Amazon AWS Certified DevOps Engineer – Professional DOP-C02 Exam Dumps and Practice Test Questions Set 8 Q 141-160

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Question 141

A company runs multiple microservices on Amazon ECS Fargate and wants safe, automated canary deployments with traffic shifting, monitoring, and rollback in case of failures. Which solution is best?

A) ECS rolling updates with custom health check grace periods.

B) AWS CodeDeploy blue/green deployments integrated with ECS and ALB.

C) CloudFormation stack updates with rollback enabled.

D) ALB slow start mode for gradual traffic ramp-up.

Answer: B)

Explanation

A) ECS rolling updates enable gradual replacement of existing tasks with new ones to maintain service availability. Custom health check grace periods ensure that slow-starting containers are not marked unhealthy prematurely. However, rolling updates cannot perform automatic rollback based on runtime application-level failures or metrics. Traffic is replaced at the task level without incremental percentage-based control, and the monitoring capabilities are limited to task health rather than application behavior. While rolling updates are adequate for basic deployments, they do not provide a fully managed canary deployment strategy, leaving production workloads at risk if new versions contain critical errors. This approach also requires manual intervention for traffic monitoring and rollback.

B) AWS CodeDeploy blue/green deployments provides a fully managed deployment orchestration mechanism integrated with ECS and Application Load Balancer (ALB). It allows the creation of a new target group for the updated service and enables incremental traffic shifting, where a percentage of traffic is routed to the new service version at configured intervals. ALB health checks and CloudWatch metrics monitor the health of the new tasks, automatically rolling back traffic to the previous version if failures or performance degradation are detected. This solution requires minimal operational effort, is ECS-native, and ensures safe, automated deployments with monitoring and rollback. CodeDeploy also supports canary and linear deployment strategies, giving teams flexibility in rollout plans and ensuring reliability during production deployments.

C) CloudFormation stack updates provide rollback functionality for template-level errors during resource creation or updates. While CloudFormation can revert infrastructure to a previous known state if a deployment fails, it does not handle runtime application health monitoring, traffic shifting, or automatic rollback at the service level. This makes it unsuitable for safe canary deployments, where application behavior must dictate rollback decisions.

D) ALB slow start mode gradually ramps traffic to newly registered targets to prevent overload. While this feature can help mitigate sudden spikes in traffic and allow new containers to warm up, it does not orchestrate deployments, monitor application health, or automatically rollback deployments. It is purely a traffic smoothing mechanism and does not fulfill the requirements of a fully managed, monitored canary deployment.

Why the correct answer is B): AWS CodeDeploy blue/green deployments provide automated, safe canary deployment orchestration, including incremental traffic shifting, health monitoring, and rollback capabilities. Other options only cover parts of the deployment process and do not satisfy all production-grade requirements for canary deployments.

Question 142

A company stores large volumes of logs in Amazon S3 and wants a serverless solution to extract structured fields, index data, and enable fast search queries without managing servers. Which solution is best?

A) Deploy ELK stack on EC2.

B) Use S3 Select for querying logs.

C) Amazon OpenSearch Serverless with S3 ingestion pipelines.

D) Store logs in DynamoDB with Global Secondary Indexes.

Answer: C)

Explanation

A) Deploying an ELK stack on EC2 allows for advanced log analytics, including full-text search, aggregations, and dashboards via Kibana. However, this approach requires provisioning, scaling, patching, and monitoring EC2 instances, which violates the serverless requirement. Large-scale log ingestion introduces operational complexity, including capacity planning, instance management, and monitoring. While powerful, ELK on EC2 adds overhead and is not truly serverless, making it less ideal for companies seeking minimal operational management.

B) S3 Select enables SQL-like queries on individual objects in S3. While useful for lightweight, ad hoc queries, it cannot index multiple objects or perform efficient full-text search across large datasets. S3 Select does not provide aggregation, automated field extraction, or visualization capabilities, making it unsuitable for scalable, production-grade log analysis.

C) Amazon OpenSearch Serverless is a fully managed, serverless solution for log analytics. It integrates with S3 ingestion pipelines to automatically index incoming logs, supporting structured field extraction, full-text search, aggregations, and near real-time queries. OpenSearch Serverless scales automatically, requires no server management, and integrates with monitoring and alerting tools such as CloudWatch. This approach fulfills all requirements: serverless operation, automated indexing, fast query performance, minimal operational burden, and near real-time visibility into log data, making it ideal for large-scale log analysis.

D) DynamoDB with Global Secondary Indexes provides fast lookups for structured data. While DynamoDB excels in low-latency queries for well-defined keys, it cannot efficiently handle unstructured logs or provide full-text search capabilities. Using DynamoDB for log analytics would necessitate additional infrastructure, such as OpenSearch or Lambda for processing and indexing, increasing complexity and cost.

Why the correct answer is C): OpenSearch Serverless provides a scalable, serverless, fully managed solution for log ingestion, indexing, and fast query performance, meeting all the requirements. Other options either require server management, lack necessary search capabilities, or increase operational overhead.

Question 143

A DevOps team wants to reduce AWS Lambda cold start latency for high-traffic functions while minimizing costs for infrequently invoked functions. Which solution is best?

A) Enable Provisioned Concurrency for high-traffic functions.

B) Increase memory allocation for all Lambda functions.

C) Deploy Lambda functions in a VPC.

D) Replace Lambda with ECS Fargate tasks.

Answer: A)

Explanation

A) Provisioned Concurrency pre-warms Lambda execution environments to eliminate cold start latency. By applying it selectively to high-traffic functions, latency-sensitive endpoints are immediately responsive, while infrequently invoked functions remain on-demand, controlling costs. This is a serverless-native solution that optimizes performance without introducing operational overhead or unnecessary expenses. It also allows granular scaling to meet specific traffic patterns, making it cost-efficient and effective in production environments.

B) Increasing memory allocation slightly improves CPU and initialization speed, potentially reducing cold start latency marginally. However, it does not prevent cold starts entirely, and higher memory allocations increase costs across all functions, including low-traffic ones. This method is inefficient compared to targeted Provisioned Concurrency.

C) Deploying Lambda in a VPC historically increased cold start latency due to Elastic Network Interface (ENI) attachment overhead. While recent networking improvements have mitigated some of this delay, VPC deployment does not eliminate cold starts and introduces additional complexity, making it a suboptimal solution.

D) Replacing Lambda with ECS Fargate tasks avoids cold starts since containers are long-lived. However, this introduces operational overhead, including task scheduling, monitoring, scaling, and maintenance. It also violates the minimal code-change requirement and can increase costs due to always-on containers, making it less efficient than Lambda with Provisioned Concurrency.

Why the correct answer is A): Provisioned Concurrency selectively pre-warms high-traffic Lambda functions, reducing cold start latency while keeping costs low for infrequently invoked functions. Other solutions either fail to eliminate cold starts effectively or increase operational complexity and cost.

Question 144

A company requires pre-deployment enforcement of compliance policies on Terraform modules deployed through CI/CD pipelines. Policies include mandatory tags, encryption, and restricted resource types. Violations must block deployment. Which solution is best?

A) AWS Config rules.

B) Sentinel policies in Terraform Cloud/Enterprise.

C) Git pre-commit hooks.

D) CloudFormation Guard.

Answer: B)

Explanation

A) AWS Config rules operate after resource deployment. While Config can detect noncompliance and trigger remediation, it cannot prevent Terraform modules from being applied in a CI/CD pipeline. Config is reactive rather than preventive, which violates the pre-deployment enforcement requirement.

B) Sentinel policies are a policy-as-code framework integrated with Terraform Cloud and Terraform Enterprise. Policies are evaluated during terraform plan or terraform apply. Violations fail the run, automatically blocking deployment. Sentinel supports rules for mandatory tags, encryption enforcement, and resource type restrictions. Integration with CI/CD ensures automated pre-deployment governance, guaranteeing noncompliant resources are prevented from being provisioned. Sentinel also allows fine-grained policy configuration for different environments and teams, providing both flexibility and enforceable guardrails.

C) Git pre-commit hooks enforce coding standards or policy rules locally before committing code. They are bypassable and do not integrate reliably with automated CI/CD pipelines. Pre-commit hooks cannot block Terraform applies, making them insufficient for automated pre-deployment compliance enforcement.

D) CloudFormation Guard (cfn-guard) validates CloudFormation templates against defined rules. While effective for CloudFormation, it is incompatible with Terraform modules without converting the templates, adding operational overhead. It cannot natively enforce policies in Terraform CI/CD pipelines.

Why the correct answer is B): Sentinel policies enforce pre-deployment compliance automatically, blocking noncompliant Terraform modules in CI/CD pipelines. Other options are reactive, bypassable, or incompatible.

Question 145

A company wants end-to-end distributed tracing for serverless APIs, including API Gateway, Lambda, DynamoDB, and S3, with minimal code changes, latency visualization, and bottleneck detection. Which solution is best?

A) Enable AWS X-Ray active tracing.

B) Use CloudWatch Logs Insights for manual correlation.

C) Deploy OpenTelemetry on EC2 instances.

D) Implement manual correlation IDs in code.

Answer: A)

Explanation

A) AWS X-Ray active tracing provides fully managed distributed tracing for serverless architectures. It automatically captures segments and subsegments for Lambda, API Gateway, DynamoDB, and S3. The service map visualizes latency, errors, and performance bottlenecks across all services. Minimal code changes are required—enabling active tracing on Lambda functions and optionally using the X-Ray SDK for custom subsegments. X-Ray scales automatically, integrates with CloudWatch dashboards, and provides near real-time observability. This solution fulfills all requirements: serverless integration, minimal code changes, latency visualization, and end-to-end tracing.

B) CloudWatch Logs Insights allows querying logs for latency and errors. Manual correlation of request IDs is possible but requires significant effort, is error-prone, and does not provide automated service maps or bottleneck detection. It is impractical for production-scale tracing and violates the minimal code-change requirement.

C) Deploying OpenTelemetry on EC2 introduces operational overhead. Each service must be instrumented, and collectors deployed, scaled, and maintained. OpenTelemetry does not natively integrate with serverless AWS services, making it less suitable for minimal-code, serverless tracing.

D) Implementing manual correlation IDs requires pervasive code changes across services. While helpful for debugging, it does not provide automated visualization, service maps, or bottleneck detection. Maintaining correlation across multiple services is difficult and error-prone.

Why the correct answer is A): AWS X-Ray provides automated, serverless, end-to-end tracing with latency visualization and bottleneck detection while requiring minimal code changes. Other options require manual effort, additional infrastructure, or cannot provide full automated observability.

Question 146

A company runs microservices on Amazon ECS Fargate and requires safe, automated canary deployments with traffic shifting, monitoring, and automatic rollback. Which solution is best?

A) ECS rolling updates with custom health check grace periods.

B) AWS CodeDeploy blue/green deployments integrated with ECS and ALB.

C) CloudFormation stack updates with rollback enabled.

D) ALB slow start mode for gradual traffic ramp-up.

Answer: B)

Explanation

A) ECS rolling updates replace tasks gradually while maintaining service availability. Health check grace periods prevent slow-starting tasks from being marked unhealthy prematurely. However, rolling updates do not provide automated rollback triggered by application-level failures, nor do they allow fine-grained traffic control between old and new versions. The replacement occurs at the task level without incremental traffic percentages or monitoring for business metrics. This method provides basic deployment safety but lacks fully automated canary deployment capabilities required for production-critical workloads.

B) AWS CodeDeploy blue/green deployments offers a fully managed canary deployment solution integrated with ECS and ALB. A new target group is created for updated services, and traffic can be shifted incrementally to the new version. ALB health checks and CloudWatch metrics monitor task health, and CodeDeploy can automatically rollback traffic to the previous version if failures occur. This solution ensures safe, progressive, and automated deployments, with monitoring and rollback capabilities. It supports canary and linear deployment strategies, allowing the team to minimize risk during production rollouts while requiring minimal operational management.

C) CloudFormation stack updates provide rollback for infrastructure template errors. While effective for maintaining template consistency, it does not handle application-level health checks, traffic shifting, or automatic rollback triggered by runtime application performance. This makes it unsuitable for orchestrating canary deployments in a dynamic microservices environment.

D) ALB slow start mode gradually ramps traffic to new targets, reducing the risk of overloading instances. While beneficial for mitigating sudden traffic spikes, it does not manage deployments, monitor application health, or automatically rollback failed updates. Slow start is only a traffic-smoothing feature, insufficient for fully automated canary deployment strategies.

Why the correct answer is B): AWS CodeDeploy blue/green deployments provide automated canary deployment orchestration with traffic shifting, monitoring, and rollback. Other options address only partial aspects of the deployment lifecycle and cannot ensure safe, progressive production rollouts.

Question 147

A company stores high-volume logs in Amazon S3 and requires a serverless solution for extracting structured fields, indexing, and fast search. Which solution is best?

A) Deploy ELK stack on EC2.

B) Use S3 Select for querying logs.

C) Amazon OpenSearch Serverless with S3 ingestion pipelines.

D) Store logs in DynamoDB with Global Secondary Indexes.

Answer: C)

Explanation

A) Deploying an ELK stack on EC2 offers full log analytics with Kibana dashboards, aggregation, and full-text search. However, it requires managing, scaling, and patching EC2 instances, which violates the serverless requirement. Operational overhead for capacity planning, monitoring, and updates is significant, making it unsuitable for a fully managed solution.

B) S3 Select allows SQL-like queries on individual S3 objects. While convenient for small, ad hoc queries, it cannot index multiple objects or provide scalable full-text search across large datasets. S3 Select lacks aggregation, visualization, and automated processing capabilities, rendering it impractical for production-grade log analytics.

C) Amazon OpenSearch Serverless provides a fully managed, serverless solution for log analytics. It integrates with S3 ingestion pipelines to automatically index incoming logs, supporting structured field extraction, full-text search, aggregation, and near real-time queries. OpenSearch Serverless scales automatically without server management and integrates with monitoring and alerting tools. This meets all requirements: serverless operation, automated indexing, fast query performance, and minimal operational burden.

D) DynamoDB with Global Secondary Indexes is suitable for low-latency lookups on structured data. However, it cannot efficiently handle unstructured logs or full-text search at scale. Using DynamoDB would require additional infrastructure, such as OpenSearch, increasing complexity and operational cost.

Why the correct answer is C): OpenSearch Serverless delivers a scalable, serverless, fully managed solution for log analytics, automated indexing, and fast query performance. Other options require servers, lack full-text search capabilities, or increase operational overhead.

Question 148

A DevOps team wants to reduce AWS Lambda cold start latency for high-traffic functions while controlling costs for infrequently invoked functions. Which solution is best?

A) Enable Provisioned Concurrency for high-traffic functions.

B) Increase memory allocation for all Lambda functions.

C) Deploy Lambda functions in a VPC.

D) Replace Lambda with ECS Fargate tasks.

Answer: A)

Explanation

A) Provisioned Concurrency pre-warms Lambda execution environments, eliminating cold start latency. Applying it selectively to high-traffic functions ensures low-latency performance for critical workloads while keeping infrequently invoked functions on-demand to control costs. This is a serverless-native, efficient, and cost-effective solution that reduces cold starts without increasing operational overhead.

B) Increasing memory allocation can improve CPU and initialization speed, slightly reducing cold start latency. However, it does not fully eliminate cold starts and increases costs for all functions, including low-traffic ones. Selective Provisioned Concurrency is more precise and cost-effective.

C) Deploying Lambda in a VPC historically increased cold start latency due to ENI initialization overhead. While improvements have mitigated some of these delays, VPC deployment does not prevent cold starts and adds complexity, making it less effective.

D) Replacing Lambda with ECS Fargate tasks avoids cold starts since containers are long-lived. However, this introduces significant operational overhead, including task management, scaling, monitoring, and always-on costs. It also violates the minimal code-change requirement.

Why the correct answer is A): Provisioned Concurrency selectively pre-warms high-traffic Lambda functions, reducing cold start latency while controlling costs for low-traffic workloads. Other options either fail to remove cold starts or introduce operational complexity and expense.

Question 149

A company needs pre-deployment enforcement of compliance policies on Terraform modules deployed through CI/CD pipelines. Policies include mandatory tags, encryption, and resource restrictions. Violations must block deployment. Which solution is best?

A) AWS Config rules.

B) Sentinel policies in Terraform Cloud/Enterprise.

C) Git pre-commit hooks.

D) CloudFormation Guard.

Answer: B)

Explanation

A) AWS Config rules operate after resources are deployed. They can detect noncompliance and trigger remediation but cannot prevent Terraform modules from being applied. Config is reactive and does not satisfy the pre-deployment enforcement requirement.

B) Sentinel policies provide policy-as-code enforcement within Terraform Cloud/Enterprise. Policies are evaluated during terraform plan or terraform apply. Violations fail the run, automatically blocking deployment. Sentinel supports tag enforcement, encryption requirements, and restricting resource types. Integration with CI/CD pipelines ensures automated pre-deployment governance, preventing noncompliant resources from reaching production. Sentinel allows fine-grained configuration for different environments, offering both flexibility and enforceable guardrails.

C) Git pre-commit hooks enforce rules locally before committing code. They are bypassable and do not reliably integrate with automated CI/CD pipelines. Pre-commit hooks cannot block Terraform applies, making them insufficient for automated pre-deployment compliance enforcement.

D) CloudFormation Guard validates CloudFormation templates but is incompatible with Terraform modules without converting the templates, adding operational complexity. It cannot natively enforce policies in Terraform CI/CD pipelines.

Why the correct answer is B): Sentinel policies enforce pre-deployment compliance automatically, blocking noncompliant Terraform modules in CI/CD pipelines. Other options are reactive, bypassable, or incompatible.

Question 150

A company wants end-to-end distributed tracing for serverless APIs, including API Gateway, Lambda, DynamoDB, and S3, with minimal code changes, latency visualization, and bottleneck detection. Which solution is best?

A) Enable AWS X-Ray active tracing.

B) Use CloudWatch Logs Insights for manual correlation.

C) Deploy OpenTelemetry on EC2 instances.

D) Implement manual correlation IDs in code.

Answer: A)

Explanation

A) AWS X-Ray active tracing provides fully managed distributed tracing for serverless applications. It automatically captures segments and subsegments for Lambda, API Gateway, DynamoDB, and S3. The service map visualizes latency, errors, and bottlenecks. Minimal code changes are required—enable active tracing on Lambda functions and optionally use the X-Ray SDK for custom subsegments. X-Ray scales automatically, integrates with CloudWatch dashboards, and provides near real-time observability. This solution meets all requirements: serverless integration, minimal code changes, latency visualization, and end-to-end tracing.

B) CloudWatch Logs Insights allows querying logs for latency and errors. Manual correlation is possible but is labor-intensive, error-prone, and does not provide automated service maps or bottleneck detection. It is impractical for production-scale tracing and violates minimal code-change requirements.

C) Deploying OpenTelemetry on EC2 introduces operational overhead. Each service requires instrumentation, and collectors must be deployed, scaled, and maintained. OpenTelemetry does not natively integrate with serverless AWS services, making it unsuitable for minimal-code solutions.

D) Implementing manual correlation IDs requires pervasive code changes across services. While helpful for debugging, it does not provide automated visualization, service maps, or bottleneck detection, and maintaining correlation across multiple services is difficult and error-prone.

Why the correct answer is A): AWS X-Ray provides automated, serverless, end-to-end tracing with latency visualization and bottleneck detection while requiring minimal code changes. Other options require manual effort, additional infrastructure, or cannot provide automated observability.

Question 151

A company runs multiple microservices on Amazon ECS Fargate and requires safe, automated canary deployments with traffic shifting, monitoring, and automatic rollback. Which solution is best?

A) ECS rolling updates with custom health check grace periods.

B) AWS CodeDeploy blue/green deployments integrated with ECS and ALB.

C) CloudFormation stack updates with rollback enabled.

D) ALB slow start mode for gradual traffic ramp-up.

Answer: B)

Explanation

A) ECS rolling updates allow tasks to be replaced gradually to maintain service availability. Health check grace periods prevent slow-starting containers from being marked unhealthy. However, ECS rolling updates do not provide automated rollback triggered by application-level errors or allow incremental traffic shifting between old and new versions. Rolling updates replace tasks without monitoring metrics at the application level, leaving a risk for production failures if the new version contains bugs. While rolling updates maintain basic availability, they cannot orchestrate a fully managed canary deployment, requiring manual monitoring and intervention for rollback.

B) AWS CodeDeploy blue/green deployments is a fully managed canary deployment solution integrated with ECS and ALB. It allows a new target group to be created for updated services and enables incremental traffic shifting from the old version to the new version. ALB health checks combined with CloudWatch metrics monitor the health of the new tasks. If failures occur, traffic is automatically rolled back to the previous version, reducing risk during deployments. CodeDeploy also supports canary and linear deployment strategies, allowing fine-grained control over deployment percentages and intervals. This solution ensures safe, automated, and monitored deployments with minimal operational overhead.

C) CloudFormation stack updates provide rollback for template-level errors during infrastructure updates. While CloudFormation ensures resource-level consistency, it does not manage runtime application health, traffic shifting, or rollback triggered by performance metrics, making it insufficient for canary deployments at the application level.

D) ALB slow start mode gradually ramps traffic to new targets to prevent overload. Although it mitigates sudden spikes, it does not orchestrate deployments, monitor application health, or provide automated rollback, serving only as a traffic smoothing mechanism rather than a complete deployment strategy.

Why the correct answer is B): AWS CodeDeploy blue/green deployments provide safe, automated canary deployment orchestration, including traffic shifting, monitoring, and rollback. Other options cover only partial aspects of deployment management.

Question 152

A company stores high-volume logs in Amazon S3 and requires a serverless solution to extract structured fields, index data, and enable fast search queries. Which solution is best?

A) Deploy ELK stack on EC2.

B) Use S3 Select for querying logs.

C) Amazon OpenSearch Serverless with S3 ingestion pipelines.

D) Store logs in DynamoDB with Global Secondary Indexes.

Answer: C)

Explanation

A) Deploying an ELK stack on EC2 allows full-featured log analytics, including dashboards and full-text search. However, it requires manual provisioning, scaling, and maintenance of EC2 instances, which is contrary to the serverless requirement. Operational overhead, patch management, and capacity planning make it unsuitable for large-scale, fully managed log processing.

B) S3 Select allows SQL-like queries on individual S3 objects. While effective for small ad hoc queries, it cannot index multiple objects or provide scalable full-text search. It also lacks aggregation, automated structured field extraction, and visualization, making it unsuitable for production-grade log analytics.

C) Amazon OpenSearch Serverless provides a fully managed, serverless solution for log analytics. It integrates with S3 ingestion pipelines to automatically index incoming logs. Features include structured field extraction, full-text search, aggregations, and near real-time queries. OpenSearch Serverless scales automatically, requires no server management, and integrates with monitoring and alerting tools like CloudWatch. This solution meets all requirements: serverless operation, automated indexing, fast query performance, and minimal operational burden.

D) DynamoDB with Global Secondary Indexes provides low-latency lookups for structured data. However, it cannot efficiently handle unstructured logs or provide full-text search. Implementing log analytics with DynamoDB would require additional infrastructure, increasing complexity and operational cost.

Why the correct answer is C): OpenSearch Serverless delivers a scalable, fully managed, serverless solution for log analytics with fast search, automated indexing, and minimal operational overhead. Other options either require server management or cannot provide full-text search at scale.

 

Question 153

A DevOps team wants to reduce AWS Lambda cold start latency for high-traffic functions while controlling costs for low-traffic functions. Which solution is best?

A) Enable Provisioned Concurrency for high-traffic functions.

B) Increase memory allocation for all Lambda functions.

C) Deploy Lambda functions in a VPC.

D) Replace Lambda with ECS Fargate tasks.

Answer: A)

Explanation

A) Provisioned Concurrency pre-warms Lambda execution environments to prevent cold starts. By applying it only to high-traffic functions, latency-sensitive endpoints are immediately responsive, while low-traffic functions remain on-demand, minimizing costs. This is a serverless-native, cost-efficient solution that optimizes performance without increasing operational complexity. Provisioned Concurrency can scale with traffic patterns, ensuring low-latency responses for critical functions.

B) Increasing memory allocation slightly improves CPU and initialization speed, which may reduce cold start latency marginally. However, it does not eliminate cold starts entirely and increases costs for all functions, including low-traffic ones. This method is less precise and less cost-effective than Provisioned Concurrency.

C) Deploying Lambda in a VPC historically increases cold start latency due to ENI attachment. While recent improvements have reduced this delay, VPC deployment does not prevent cold starts and introduces additional complexity, making it a suboptimal solution.

D) Replacing Lambda with ECS Fargate tasks avoids cold starts because containers are long-lived. However, it introduces operational overhead, including task management, scaling, monitoring, and always-on costs, and violates the minimal code-change requirement.

Why the correct answer is A): Provisioned Concurrency selectively pre-warms high-traffic Lambda functions, reducing cold start latency while controlling costs. Other options either fail to eliminate cold starts or increase operational complexity and expense.

Question 154

A company requires pre-deployment enforcement of compliance policies on Terraform modules in CI/CD pipelines. Policies include mandatory tags, encryption, and restricted resource types. Violations must block deployment. Which solution is best?

A) AWS Config rules.

B) Sentinel policies in Terraform Cloud/Enterprise.

C) Git pre-commit hooks.

D) CloudFormation Guard.

Answer: B)

Explanation

A) AWS Config rules evaluate resources after deployment. Config can detect violations and trigger remediation, but it cannot prevent Terraform modules from being applied, making it reactive rather than preventive. It does not fulfill pre-deployment compliance enforcement requirements.

B) Sentinel policies provide policy-as-code enforcement in Terraform Cloud/Enterprise. Policies are evaluated during terraform plan or terraform apply. Violations fail the run, automatically preventing deployment. Sentinel supports enforcing mandatory tags, encryption, and restricting resource types. Integrated into CI/CD pipelines, it ensures pre-deployment governance and prevents noncompliant resources from reaching production. Sentinel allows fine-grained configuration across environments, providing flexibility and enforceable guardrails without increasing operational overhead.

C) Git pre-commit hooks enforce coding standards locally before code commits. They are bypassable and do not reliably integrate with automated CI/CD pipelines, making them insufficient for pre-deployment policy enforcement.

D) CloudFormation Guard validates CloudFormation templates. While effective for CloudFormation, it is not compatible with Terraform modules without converting templates, adding complexity. It cannot natively enforce policies in Terraform CI/CD pipelines.

Why the correct answer is B): Sentinel policies enforce pre-deployment compliance automatically, blocking noncompliant Terraform modules in CI/CD pipelines. Other options are reactive, bypassable, or incompatible with Terraform.

Question 155

A company wants end-to-end distributed tracing for serverless APIs including API Gateway, Lambda, DynamoDB, and S3, with minimal code changes, latency visualization, and bottleneck detection. Which solution is best?

A) Enable AWS X-Ray active tracing.

B) Use CloudWatch Logs Insights for manual correlation.

C) Deploy OpenTelemetry on EC2 instances.

D) Implement manual correlation IDs in code.

Answer: A)

Explanation

A) AWS X-Ray active tracing provides fully managed distributed tracing for serverless applications. It automatically captures segments and subsegments for Lambda, API Gateway, DynamoDB, and S3. The service map visualizes latency, errors, and bottlenecks across services. Minimal code changes are required—enable active tracing on Lambda functions and optionally use the X-Ray SDK for custom subsegments. X-Ray scales automatically, integrates with CloudWatch dashboards, and provides near real-time observability. This solution satisfies all requirements: serverless integration, minimal code changes, latency visualization, and end-to-end tracing.

B) CloudWatch Logs Insights allows querying logs for latency and errors. Manual correlation is possible but requires significant effort, is error-prone, and does not provide automated service maps or bottleneck detection. It is impractical for production-scale tracing.

C) Deploying OpenTelemetry on EC2 introduces operational overhead. Each service requires instrumentation, and collectors must be deployed, scaled, and maintained. OpenTelemetry does not integrate natively with serverless AWS services, making it unsuitable for minimal-code solutions.

D) Implementing manual correlation IDs requires pervasive code changes. While helpful for debugging, it does not provide automated visualization, service maps, or bottleneck detection, and maintaining correlation across multiple services is error-prone.

Why the correct answer is A): AWS X-Ray provides automated, serverless, end-to-end tracing with latency visualization and bottleneck detection while requiring minimal code changes. Other options require manual effort, additional infrastructure, or cannot provide automated observability.

Question 156

A company runs multiple microservices on Amazon ECS Fargate and needs safe, automated canary deployments with traffic shifting, monitoring, and automatic rollback. Which solution is best?

A) ECS rolling updates with custom health check grace periods.

B) AWS CodeDeploy blue/green deployments integrated with ECS and ALB.

C) CloudFormation stack updates with rollback enabled.

D) ALB slow start mode for gradual traffic ramp-up.

Answer: B)

Explanation

A) ECS rolling updates replace tasks gradually to maintain service availability, and health check grace periods prevent slow-starting containers from being marked unhealthy prematurely. While this approach helps maintain baseline service availability, ECS rolling updates do not provide automatic rollback based on application-level metrics. Tasks are replaced incrementally at the ECS service level, but there is no built-in mechanism to shift traffic based on error rates, latency, or other runtime indicators. Consequently, rolling updates alone cannot guarantee a safe canary deployment, as failures in the new version could impact production before manual intervention occurs. This method is primarily useful for basic deployments but lacks full automation, monitoring, and rollback capabilities required for mission-critical microservices.

B) AWS CodeDeploy blue/green deployments provides a fully managed deployment orchestration integrated with ECS and ALB. In this model, a new target group is created for updated services, and traffic can be shifted incrementally to the new version according to a defined schedule or percentage. ALB health checks and CloudWatch metrics monitor the performance of new tasks, and if errors occur, traffic is automatically rolled back to the previous stable version. CodeDeploy supports canary and linear strategies, offering precise control over rollout, monitoring, and automated rollback, reducing risk during production deployments. This solution is ECS-native, requires minimal operational effort, and ensures safe, automated, and monitored deployments, making it the best choice.

C) CloudFormation stack updates provide rollback for template-level failures during infrastructure updates. While CloudFormation can revert resources to a previous state if a deployment fails at the resource level, it does not account for runtime application health, traffic distribution, or automated rollback triggered by performance metrics. As a result, it cannot provide a safe canary deployment for ECS microservices at the application level.

D) ALB slow start mode gradually ramps traffic to newly registered targets to prevent overload. While this mitigates sudden traffic spikes, it does not orchestrate deployments, monitor application-level performance, or automatically rollback failed updates. Slow start is only a traffic ramp-up feature and cannot manage deployments end-to-end.

Why the correct answer is B): AWS CodeDeploy blue/green deployments provide comprehensive canary deployment capabilities, including incremental traffic shifting, monitoring, and automatic rollback. Other options address only partial aspects of deployment management and do not guarantee safe production rollouts.

Question 157

A company stores high-volume logs in Amazon S3 and requires a serverless solution to extract structured fields, index data, and enable fast search queries. Which solution is best?

A) Deploy ELK stack on EC2.

B) Use S3 Select for querying logs.

C) Amazon OpenSearch Serverless with S3 ingestion pipelines.

D) Store logs in DynamoDB with Global Secondary Indexes.

Answer: C)

Explanation

A) Deploying an ELK stack on EC2 provides advanced log analytics with Kibana dashboards, aggregations, and full-text search. However, this approach requires manual provisioning, patching, scaling, and monitoring of EC2 instances, violating the serverless requirement. Managing EC2 for high-volume logs adds operational complexity, including capacity planning and updates, making it unsuitable for serverless, fully managed log analytics.

B) S3 Select allows SQL-like queries on individual S3 objects. While convenient for small, ad hoc queries, it cannot index multiple objects or perform scalable full-text search across large datasets. S3 Select also lacks aggregation, automated field extraction, and visualization capabilities, making it impractical for production-grade log analysis.

C) Amazon OpenSearch Serverless is a fully managed, serverless solution for log analytics. It integrates with S3 ingestion pipelines to automatically index incoming logs. Features include structured field extraction, full-text search, aggregations, and near real-time queries. OpenSearch Serverless scales automatically, requires no server management, and integrates with monitoring and alerting tools such as CloudWatch. This solution satisfies all requirements: serverless operation, automated indexing, fast query performance, and minimal operational burden.

D) DynamoDB with Global Secondary Indexes provides fast lookups for structured data. However, it cannot efficiently handle unstructured logs or full-text search. Implementing log analytics using DynamoDB would require additional infrastructure, such as OpenSearch, increasing complexity and operational cost.

Why the correct answer is C): OpenSearch Serverless provides a scalable, serverless, fully managed solution for log analytics with automated indexing and fast search. Other options either require servers or cannot provide full-text search at scale.

Question 158

A DevOps team wants to reduce AWS Lambda cold start latency for high-traffic functions while controlling costs for low-traffic functions. Which solution is best?

A) Enable Provisioned Concurrency for high-traffic functions.

B) Increase memory allocation for all Lambda functions.

C) Deploy Lambda functions in a VPC.

D) Replace Lambda with ECS Fargate tasks.

Answer: A)

Explanation

A) Provisioned Concurrency pre-warms Lambda execution environments, eliminating cold starts. By applying it selectively to high-traffic functions, latency-sensitive endpoints remain responsive while low-traffic functions stay on-demand, controlling costs. This is a serverless-native, cost-efficient solution that optimizes performance without increasing operational complexity. Provisioned Concurrency can also scale based on traffic patterns, ensuring consistent performance for critical functions.

B) Increasing memory allocation improves CPU and initialization speed, which may slightly reduce cold start latency. However, it does not eliminate cold starts and increases costs for all functions, including low-traffic ones. Compared to selective Provisioned Concurrency, this approach is less precise and more expensive.

C) Deploying Lambda in a VPC historically increased cold start latency due to ENI attachment overhead. While recent improvements have mitigated some of this latency, VPC deployment does not prevent cold starts and adds operational complexity, making it suboptimal.

D) Replacing Lambda with ECS Fargate tasks avoids cold starts since containers are long-lived. However, this introduces operational overhead, including task management, scaling, monitoring, and always-on costs. It also violates the minimal code-change requirement and is less efficient than using Lambda with Provisioned Concurrency.

Why the correct answer is A): Provisioned Concurrency selectively pre-warms high-traffic Lambda functions, reducing cold start latency while controlling costs. Other options either fail to remove cold starts or increase operational complexity and expense.

Question 159

A company requires pre-deployment enforcement of compliance policies on Terraform modules in CI/CD pipelines. Policies include mandatory tags, encryption, and resource restrictions. Violations must block deployment. Which solution is best?

A) AWS Config rules.

B) Sentinel policies in Terraform Cloud/Enterprise.

C) Git pre-commit hooks.

D) CloudFormation Guard.

Answer: B)

Explanation

A) AWS Config rules evaluate resources after deployment. While Config can detect noncompliance and trigger remediation, it cannot prevent Terraform modules from being applied. This makes Config reactive rather than preventive, failing the pre-deployment enforcement requirement.

B) Sentinel policies provide policy-as-code enforcement in Terraform Cloud/Enterprise. Policies are evaluated during terraform plan or terraform apply. Violations fail the run, automatically blocking deployment. Sentinel supports mandatory tags, encryption, and restricting resource types. Integrated into CI/CD pipelines, Sentinel ensures pre-deployment governance, preventing noncompliant resources from reaching production. It also allows fine-grained configuration for different environments, providing both flexibility and enforceable guardrails without adding operational overhead.

C) Git pre-commit hooks enforce rules locally before committing code. They are bypassable and cannot reliably integrate with automated CI/CD pipelines, making them insufficient for pre-deployment enforcement.

D) CloudFormation Guard validates CloudFormation templates. While effective for CloudFormation, it is not compatible with Terraform modules without converting templates, adding complexity. It cannot natively enforce policies in Terraform CI/CD pipelines.

Why the correct answer is B): Sentinel policies enforce pre-deployment compliance automatically, blocking noncompliant Terraform modules in CI/CD pipelines. Other options are reactive, bypassable, or incompatible.

Question 160

A company wants end-to-end distributed tracing for serverless APIs including API Gateway, Lambda, DynamoDB, and S3, with minimal code changes, latency visualization, and bottleneck detection. Which solution is best?

A) Enable AWS X-Ray active tracing.

B) Use CloudWatch Logs Insights for manual correlation.

C) Deploy OpenTelemetry on EC2 instances.

D) Implement manual correlation IDs in code.

Answer: A)

Explanation

A) AWS X-Ray active tracing provides fully managed distributed tracing for serverless applications. It automatically captures segments and subsegments for Lambda, API Gateway, DynamoDB, and S3. The service map visualizes latency, errors, and bottlenecks across all services. Minimal code changes are required—enable active tracing on Lambda functions and optionally use the X-Ray SDK for custom subsegments. X-Ray scales automatically, integrates with CloudWatch dashboards, and provides near real-time observability. This solution satisfies all requirements: serverless integration, minimal code changes, latency visualization, and end-to-end tracing.

B) CloudWatch Logs Insights allows querying logs for latency and errors. Manual correlation is possible but requires significant effort, is error-prone, and does not provide automated service maps or bottleneck detection. It is impractical for production-scale tracing.

C) Deploying OpenTelemetry on EC2 introduces operational overhead. Each service requires instrumentation, and collectors must be deployed, scaled, and maintained. OpenTelemetry does not integrate natively with serverless AWS services, making it unsuitable for minimal-code solutions.

D) Implementing manual correlation IDs requires pervasive code changes. While helpful for debugging, it does not provide automated visualization, service maps, or bottleneck detection, and maintaining correlation across multiple services is error-prone.

Why the correct answer is A): AWS X-Ray provides automated, serverless, end-to-end tracing with latency visualization and bottleneck detection while requiring minimal code changes. Other options require manual effort, additional infrastructure, or cannot provide automated observability.