Amazon AWS Certified DevOps Engineer — Professional DOP-C02 Exam Dumps and Practice Test Questions Set 6 Q76-90
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Question 76
A DevOps engineer must design an automated, zero-downtime patching process for Amazon EC2 instances running in an Auto Scaling group behind an Application Load Balancer. The solution must ensure that patched instances are fully verified before receiving production traffic. Which approach best meets these requirements?
A) Use EC2 Auto Scaling instance refresh with lifecycle hooks and AWS Systems Manager Automation
B) Use AWS Config rules with CloudFormation drift detection
C) Use AWS Lambda to manually terminate and replace EC2 instances one by one
D) Use Amazon Inspector to patch EC2 instances during peak traffic windows
Answer: A) Use EC2 Auto Scaling instance refresh with lifecycle hooks and AWS Systems Manager Automation
Explanation:
EC2 Auto Scaling instance refresh provides a structured and automated way to update all instances in an Auto Scaling group without incurring downtime. When an update is initiated, Auto Scaling gradually replaces existing instances with newly launched ones that contain the required patches or configurations. This ensures continuous availability while maintaining instance compliance. Lifecycle hooks give the engineer the ability to control what happens during each step of the replacement process. When a new instance is launched, it can be placed in a pending wait state, allowing validation, configuration, or baseline checks to occur before the instance enters service. This ensures that no unverified or improperly configured instance receives production traffic. AWS Systems Manager Automation further enhances the process by providing standardized patching, compliance scanning, application installation, or configuration routines that run at instance launch time. This combination creates a robust and repeatable patching workflow that prevents problematic updates from impacting live workloads.
AWS Config rules with CloudFormation drift detection focus on configuration compliance and infrastructure status, but do not provide a mechanism for replacing or updating instances seamlessly. These tools are powerful for identifying inconsistencies between deployed resources and expected configurations, but they do not address automated patching, zero-downtime refreshes, lifecycle control, or integration with the load balancer. They operate more as monitoring and compliance services than active deployment or orchestration solutions for EC2 instances. Therefore, while helpful in identifying configuration drift, they do not fulfill the requirement of safely validating patched instances before receiving traffic.
Using AWS Lambda to manually terminate and replace EC2 instances introduces risk, as it requires custom logic for termination sequencing, capacity maintenance, health checks, load balancer deregistration, and validation. Implementing these tasks manually increases operational burden and the possibility of failure during deployments. This approach lacks the built-in safeguards, rollback mechanisms, and orchestration features available in Auto Scaling instance refresh. Additionally, manual scripting for lifecycle processes can become brittle and difficult to maintain over time, especially as the environment scales or deployment patterns change.
Amazon Inspector provides automated vulnerability assessments and identifies required patches, but does not perform patching itself. An inspector cannot orchestrate zero-downtime patching or instance replacement and is not designed to control traffic flow to EC2 instances. It is primarily a scanning and compliance tool, not a deployment or refresh mechanism. Running scans during peak traffic also contradicts best practices, as it may add overhead or delay remediation efforts. The inspector’s role is to pinpoint vulnerabilities rather than execute patching or coordinate the launch and validation of replacement instances in a controlled fashion.
The combination of instance refresh, lifecycle hooks, and Systems Manager Automation is ideal because it unifies automated patch deployment with controlled instance replacement. Instance refresh handles the rolling update process, ensuring capacity is maintained, and new instances are introduced gradually. Lifecycle hooks allow verification tasks to run before an instance becomes active, preventing misconfigured or unpatched machines from receiving traffic. Systems Manager Automation provides a programmable and repeatable mechanism to apply patches, run compliance checks, or execute application setup steps. Together, these technologies create a complete solution that ensures patched instances are validated, compliant, correctly configured, and ready before joining production. This reduces risk, maintains availability, and adheres to DevOps principles of automation, repeatability, and minimal human intervention.
These capabilities collectively ensure a safe, efficient, and scalable patching process for EC2 infrastructure operating behind an Application Load Balancer. The approach eliminates downtime, maintains operational consistency, and ensures adherence to organizational security and compliance standards. By integrating native AWS automation tools, the engineer achieves a predictable, maintainable deployment workflow that addresses all the functional and operational requirements of zero-downtime patching. This makes it the most complete and effective solution.
Question 77
A company requires a centralized mechanism to automatically detect, log, and block suspicious API activity across multiple AWS accounts. The solution must provide near real-time visibility, integrate with automated remediation, and support organization-wide governance. Which service combination is most appropriate?
A) Amazon GuardDuty + AWS Organizations + AWS Security Hub
B) AWS CloudTrail + S3
C) AWS Lambda scheduled scans
D) Amazon S3 server access logging
Answer: A) Amazon GuardDuty + AWS Organizations + AWS Security Hub
Explanation:
Amazon GuardDuty is designed to provide intelligent threat detection and continuous monitoring across accounts. It identifies suspicious API calls, unusual access patterns, and potential malicious activity by analyzing CloudTrail logs, VPC Flow Logs, and DNS logs. When integrated with AWS Organizations, GuardDuty can be enabled and managed centrally for all member accounts. This creates a unified security posture across an entire organization. It ensures that new accounts are automatically enrolled, providing consistent governance. GuardDuty also delivers findings in near real time, allowing immediate visibility of suspicious activity. These findings integrate naturally with automated remediation workflows using services such as Lambda, Systems Manager, EventBridge, or Step Functions. This centralized threat detection capability ensures that the entire organization benefits from continuous monitoring without the need for manual configuration in individual accounts.
AWS Security Hub plays a complementary role by aggregating and correlating findings from GuardDuty and other integrated tools. It provides security insights, prioritization, and standard compliance checks such as CIS Benchmarks and PCI DSS. Security Hub assists teams in maintaining a unified security dashboard across all accounts. By centralizing findings, teams can quickly identify trends and determine the best remediation actions. It becomes the authoritative source for multi-account visibility and compliance posture monitoring. When combined with Amazon GuardDuty in an AWS Organizations structure, Security Hub scales easily and maintains organization-wide consistency. Its ability to integrate with automated response mechanisms further enhances its usefulness in a DevOps or DevSecOps workflow by triggering remediation actions when certain findings are detected.
AWS CloudTrail with S3 is useful for logging account activity, but does not offer intelligent threat detection, automated anomaly analysis, or real-time analysis of suspicious API calls. CloudTrail records what happened, but it does not provide insight into whether those actions represent suspicious behavior. Using CloudTrail alone requires custom logic to detect anomalies, build dashboards, or correlate events across accounts. This adds operational overhead and complexity. While CloudTrail is excellent for auditing, forensic investigations, and long-term storage of logs, it cannot meet the requirements for automatic detection, governance, blocking malicious activity, or organization-wide management in near real time.
AWS Lambda scheduled scans cannot provide real-time threat detection. Lambda can run periodic assessments or analyze logs, but only at intervals such as every five or fifteen minutes. This delay reduces the effectiveness of the immediate threat response. It also requires custom code to parse logs, identify suspicious behavior, and issue remediation commands. Maintaining such custom scripts becomes increasingly difficult as an environment scales across multiple accounts. Lambda scheduled scans lack native integration with automated threat intelligence or continuous monitoring mechanisms required for centralized security governance.
Amazon S3 server access logging records requests made to S3 buckets, but it does not analyze API calls across services. It cannot monitor suspicious IAM activity, unusual EC2 actions, or malicious API usage. This service is narrow in scope and limited to S3. It cannot block or remediate threats, integrate centrally across accounts, or provide real-time insights. It cannot satisfy requirements for automated detection, centralized governance, or broad API activity monitoring.
The combination of Amazon GuardDuty, AWS Organizations, and AWS Security Hub is therefore the most suitable solution because it brings continuous, automated threat detection into a centralized governance structure. GuardDuty continuously monitors API activity and network behavior using built-in machine learning and threat intelligence. AWS Organizations ensures that all member accounts are automatically protected and centrally managed. AWS Security Hub provides aggregated findings, compliance validation, and an organization-wide security dashboard. This integrated solution delivers near real-time detection, centralized visibility, automated remediation, and consistent governance. It supports DevOps and DevSecOps principles by enabling automated responses to detected threats while maintaining compliance and visibility across all AWS accounts.
Question 78
A team needs a fully automated build-and-test pipeline for a monolithic application, with dependency caching, parallel testing, and environment-specific artifact promotion. Which AWS service best meets the requirement?
A) AWS CodeBuild with CodePipeline
B) AWS Batch
C) Amazon EC2
D) AWS CloudFormation
Answer: A) AWS CodeBuild with CodePipeline
Explanation:
AWS CodeBuild provides a fully managed build service capable of compiling source code, running tests, and packaging artifacts. It supports dependency caching, which significantly improves build times for large monolithic applications. By caching dependencies such as Maven packages, Node modules, or Python libraries, CodeBuild ensures that only new or changed components need to be downloaded during each build. This reduces costs and improves pipeline performance. CodeBuild also supports parallel test execution by allowing multiple build containers to run concurrently or by configuring batch builds. These capabilities optimize the testing process for complex applications that require substantial test coverage.
AWS CodePipeline orchestrates the full CI/CD process, moving artifacts through sequential stages such as source, build, test, staging, and production. It supports environment-specific artifact promotion by triggering subsequent stages only when a previous stage is successful. This ensures that only verified and fully tested builds move to production. CodePipeline integrates seamlessly with CodeBuild, enabling a complete automated workflow. It also supports manual approvals when required, providing flexibility for organizations that need additional oversight for production deployments.
AWS Batch is designed for large-scale batch processing and computational workloads, but is not optimized for CI/CD automation. While it is powerful for running jobs in parallel, it does not provide native features for build orchestration, dependency caching, or artifact promotion. Batch also requires additional setup for container environments and job definitions, which adds complexity and does not provide the same streamlined CI/CD functionality as CodeBuild and CodePipeline.
Amazon EC2 provides compute resources but lacks managed CI/CD capabilities. Setting up a build system on EC2 requires manual configuration of build tools, storage, scripts, and scheduling. This approach does not offer automatic scaling, caching, or orchestration. Managing build environments on EC2 is error-prone and increases operational cost and maintenance burdens.
AWS CloudFormation is used for provisioning and managing infrastructure as code. It does not handle application builds, tests, dependency caching, or artifact promotion. While it is essential for deploying infrastructure, it does not provide the CI/CD capabilities required for building and testing applications.
The combination of CodeBuild and CodePipeline is, therefore, the most effective solution. It provides fully automated builds, support for dependency caching, parallel testing capabilities, and structured artifact promotion across different deployment environments. This combination aligns with best DevOps practices by creating repeatable, scalable, and reliable CI/CD pipelines that require minimal operational overhead.
Question 79
A DevOps engineer needs to deploy containerized workloads on AWS with automated image updates, Canary deployments, and built-in monitoring without managing servers. Which service combination best meets these needs?
A) AWS App Runner
B) Amazon EC2
C) Amazon RDS
D) AWS Batch
Answer: A) AWS App Runner
Explanation:
AWS App Runner is a fully managed container application service designed for rapid deployment without handling servers, clusters, or complex orchestration tools. It provides a streamlined mechanism for deploying and scaling applications directly from source code or container images stored in Amazon ECR. One key capability is automatic image-based deployments. When teams push a new container image to ECR, App Runner can automatically detect the update and trigger a new deployment. This seamlessly supports continuous delivery workflows where new versions of applications are continuously integrated and deployed with minimal manual effort. Furthermore, App Runner manages traffic shifting automatically. During deployments, it uses a gradual rollout mechanism that resembles a Canary deployment, letting a subset of traffic flow to the new version while monitoring health and performance. If anomalies occur, App Runner can roll back automatically, thereby reducing risk and improving deployment reliability. App Runner also includes built-in monitoring through CloudWatch metrics, logs, and request tracking, providing operational insight without requiring custom instrumentation. Its managed autoscaling ensures that the application adjusts capacity based on demand without manual configuration. The service abstracts all underlying compute resources, networking, scaling policies, and deployment strategies, providing a fully serverless operational model that best satisfies the requirements.
Amazon EC2 provides raw compute capacity but does not inherently manage containers or deployment workflows. Using EC2 for container workloads requires setting up Docker, ECS, or Kubernetes manually. It becomes necessary to patch operating systems, configure autoscaling, manage EBS volumes, and handle networking. Canary deployments would require additional components such as Application Load Balancers, weighted target groups, and custom scripts or pipeline logic. EC2 does not offer automated image updates. Monitoring is possible through CloudWatch, but configuration is manual. Overall, EC2 requires substantial ongoing operational effort and does not fit the requirement of avoiding server management.
Amazon RDS is a relational database service and not designed to run application containers. It cannot host web applications, perform deployments, or manage traffic. Although RDS provides automatic updates and scaling within the database domain, it does not support container execution, image updates, or deployment strategies. It has no role in Canary deployments or container orchestration. Therefore, RDS cannot meet any of the requirements and is unsuitable for containerized workloads.
AWS Batch focuses on running batch or compute-intensive jobs in a managed environment. Although it supports containerized workloads, its design goals differ significantly from those of an always-on application service. Batch workloads operate as jobs with a clear start and end, making them unsuitable for continuously running web services or APIs. Batch does not provide traffic management, Canary release mechanisms, automatic image-based updates, or integrated monitoring for long-running services. Instead, Batch is optimized for distributed computing tasks such as data processing, simulations, or rendering. It also requires job queues and definitions, which add configuration overhead. Therefore, AWS Batch does not align with the deployment patterns required for containerized web applications.
AWS App Runner stands out as the optimal solution because it incorporates automated deployments, Canary-style gradual traffic shifting, integrated monitoring, and serverless operation. Its automatic image update mechanism removes the operational burden of orchestrating deployments and configuring systems for continuous delivery. App Runner also ensures consistent, repeatable deployments by standardizing runtime environments and consolidating configuration into service settings. Built-in health checks ensure that the new version is validated before traffic gradually shifts, and automatic rollback protects application availability. The service integrates directly with CloudWatch for monitoring metrics such as latency, request count, and error rate. With scaling policies built in, App Runner eliminates the need to configure autoscaling groups or load balancers. It supports secure connections, environment variables, and networking options without exposing complexity. Everything from deployment to scaling happens automatically, aligning perfectly with DevOps principles of automation, speed, and operational efficiency.
Because the question specifically emphasizes automated updates, Canary deployments, built-in monitoring, and no server management, App Runner provides the only complete solution without requiring additional tools or manual orchestration. For these reasons, AWS App Runner is the correct answer.
Question 80
A company needs to enforce that all AWS Lambda functions across multiple accounts use the latest version of a specific runtime. The compliance check must be continuous, centrally managed, and capable of triggering automated remediation to update noncompliant functions. Which AWS service combination best meets these requirements?
A) AWS Config + AWS Systems Manager Automation
B) Amazon GuardDuty + EventBridge
C) AWS CloudTrail + S3
D) AWS Step Functions + SQS
Answer: A) AWS Config + AWS Systems Manager Automation
Explanation:
AWS Config continuously evaluates resource configurations across all accounts and regions in an organization. It is capable of checking AWS Lambda runtime versions and validating compliance against organizational requirements. Config rules allow the company to define which runtime versions are permitted or required. Using AWS Organizations, Config can be enabled centrally so that all accounts automatically inherit governance policies. When a Lambda function violates the required configuration, AWS Config generates a noncompliant finding. The service also maintains historical configuration data, which helps auditors understand how and when the function became noncompliant. It provides real-time or near-real-time compliance detection, essential for environments where runtime versions may introduce security or compatibility risks if outdated.
AWS Systems Manager Automation integrates directly with Config to remediate noncompliant resources. When Config flags a Lambda function using an outdated runtime, Systems Manager Automation can trigger a remediation runbook that updates the function to the required runtime. The automation workflow can modify configurations, deploy updated code, or set new runtime parameters. Using prebuilt or custom automation documents ensures that the remediation is repeatable, controlled, and auditable. This combination of detection and automated correction delivers a full compliance lifecycle, ensuring a consistent enforcement model across multiple accounts. Systems Manager Automation also supports cross-account execution when configured with appropriate permissions, enabling centralized operational control.
Amazon GuardDuty is a threat detection service and does not provide configuration compliance checks for Lambda runtimes. GuardDuty focuses on detecting suspicious behavior, malware activity, or potential compromise scenarios. It does not monitor function configurations or trigger remediation workflows based on outdated runtime versions. EventBridge can route alerts, but without Config rules, it cannot evaluate compliance. Therefore, this combination does not satisfy the requirement of enforcing runtime standards.
AWS CloudTrail records API activity and delivers logs to S3, but it does not evaluate compliance in real time. CloudTrail cannot enforce runtime requirements or detect outdated configurations automatically. While CloudTrail logs might be used for forensic analysis or manual review, the question requires continuous monitoring and automatic remediation. S3 simply stores the logs and cannot enforce or trigger remediation actions. This makes the combination unsuitable.
AWS Step Functions orchestrate workflows, and SQS handles message queues, but neither service evaluates compliance or triggers continuous monitoring. Step Functions could theoretically run remediation if manually triggered, but it would require heavy custom development. SQS is purely a message buffer and contributes nothing to runtime compliance enforcement. Without Config’s rules engine, these services cannot detect noncompliant Lambda functions automatically.
The combination of AWS Config and Systems Manager Automation best fits the requirement. It offers real-time compliance checks, central governance, automated remediation, multi-account support, and auditable change history. These capabilities match modern DevSecOps practices and ensure consistent and secure management of Lambda runtimes across the organization.
Question 81
A financial institution must ensure that all infrastructure deployments follow strict approval workflows, include change tracking, and prevent unauthorized modifications. The solution must integrate with CI/CD, support multi-account environments, and provide auditable change history. Which AWS service combination is most appropriate?
A) AWS Service Catalog + AWS CodePipeline
B) Amazon EC2 + CloudWatch
C) AWS CloudShell + Lambda
D) AWS DynamoDB + SNS
Answer: A) AWS Service Catalog + AWS CodePipeline
Explanation:
AWS Service Catalog provides a governance-focused catalog of approved infrastructure products that developers or operators can deploy in a controlled manner. It ensures that only vetted and standardized templates can be launched. This prevents unauthorized modifications and enforces compliance. Service Catalog integrates with AWS CloudFormation, enabling detailed tracking of all resource changes. It also supports versioning of products, ensuring that infrastructure administrators can maintain strict control over which versions are permitted for deployment. Approvals can be built directly into the provisioning process, enabling the financial institution to enforce strict workflows for infrastructure changes.
AWS CodePipeline provides CI/CD orchestration and supports approval actions natively. By integrating Service Catalog with CodePipeline, the institution can automate delivery while ensuring that every infrastructure deployment undergoes mandatory approval stages. CodePipeline records all events, providing a complete history of changes, including who approved them and when. This ensures traceability and auditability, which are essential in regulated environments. With AWS Organizations, Service Catalog products can be distributed across multiple accounts, delivering consistent governance enterprise-wide.
Amazon EC2 and CloudWatch do not address approval workflows or prevent unauthorized infrastructure modifications. CloudWatch provides monitoring but no governance or deployment control. EC2 is simply a compute service and cannot enforce standardized deployments.
AWS CloudShell and Lambda do not provide governance or approval structures. CloudShell is an interactive shell environment and cannot enforce deployment standards. Lambda executes code but does not manage infrastructure provisioning governance.
DynamoDB and SNS are storage and messaging services. They cannot enforce provisioning standards or integrate with CI/CD for approval workflows. While they can be components in custom systems, they do not solve the problem directly.
AWS Service Catalog, combined with CodePipeline, delivers controlled, auditable, approval-based infrastructure deployment, fulfilling all requirements for a regulated financial institution.
Question 82
A company uses AWS Elastic Beanstalk to deploy a critical production application. The DevOps team needs to enforce mandatory configuration standards, store all configuration versions, and ensure that any drift from approved settings is automatically detected. The solution must integrate with CI/CD pipelines and provide detailed auditing for compliance. Which AWS service combination best meets these requirements?
A) AWS Elastic Beanstalk Configuration Files + AWS Config
B) Amazon SQS + AWS Lambda
C) Amazon SNS + AWS CloudTrail
D) AWS Batch + AWS Step Functions
Answer: A) AWS Elastic Beanstalk Configuration Files + AWS Config
Explanation:
Elastic Beanstalk configuration files allow teams to define and enforce standardized application environment settings. When included in the application source bundle, these configuration files specify mandatory behaviors related to software packages, environment variables, instance profiles, security groups, and platform hooks. Because these files are version-controlled within the deployment source, they support consistent configuration across all environments. This eliminates the risk of manual errors or inconsistencies during deployments. When integrated with CI/CD pipelines, such as AWS CodePipeline or CodeBuild, configuration files enable automated enforcement of standards during every deployment cycle. Changes to configuration files are tracked automatically through version control systems, providing a complete historical record of modifications. These files act as an authoritative definition of the environment, ensuring that all deployments follow approved settings and align with compliance requirements.
AWS Config continuously monitors Elastic Beanstalk environment settings and resources to detect configuration drift. By creating custom or managed rules, Config can compare actual environment configurations with approved baselines defined in the configuration files or CloudFormation stacks underlying the Beanstalk environment. Config captures snapshots of resource states and logs configuration changes over time, giving auditors complete visibility into when and how any deviation occurred. If unauthorized changes are made, Config flags noncompliance and can trigger notifications or remediation actions using Systems Manager Automation or Lambda. This provides ongoing assurance that production environments remain aligned with governance policies. Config also integrates with AWS Organizations for multi-account compliance, making it suitable for enterprise-level visibility.
Amazon SQS and AWS Lambda do not provide configuration enforcement or drift detection for Elastic Beanstalk. While SQS can queue messages and Lambda can execute logic, they cannot natively monitor Beanstalk environment states or track compliance. Building a custom drift detection system using these two services would require substantial development effort, lack centralized auditing, and not integrate seamlessly with CI/CD or compliance tools. Therefore, they fail to meet the operational governance needs described.
Amazon SNS and AWS CloudTrail offer event notifications and API activity logging, but do not enforce configuration standards. CloudTrail records actions taken by users or services, but it does not monitor whether environment settings deviate from approved baselines. SNS merely distributes notifications and lacks any compliance evaluation capability. Neither service can detect changes in Elastic Beanstalk environment variables, platform configurations, or load balancer settings. Without automated configuration baselines and evaluation, these services cannot enforce compliance.
AWS Batch and AWS Step Functions focus on orchestrating batch computing jobs and workflow automation, respectively. They are not designed for configuration compliance, drift detection, or Beanstalk integration. Batch runs workload jobs on demand rather than monitoring application environments. Step Functions can orchestrate tasks, but provide no built-in capabilities for tracking environment configurations. Implementing compliance enforcement with these services would be impractical and would not satisfy auditing and CI/CD integration needs.
Elastic Beanstalk configuration files define the intended environment state, while AWS Config evaluates and records resource configurations continuously. Together, they ensure enforcement, drift detection, historical tracking, and integration with automated pipelines, making them the correct combination.
Question 83
A DevOps team wants a centralized system to store application secrets, rotate them automatically, and provide fine-grained access control to microservices running on AWS Fargate. The team must also audit access attempts and integrate the solution with deployments in multiple AWS accounts. Which service best satisfies these requirements?
A) AWS Secrets Manager
B) Amazon Redshift
C) Amazon Cognito
D) AWS Amplify
Answer: A) AWS Secrets Manager
Explanation:
AWS Secrets Manager is designed to store sensitive information securely while enabling fine-grained access control through IAM policies. It supports automated rotation of secrets by integrating with services such as Amazon RDS, Redshift, and other databases. For microservice architectures running on AWS Fargate, Secrets Manager integrates seamlessly through task execution roles. Each microservice retrieves secrets only when authorized, and IAM policies ensure least-privilege access. Because Secrets Manager supports cross-account access through resource-based policies, secrets can be shared or managed centrally within an organization-wide security account. The service also logs secret retrieval attempts in CloudTrail, enabling full visibility and auditing. Rotation can occur automatically using Lambda functions, which update applications transparently without requiring redeployment. This matches the requirement for automation, auditing, and secure distribution across multiple accounts.
Amazon Redshift is a data warehousing service and cannot store application secrets securely. Although it can store data including sensitive fields, it does not support secret rotation, nor does it integrate with microservices for short-lived secret retrieval. It also lacks fine-grained access control tied to IAM for application-level secrets.
Amazon Cognito manages user authentication and identity federation, not application secrets. While Cognito stores tokens and helps authenticate users, it cannot manage database credentials, API keys, or other sensitive application-level secrets. It also does not provide automated rotation or cross-account secret distribution.
AWS Amplify is a toolset for building mobile and web applications. It does not manage backend secrets or support rotation of credentials. Amplify focuses on front-end workflows, hosting, and API integration rather than backend security governance.
Secrets Manager provides secure storage, automatic rotation, IAM-based permissions, auditing through CloudTrail, and cross-account support. These capabilities align directly with the DevOps team’s needs, making it the correct answer.
Question 84
A company is implementing GitOps for managing Amazon EKS clusters. The team needs automated synchronization between a Git repository and cluster state, drift detection, and controlled rollbacks. They also need a dashboard for deployment visibility. Which tool best satisfies these requirements?
A) Argo CD
B) AWS Backup
C) Amazon Comprehend
D) AWS Shield
Answer: A) Argo CD
Explanation:
Argo CD is a declarative GitOps continuous delivery tool designed specifically for Kubernetes environments, including Amazon EKS. It continuously monitors Git repositories for changes and synchronizes them with the cluster, ensuring that the actual state of the cluster matches the declared state. Argo CD provides drift detection by highlighting differences between Git-defined configurations and currently deployed resources. When deviations occur, the system can automatically or manually restore the cluster to the desired state. Argo CD supports controlled rollbacks by allowing teams to revert to any previously committed version. It also offers a detailed dashboard showing application state, deployment history, health, and synchronization status. This graphical interface improves visibility and governance. Argo CD integrates with IAM Roles for Service Accounts (IRSA), making it secure for multi-account and multi-team environments.
AWS Backup provides snapshot management and backup services, not GitOps. It cannot synchronize cluster state with a repository or detect drift.
Amazon Comprehend is an NLP service and does not relate to infrastructure management.
AWS Shield protects against DDoS attacks but does not manage cluster deployments or configuration drift.
Argo CD fulfills all GitOps requirements, making it the correct solution.
Question 85
A company runs a high-traffic, globally distributed API on Amazon API Gateway and AWS Lambda. They need a deployment strategy that minimizes downtime, allows gradual traffic shifting, offers automatic rollback if errors rise, and integrates with CI/CD pipelines. Which approach meets these requirements?
A) API Gateway Canary Release + Lambda Aliases
B) CloudTrail Event Logging
C) Amazon S3 Versioning
D) Amazon QuickSight Dashboards
Answer: A) API Gateway Canary Release + Lambda Aliases
Explanation:
API Gateway Canary Release combined with Lambda aliases offers a deployment model that enables smooth and controlled updates for serverless applications. Using canary traffic shifting, teams can direct a small percentage of production traffic to a new version of the Lambda function while the majority of traffic continues using the stable version. Lambda aliases map to function versions, allowing the new version to be exposed only through the canary path. This arrangement provides gradual rollout, making it possible to observe real traffic behavior on the newly deployed code without affecting most users. If metrics indicate problems, the deployment can be halted or rolled back simply by reverting the alias mapping. This model integrates seamlessly with CI/CD tools such as CodePipeline and CodeDeploy. When configured with CodeDeploy, the system can include automation steps that monitor CloudWatch metrics such as error rates, latency, or throttling. If these metrics breach thresholds, automatic rollback occurs. Hence, this strategy minimizes downtime and protects users from defective releases. Traffic shifting durations and percentages are configurable, supporting safe progressive delivery in a production environment.
CloudTrail event logging provides detailed tracking of API calls across AWS services. CloudTrail allows teams to audit actions, detect unusual account activity, and maintain compliance records. However, it lacks the ability to shift traffic, deploy new code versions, or manage controlled releases. CloudTrail’s function is strictly to record events, not to orchestrate deployment strategies or handle API Gateway stage configuration. While CloudTrail may be used for auditing deployment actions, it does not enable canary rollouts, gradual updates, or rollback capabilities for Lambda and API Gateway. Therefore, it cannot fulfill the requirement of supporting deployment safety or integrating with CI/CD in a controlled rollout context.
Amazon S3 versioning preserves object versions within S3 buckets. This provides protection against accidental deletion or overwriting of objects, making it valuable for data durability and recovery. However, S3 versioning has no relationship to application deployment strategies. It does not integrate with API Gateway or Lambda to control traffic flow, rollout patterns, or rollback mechanisms. Although versioned S3 objects could store deployment artifacts such as Lambda packages, S3 versioning itself does not provide deployment orchestration or staged rollouts. Consequently, it is unsuitable for minimizing downtime or supporting progressive delivery for serverless systems.
Amazon QuickSight Dashboards support visualization and analytics by allowing users to build dashboards, interact with data, and create reports. Although dashboards can consume operational metrics, QuickSight does not influence deployment behaviors or traffic routing. It cannot manage Lambda versions, adjust canary percentages, or integrate with CI/CD to trigger deployments. Its purpose is analytical insight, not operational control. It cannot carry out rollback actions or enforce staged deployments.
The combination of API Gateway canary releases and Lambda aliases best satisfies requirements for controlled, low-risk deployments. Canary releases allow incremental traffic exposure, while Lambda aliases ensure version separation and easy rollback. Integration with CodeDeploy ensures automated monitoring and rollback. CloudWatch metrics trigger responses if error rates rise. This approach minimizes downtime, supports automation, and maintains resilience during updates, making it the correct solution.
Question 86
A company has multiple development teams building microservices on Amazon ECS using Fargate. The DevOps team needs to enforce a standard container image scanning process before deployment, ensure policy compliance, and prevent vulnerable images from being promoted. They want a fully managed solution integrated with CI/CD. Which AWS service best meets these needs?
A) Amazon ECR image scanning with enhanced scanning
B) Amazon CloudFront
C) AWS Direct Connect
D) AWS Budgets
Answer: A) Amazon ECR image scanning with enhanced scanning
Explanation:
Amazon ECR image scanning with enhanced scanning provides automated vulnerability detection integrated directly into the container registry. When developers push images to ECR, scanning occurs either on-push or on a recurring schedule. Enhanced scanning, powered by Amazon Inspector, examines container layers and identifies vulnerabilities across a wide range of CVEs. It provides detailed findings with severity levels, recommended remediation steps, and evidence of exposure. Findings can be routed to EventBridge, enabling automated actions in CI/CD pipelines. Teams can block deployments automatically if high-severity vulnerabilities are present. With ECR’s lifecycle policies, development teams can ensure that only approved and scanned images progress to staging and production repositories. The system integrates with IAM for fine-grained access control and ensures that vulnerable or noncompliant images are not deployed. The scanning capability is fully managed and removes the need to install or maintain scanning tools. For organizations with multiple accounts, ECR replication and cross-account access simplify governance. Integration with CodePipeline or CodeBuild allows teams to implement scanning gates that enforce compliance before deployment.
Amazon CloudFront is a fast content Delivery Network used for caching web content and lowering latency. While CloudFront distributes static and dynamic content globally, it does not examine container images or evaluate vulnerabilities. CloudFront has no integration with container registries or CI/CD pipelines for policy enforcement. Its role is delivery optimization, not security validation or pipeline gating.
AWS Direct Connect provides dedicated network connectivity between on-premises environments and AWS. Direct Connect improves throughput and reduces latency for hybrid applications. However, it has no relationship to container image scanning. It cannot evaluate images, generate vulnerability reports, or integrate with CI/CD gates. Direct Connect’s purpose is networking, not DevSecOps.
AWS Budgets monitors and controls AWS cost usage. While useful for financial governance, it does not enforce security policies for container images or control deployments. It cannot prevent vulnerable images from being used, nor can it integrate into build pipelines.
ECR enhanced scanning, integrated with Amazon Inspector and EventBridge, supports automated vulnerability detection, compliance enforcement, CI/CD gating, reporting, and multi-account governance. It is the only option that meets all stated DevOps requirements.
Question 87
A large enterprise wants centralized management of Terraform deployments across multiple AWS accounts. They need policy enforcement, drift detection, repeatable deployments, and the ability to approve or deny infrastructure changes before provisioning. Which solution best fits these needs?
A) Terraform Cloud/Enterprise with AWS IAM integration
B) Amazon Macie
C) AWS Glue
D) Amazon Neptune
Answer: A) Terraform Cloud/Enterprise with AWS IAM integration
Explanation:
Terraform Cloud or Terraform Enterprise provides centralized Terraform execution, policy enforcement, state management, and multi-account deployment capabilities. It allows organizations to store Terraform state securely in a central workspace, preventing corruption or accidental deletion. Workspaces create separation between environments such as dev, staging, and production. Policy-as-code using Sentinel enforces governance rules across Terraform runs. Sentinel policies can check resource configuration, naming standards, encryption requirements, or network rules before Terraform plans are approved. Administrators can set mandatory approval workflows to ensure that infrastructure changes must be reviewed by designated personnel before apply operations can proceed. Terraform Cloud integrates with AWS IAM through programmatic credentials or assumed roles, allowing it to manage resources across multiple AWS accounts securely. Drift detection is enabled through automated plan runs triggered periodically or by VCS changes, allowing teams to see differences between desired and actual state. CI/CD integrations allow infrastructure changes to be triggered automatically through Git pushes, while still enforcing policies and approvals. This aligns with enterprise IaC governance requirements.
Amazon Macie focuses on scanning S3 buckets for sensitive data. It cannot execute Terraform, enforce infrastructure policies, or manage Terraform state. AWS Glue is an ETL and data integration tool, not an infrastructure management platform. Amazon Neptune is a graph database and unrelated to Terraform governance.
Terraform Cloud/Enterprise provides controlled, auditable, policy-governed IaC management across accounts, making it the correct solution.
Question 88
A large organization uses AWS CloudFormation to manage infrastructure across multiple business units. They want to ensure that teams cannot deploy CloudFormation stacks that violate organizational standards such as mandatory encryption, approved instance types, restricted IAM permissions, and proper tagging. They also need a centralized enforcement mechanism with the ability to block noncompliant deployments before resources are provisioned. Which AWS service best addresses these requirements?
A) AWS CloudFormation Guard
B) Amazon Inspector
C) AWS Shield
D) Amazon EMR
Answer: A) AWS CloudFormation Guard
Explanation:
AWS CloudFormation Guard provides a policy-as-code framework designed specifically to validate CloudFormation templates before they are deployed. It allows organizations to define mandatory compliance rules that CloudFormation templates must satisfy. These rules can enforce encryption requirements, restrict instance types, ensure required tags, limit IAM permissions, and enforce networking constraints. Guard performs template validation during the CI/CD process, ensuring that noncompliant templates are rejected before deployment, preventing the creation of unauthorized or insecure resources. The rules are written in a declarative language suited to defining governance standards. Because Guard policies can be centrally managed, organizations can apply consistent validation across all business units, environments, and accounts. CloudFormation Guard integrates smoothly with pipelines built using services such as AWS CodePipeline, Jenkins, or GitHub Actions. When a template fails validation, the deployment process stops immediately, eliminating the risk of configuration drift or accidental bypass of organizational controls. This pre-deployment validation is essential for large enterprises that require strong governance controls and need to ensure that only compliant infrastructure is provisioned. This centralized mechanism strengthens security posture and ensures consistent infrastructure deployment practices.
Amazon Inspector focuses on vulnerability scanning of running workloads rather than validating infrastructure templates. It scans EC2 instances, container images, Lambda functions, and associated packages for known vulnerabilities and creates security findings. While Inspector provides strong runtime security capabilities, it does not evaluate CloudFormation templates for compliance before deployment. Therefore, it cannot enforce organizational standards during infrastructure provisioning.
AWS Shield protects applications from Distributed Denial of Service attacks. It is a network protection service that works with CloudFront, Route 53, and other public-facing resources to mitigate large-scale traffic floods. While Shield helps ensure application availability, it has no mechanism to validate CloudFormation templates, enforce security policies on infrastructure definitions, or block deployments based on internal governance requirements. Shield is not relevant to template compliance.
Amazon EMR provides a managed Hadoop and big data processing environment. Although EMR is important for data analytics workloads, it has no connection with CloudFormation governance. It does not validate infrastructure templates and does not function as a compliance enforcement layer for CloudFormation deployments. EMR is entirely unrelated to the governance and compliance domain in which the organization needs a solution.
CloudFormation Guard is the only service designed explicitly to validate CloudFormation templates against predetermined rules before resource provisioning occurs. It supports centralized management, continuous integration, consistent enforcement, and direct control over which templates are allowed to progress. Because the requirements involve preventing noncompliant stacks and enforcing mandatory configurations before deployment, CloudFormation Guard meets all criteria and is the correct answer.
Question 89
A DevOps team maintains an application hosted on Amazon EKS. The organization needs centralized log collection, real-time querying, indexless search, anomaly detection, and dashboards with minimal operational overhead. They want a fully managed solution that integrates with Kubernetes workloads and requires no cluster-based storage. Which AWS service best meets these requirements?
A) Amazon CloudWatch Logs with CloudWatch Logs Insights
B) Amazon QuickSight
C) Amazon MemoryDB
D) AWS Glue
Answer: A) Amazon CloudWatch Logs with CloudWatch Logs Insights
Explanation:
Amazon CloudWatch Logs combined with CloudWatch Logs Insights provides a managed log collection, aggregation, and query platform suitable for Kubernetes workloads running on Amazon EKS. CloudWatch integrates with tools such as Fluent Bit, enabling automatic log forwarding from pods, nodes, and system components to centralized log groups. This eliminates the need to manage storage inside the EKS cluster and provides a durable, scalable, and cost-efficient log retention solution. CloudWatch Logs Insights offers indexless querying, allowing DevOps teams to search and analyze logs in real time without the need to build or maintain indexing systems. Queries can filter, aggregate, parse, and transform log data, providing operational insight into EKS workloads. Because Logs Insights is a fully managed service, there is no need for cluster-based storage, Elasticsearch clusters, or self-managed logging systems. CloudWatch’s anomaly detection uses machine learning to identify unusual patterns in metrics, which helps teams catch performance degradation or unexpected workload behaviors. CloudWatch dashboards allow the creation of visualizations that incorporate metrics and logs, giving developers and operators real-time visibility into application health. Integration with EventBridge allows alerting and operational automation based on log patterns. Together, these services provide a holistic logging and monitoring platform with minimal operational burden.
Amazon QuickSight is a BI analytics and dashboarding service meant to visualize business data rather than operational logs. While it supports interactive dashboards, it cannot act as a primary logging system. It does not ingest Kubernetes logs directly or provide indexless log search, making it unsuitable for real-time operational diagnostics.
Amazon MemoryDB is an in-memory database compatible with Redis. It is optimized for microsecond latency access to structured data and caching use cases. MemoryDB does not provide log management, log analytics, anomaly detection, or integration with EKS logging pipelines. It cannot ingest logs or support log-based search and dashboards.
AWS Glue provides ETL capabilities for data lakes and analytics workflows. It cannot serve as a log aggregation platform or indexless log query engine. Glue focuses on batch transformations and metadata management rather than real-time operational insights or Kubernetes integration.
CloudWatch Logs and Logs Insights meet all the stated needs by offering log ingestion, real-time search, anomaly detection, dashboards, and minimal overhead. Therefore, the correct solution is CloudWatch Logs with Logs Insights.
Question 90
A company needs a secure mechanism to manage short-lived access for developers who occasionally require temporary elevated permissions to perform production troubleshooting. The process must require approval, log all access events, automatically revoke permissions after a set duration, and avoid the long-term use of static IAM credentials. Which solution best satisfies these requirements?
A) AWS IAM Access Requests using AWS IAM Identity Center and Permission Sets
B) AWS WAF
C) Amazon MQ
D) Amazon Lightsail
Answer: A) AWS IAM Access Requests using AWS IAM Identity Center and Permission Sets
Explanation:
AWS IAM Identity Center provides centralized identity and access management for AWS environments. Using permission sets, administrators can define the exact permissions developers receive when they request access. Temporary elevated access can be configured using short-lived session durations, ensuring that permissions automatically expire. Approvals can be integrated through external ticketing systems or automated workflows that evaluate requests before granting elevated access. Identity Center logs all sign-in and access events through CloudTrail, providing full audit visibility. Because it issues temporary credentials through AWS IAM Identity Center rather than static long-term keys, the solution reduces security risk and ensures compliance with best practices for privileged access management. Developers only receive permissions for the specific tasks needed, following the principle of least privilege.
AWS WAF protects applications from web exploits but does not manage identity or temporary access. Amazon MQ is a messaging service unrelated to access control. Amazon Lightsail provides simple virtual servers but does not handle IAM governance. Identity Center with permission sets is the only solution satisfying secure, time-bound, auditable elevated access.