful for post-mortem analysis but not for proactive monitoring.
AWS Systems Manager provides operational automation and management tools. While useful for maintenance tasks, it does not provide centralized logging, real-time search, or visualization for multi-account, multi-region applications. CloudWatch alone, without Cross-Account Observabilit,y lacks aggregation capabilities across multiple accounts.
The combination of CloudWatch Logs, Cross-Account Observability, and OpenSearch is optimal because it provides centralized log collection, real-time monitoring, advanced search, visualization, and alerting capabilities across multi-account, multi-region environments. Logs are consolidated for operational visibility, OpenSearch enables advanced analytics and dashboards, and Cross-Account Observability ensures teams have a single pane of glass to monitor application performance and detect anomalies. Integration with CloudWatch Alarms and SNS enables proactive alerting for application errors or performance degradation. This setup aligns with DevOps best practices by providing observability, operational insights, and actionable intelligence, reducing mean time to resolution and improving system reliability and stability. Centralized logging ensures faster troubleshooting, improved operational efficiency, and better governance across complex, distributed infrastructures.
Question 70
A DevOps engineer needs to implement a global, highly available API for a serverless application that automatically scales and provides caching at edge locations to reduce latency. Which AWS service combination is most suitable?
A) Amazon API Gateway + AWS Lambda + Amazon CloudFront + AWS WAF
B) AWS Systems Manager + EC2 Auto Scaling
C) AWS CloudFormation + AWS Config
D) Amazon S3 + CloudTrail
Answer: A) Amazon API Gateway + AWS Lambda + Amazon CloudFront + AWS WAF
Explanation:
Amazon API Gateway enables the creation, management, and secure deployment of RESTful APIs or WebSocket APIs. It handles request throttling, authentication, and authorization, and integrates with AWS Lambda for serverless backend logic. API Gateway automatically scales to handle varying traffic loads, ensuring high availability without manual intervention.
AWS Lambda executes the backend application logic in a fully serverless environment, automatically scaling with incoming request volume. Lambda eliminates the need to manage servers, simplifies deployment, and allows developers to focus on application code rather than infrastructure. Versioning and aliases support safe deployments, blue/green releases, and canary testing for API updates.
Amazon CloudFront provides caching and content delivery at global edge locations, significantly reducing latency for end users worldwide. CloudFront caches responses from API Gateway and Lambda, improving performance and reducing backend load. Edge caching also enhances user experience for distributed applications by minimizing round-trip times to the origin.
AWS WAF (Web Application Firewall) protects APIs against common web exploits and DDoS attacks. WAF allows configuration of rules to filter malicious traffic, block attacks, and ensure secure API access. When combined with CloudFront, WAF provides global protection and integrates seamlessly with API Gateway endpoints.
AWS Systems Manager and EC2 Auto Scaling are suitable for managing EC2-based applications, automating operational tasks, and scaling instances. However, they require server management, patching, and manual orchestration, increasing operational overhead. This approach does not provide serverless execution, global edge caching, or integrated DDoS protection.
AWS CloudFormation, combined with AWS Config, automates infrastructure provisioning and configuration compliance but does not execute serverless application logic, provide global scaling, or manage edge caching and security for APIs. These services are better suited for infrastructure management and governance rather than runtime operations.
Amazon S3 with CloudTrail can store static content and log API activity for auditing, but S3 is not designed to serve dynamic API requests. CloudTrail provides auditing but does not support scaling, caching, or security for API workloads.
The combination of API Gateway, Lambda, CloudFront, and WAF is optimal because it provides a highly available, global serverless API architecture with automatic scaling, edge caching, and security protections. Lambda handles execution without server management, API Gateway manages endpoints and throttling, CloudFront reduces latency and improves performance, and WAF protects against malicious traffic. Integration with CloudWatch allows monitoring, logging, and alerting for operational visibility. This setup aligns with DevOps best practices by enabling rapid deployments, secure and scalable execution, and global performance optimization. By combining serverless execution with edge caching and integrated security, teams can achieve low-latency, high-reliability API delivery while minimizing operational complexity, enhancing security posture, and providing an exceptional user experience.
Question 71
A DevOps engineer needs to implement automated vulnerability scanning of container images before deployment to Amazon ECS. Which AWS service is most suitable?
A) Amazon ECR
B) Amazon S3
C) AWS CodeCommit
D) AWS CloudTrail
Answer: A) Amazon ECR
Explanation:
Amazon ECR (Elastic Container Registry) is a fully managed container image registry that integrates with AWS CI/CD services. ECR provides automated vulnerability scanning of container images using Amazon Inspector. When a new image is pushed, ECR scans it for known vulnerabilities (CVEs) and generates reports. These reports can prevent the deployment of insecure images to ECS, ensuring that only verified, secure images are promoted to production.
ECR provides versioning and lifecycle policies to manage image retention, automatically removing older images to optimize storage costs. It integrates with AWS IAM for fine-grained access control, ensuring only authorized users or services can push or pull images. Additionally, ECR integrates with CodePipeline and CodeBuild, enabling automated scanning during CI/CD pipelines and preventing insecure deployments.
Amazon S3 is suitable for storing artifacts, but does not natively scan container images for vulnerabilities. Using S3 would require custom scripts and automation, increasing complexity and operational risk.
AWS CodeCommit stores source code but does not manage container images or perform vulnerability scanning. While it integrates with CI/CD pipelines, additional tools would be required to ensure image security.
AWS CloudTrail records API activity for auditing purposes. While it tracks changes to container repositories, it does not scan images or prevent insecure deployments. CloudTrail is more suitable for compliance and forensic analysis rather than proactive security scanning.
Amazon ECR is the optimal solution because it provides fully managed container image storage, integrated vulnerability scanning, fine-grained access control, and seamless CI/CD integration. Automated scanning ensures DevOps teams detect vulnerabilities early in the software lifecycle, preventing security risks and maintaining compliance. Lifecycle policies reduce storage management overhead, and integration with CloudWatch enables monitoring of scan results. This approach aligns with DevSecOps best practices, embedding security into CI/CD workflows, ensuring only secure images are deployed, and reducing operational complexity. Teams gain automated detection, reporting, and remediation capabilities, improving security posture while maintaining operational efficiency and deployment agility.
Question 72
A DevOps team wants to automatically remediate non-compliant S3 buckets that are publicly accessible or lack encryption. Which AWS service combination is most suitable?
A) AWS Config + AWS Systems Manager
B) Amazon CloudWatch + Lambda
C) AWS CloudTrail + S3
D) AWS CodePipeline + CodeBuild
Answer: A) AWS Config + AWS Systems Manager
Explanation:
AWS Config continuously evaluates AWS resources against defined compliance rules. Managed or custom rules can detect S3 buckets that are publicly accessible or not encrypted. Config provides a historical record of resource compliance, enabling auditing and identification of violations. This ensures visibility into the security posture of S3 buckets across accounts and regions.
AWS Systems Manager integrates with Config to provide automated remediation using runbooks (Automation Documents). When Config identifies a non-compliant bucket, Systems Manager can trigger a remediation action such as enabling server-side encryption or modifying bucket policies to restrict public access. This approach automates compliance enforcement, reduces operational overhead, and ensures consistency across environments.
Amazon CloudWatch and Lambda can be used for custom monitoring and remediation, but this requires significant development effort. CloudWatch detects metric changes but does not natively identify S3 compliance violations. Lambda would need custom scripts to enforce security standards, increasing complexity and maintenance overhead.
AWS CloudTrail logs API activity, providing audit trails for changes to S3 buckets. While CloudTrail helps in identifying misconfigurations retrospectively, it does not proactively detect or remediate non-compliance.
AWS CodePipeline and CodeBuild are CI/CD tools that orchestrate application deployment. They do not monitor resource compliance or provide automated remediation for non-compliant S3 buckets. Using these services for compliance enforcement would require custom integration, adding complexity and risk.
The combination of AWS Config and Systems Manager is optimal because it provides continuous compliance monitoring, automated remediation, and centralized auditing. Config evaluates bucket configurations against organizational policies and identifies violations, while Systems Manager enforces remediation actions automatically. This approach reduces human error, ensures consistent enforcement of security standards, and maintains compliance across multiple accounts and regions. Integration with CloudWatch and SNS allows alerting for non-compliant resources, supporting proactive operational management. By automating detection and remediation, organizations maintain a secure and compliant environment, improve operational efficiency, and adhere to DevOps and DevSecOps best practices, minimizing risk while reducing manual intervention and operational burden.
Question 73
A company wants to centrally manage secrets for microservices across multiple AWS accounts, with automatic rotation and runtime access. 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 is a fully managed service for storing and managing secrets such as database credentials, API keys, and other sensitive information. Secrets can be retrieved programmatically at runtime by authorized applications, eliminating hardcoding of credentials in source code. Secrets Manager supports automatic rotation using AWS Lambda, ensuring that secrets are regularly updated without manual intervention.
IAM policies provide fine-grained access control, restricting secret access to specific applications or services. Integration with monitoring and audit logging allows teams to track usage and access, supporting compliance and security requirements.
AWS Systems Manager Parameter Store provides secure storage for configuration values and sensitive strings, but lacks automatic rotation. Custom automation is needed for rotation, increasing operational complexity and risk.
AWS Config is used for compliance monitoring of AWS resources and does not manage secrets or provide runtime access.
Amazon S3 can store encrypted secrets but lacks native rotation, fine-grained access control, or runtime retrieval mechanisms. Using S3 for secrets increases operational risk and requires custom automation.
Secrets Manager is optimal because it centralizes secret management, provides automatic rotation, ensures secure access at runtime, and integrates with auditing and monitoring tools. This reduces operational risk, simplifies DevOps processes, and maintains compliance across multiple accounts and services. By automating the lifecycle of secrets, teams ensure secure, reliable, and consistent access to sensitive information while adhering to DevSecOps best practices.
Question 74
A DevOps engineer wants to implement continuous monitoring of application performance across multiple AWS accounts and regions, with anomaly detection and real-time notifications. Which AWS service combination is most suitable?
A) CloudWatch Cross-Account Observability + Amazon SNS
B) AWS Config + CloudTrail
C) Amazon S3 + Athena
D) AWS Systems Manager + CloudWatch
Answer: A) CloudWatch Cross-Account Observability + Amazon SNS
Explanation:
CloudWatch Cross-Account Observability centralizes operational monitoring of metrics, logs, and traces from multiple AWS accounts and regions. This provides a single pane of glass for analyzing application performance, detecting anomalies, and troubleshooting issues across distributed systems. It supports anomaly detection, using machine learning to establish baselines for metrics such as latency, error rates, and throughput. Deviations trigger alerts, enabling rapid response.
Amazon SNS enables automated notifications for anomalies detected in CloudWatch. Notifications can be delivered via email, SMS, or HTTP endpoints, ensuring operational teams are aware of performance deviations in real time.
AWS Config monitors resource configurations and enforces compliance, but does not provide operational performance monitoring or anomaly detection. It is focused on governance rather than application observability.
CloudTrail provides audit logging of API calls but does not analyze metrics or detect performance anomalies.
Amazon S3 with Athena allows historical log analysis but lacks real-time monitoring and automated alerting, limiting its effectiveness for operational incident response.
AWS Systems Manager supports operational automatio,n and CloudWatch provides monitoring for individual resources, but without Cross-Account Observability, it cannot consolidate metrics across accounts and regions for anomaly detection.
The combination of CloudWatch Cross-Account Observability and SNS is optimal for centralized, real-time monitoring and automated alerting. Anomaly detection identifies deviations from normal behavior, and SNS ensures immediate notification for remediation. This approach aligns with DevOps best practices, improving operational visibility, reducing mean time to resolution, and supporting proactive management of distributed applications.
Question 75
A company wants to implement automated blue/green deployments for containerized applications on ECS, with traffic shifting and automatic rollback in case of errors. Which AWS service combination is most suitable?
A) CodeDeploy + ECS + CodePipeline
B) CloudFormation + Config
C) EC2 Auto Scaling + CloudWatch
D) S3 + Lambda
Answer: A) CodeDeploy + ECS + CodePipeline
Explanation:
AWS CodeDeploy supports blue/green deployments for ECS, allowing the creation of separate environments for the existing (blue) and new (green) versions. Traffic can be shifted gradually to the green environment while monitoring health metrics. If errors occur, CodeDeploy can automatically roll back to the blue environment, maintaining availability.
ECS orchestrates container tasks and services, providing scalability, service discovery, and task management. CodePipeline integrates the CI/CD workflow, automating builds, tests, and deployments. CodeBuild ensures that container images are verified and tested before promotion.
CloudFormation is infrastructure-as-co,de but does not manage traffic shifting or automated deployment strategies. Config monitors compliance but does not deploy applications.
EC2 Auto Scaling and CloudWatch manage compute resources and monitoring, but do not orchestrate blue/green deployments or container versioning.
S3 and Lambda cannot manage containerized deployments or traffic shifting.
The combination of CodeDeploy, ECS, and CodePipeline is optimal because it automates the CI/CD pipeline, manages containerized blue/green deployments, monitors health, and performs automatic rollback, ensuring minimal downtime and operational reliability.