Amazon AWS Certified DevOps Engineer – Professional DOP-C02 Exam Dumps and Practice Test Questions Set 10 Q181-200

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Question 181

A company runs multiple microservices on Amazon ECS Fargate and wants 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 ECS tasks gradually while keeping the service available. Health check grace periods prevent premature failure detection of slow-starting containers. This approach ensures that tasks are not killed during initialization and helps maintain basic availability. However, ECS rolling updates do not support automated rollback based on application-level performance metrics. There is also no mechanism to perform controlled incremental traffic shifting between old and new versions. Therefore, while it helps maintain uptime, it lacks the safety and observability required for complex canary deployments in production microservices environments. Operational staff would need to monitor performance manually and intervene in case of failures, which increases risk and response time.

B) AWS CodeDeploy blue/green deployments provides a fully managed solution for canary deployments integrated with ECS and ALB. In this model, a new target group is created for updated ECS tasks, while existing tasks continue to serve traffic. Traffic is gradually shifted from the old target group to the new one, either linearly or using a canary schedule. ALB health checks and CloudWatch metrics monitor the new tasks for errors or performance degradation. If a failure occurs, CodeDeploy automatically rolls back traffic to the previous version, ensuring minimal impact on end-users. This approach supports incremental rollout, continuous monitoring, and automatic rollback, making it the safest option for production deployments of microservices. It minimizes risk while providing flexibility in deployment strategies.

C) CloudFormation stack updates allow rollback if a resource creation or update fails. While this ensures infrastructure-level consistency, it cannot monitor application-level metrics or shift traffic incrementally between versions. CloudFormation rollbacks are only triggered by resource failures and do not account for runtime application errors or performance degradation, making it unsuitable for canary-style deployments that require dynamic monitoring and traffic management.

D) ALB slow start mode gradually increases traffic to new targets to prevent overload. While useful for preventing spikes on newly launched containers, it does not manage deployment lifecycle, traffic shifting between old and new versions, or automated rollback. It only addresses ramp-up behavior and does not provide a comprehensive deployment strategy.

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

Question 182

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 full-featured log analytics, including dashboards, aggregations, and full-text search. However, it requires manual provisioning, patching, scaling, and monitoring, which violates the serverless requirement. Managing EC2 instances for high-volume logs introduces operational overhead, including capacity planning and fault tolerance, making it unsuitable for a fully managed, serverless solution.

B) S3 Select enables 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. S3 Select also lacks aggregation, automated structured field extraction, and visualization capabilities, making it impractical for enterprise-scale log analytics.

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

D) DynamoDB with Global Secondary Indexes is designed for structured data lookups, but cannot efficiently handle unstructured logs or perform full-text search. Using DynamoDB for log analytics would require additional indexing and query infrastructure, increasing operational complexity and cost.

Why the correct answer is C): OpenSearch Serverless delivers a scalable, serverless, fully managed log analytics platform capable of fast search, automated indexing, and minimal operational overhead. Other options either require servers or lack full-text search capabilities at scale.

Question 183

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 start latency. By applying it selectively to high-traffic functions, latency-sensitive endpoints remain responsive while low-traffic functions remain on-demand, controlling costs. Provisioned Concurrency is serverless-native, cost-effective, and precise, ensuring consistent performance without introducing additional infrastructure. It also scales automatically with traffic patterns, making it ideal for production-critical Lambda workloads.

B) Increasing memory allocation improves CPU resources and may slightly reduce cold start time. However, it does not prevent cold starts completely and increases costs for all functions, including low-traffic ones, making it less efficient than selectively applying Provisioned Concurrency.

C) Deploying Lambda in a VPC historically increased cold start latency due to ENI attachment overhead. While recent improvements mitigate this, VPC deployment does not eliminate cold starts and adds configuration complexity, making it unsuitable for reducing latency in high-traffic functions.

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 principle, making it less efficient than using Provisioned Concurrency for Lambda.

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 cost.

Question 184

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 operate after deployment, detecting noncompliance and optionally triggering remediation. While useful for post-deployment governance, Config cannot prevent Terraform modules from being applied, making it reactive rather than proactive. This fails the requirement for pre-deployment enforcement.

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 restricted resource types. Integrated into CI/CD pipelines, Sentinel ensures pre-deployment governance, preventing noncompliant resources from reaching production. Fine-grained policy configuration allows flexibility across environments, providing operational safety without additional complexity.

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

D) CloudFormation Guard validates CloudFormation templates but is not compatible with Terraform modules without template conversion, adding operational overhead. It cannot enforce pre-deployment policies natively 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 185

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 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 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 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 useful 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 186

A company wants to automate ECS Fargate service deployments with rollback capabilities, ensuring minimal downtime and monitored traffic shifting. Which solution is best?

A) ECS rolling updates with custom health checks.

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

C) Manual ECS task replacement using scripts.

D) ALB weighted target groups without deployment automation.

Answer: B)

Explanation

A) ECS rolling updates replace tasks gradually, maintaining service availability. Health checks ensure tasks are healthy before traffic reaches them. While this reduces downtime, ECS rolling updates do not support automatic rollback triggered by application-level metrics. Traffic shifting is implicit during task replacement rather than controllable in percentages or schedules, making it less safe for complex deployment scenarios.

B) AWS CodeDeploy blue/green deployments provides a fully managed automated deployment mechanism for ECS with ALB integration. It creates a new target group for updated tasks and shifts traffic incrementally according to a predefined strategy (canary or linear). ALB health checks monitor the new deployment, and CodeDeploy automatically rolls back to the previous version if performance or error metrics degrade. This ensures minimal downtime, safe progressive rollout, and automated rollback, making it the optimal choice for automated, monitored deployments of ECS Fargate services.

C) Manual ECS task replacement using scripts can achieve basic rolling updates, but it introduces operational risk, lacks monitoring, and requires constant human intervention. Rollbacks are not automatic and depend on manual detection of failures. This approach does not scale well and is prone to human error, making it unsuitable for production.

D) ALB weighted target groups allow traffic to be split between different sets of targets. While useful for gradual traffic shifting, it does not provide deployment automation or rollback mechanisms. Manual updates or scripts are required to manage deployments, reducing safety and increasing operational complexity.

Why the correct answer is B): AWS CodeDeploy blue/green deployments provide fully automated, monitored ECS Fargate deployments with traffic shifting and automatic rollback. Other approaches either lack automation, monitoring, or rollback capabilities.

Question 187

A company stores application logs in Amazon S3 and requires serverless, scalable, and searchable analytics without managing infrastructure. Which solution is best?

A) Deploy ELK stack on EC2.

B) Use S3 Select queries.

C) Amazon OpenSearch Serverless with S3 ingestion.

D) Store logs in DynamoDB with indexes.

Answer: C)

Explanation

A) Deploying ELK on EC2 provides comprehensive search, aggregation, and visualization, but requires manual provisioning, patching, and scaling. Managing high-volume log ingestion at scale becomes operationally intensive, violating the serverless requirement. It also introduces infrastructure overhead, cost, and maintenance responsibilities.

B) S3 Select allows SQL-like queries on individual objects. While useful for quick queries, it cannot index multiple objects, perform full-text search across logs, or provide aggregated analytics. S3 Select is insufficient for enterprise-scale log analytics where performance, searchability, and scalability are required.

C) Amazon OpenSearch Serverless is a fully managed, serverless solution for log analytics. It automatically ingests S3 logs, indexes them for structured and full-text search, and provides near real-time query capabilities. It scales automatically, requires no server management, and integrates with CloudWatch for monitoring. OpenSearch Serverless meets all requirements: serverless operation, scalability, fast search, aggregation, and minimal operational overhead.

D) DynamoDB with indexes is designed for structured, low-latency lookups. It is not optimized for unstructured logs, full-text search, or complex aggregation. Implementing log analytics in DynamoDB would require additional indexing infrastructure, increasing complexity and cost.

Why the correct answer is C): OpenSearch Serverless delivers scalable, serverless, and fully managed log analytics, providing indexing, full-text search, and automated scaling. Other solutions either require infrastructure management or cannot meet search and analytics requirements.

Question 188

A DevOps team needs to reduce AWS Lambda cold start latency for critical, high-traffic functions while controlling cost for infrequent functions. Which solution is best?

A) Enable Provisioned Concurrency selectively.

B) Increase memory for all functions.

C) Deploy Lambda in a VPC.

D) Migrate functions to ECS Fargate.

Answer: A)

Explanation

A) Provisioned Concurrency pre-warms Lambda execution environments, effectively eliminating cold starts. By applying it selectively to high-traffic functions, latency-sensitive endpoints maintain performance while low-traffic functions remain on-demand, controlling costs. Provisioned Concurrency is serverless-native, cost-efficient, and scales automatically, providing consistent performance without extra operational overhead.

B) Increasing memory allocation may improve CPU resources and slightly reduce cold start times, but it does not prevent cold starts completely. It also increases cost for all functions, including infrequent ones, making it less efficient than selective Provisioned Concurrency.

C) Deploying Lambda in a VPC historically increased cold start latency due to ENI initialization overhead. While performance improvements exist, VPC deployment does not eliminate cold starts and adds operational complexity.

D) Migrating to ECS Fargate avoids cold starts because containers are long-lived. However, it introduces operational overhead including task management, scaling, monitoring, and always-on costs. It also violates minimal code-change requirements and adds complexity for serverless workloads.

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 do not eliminate cold starts or increase complexity and cost.

Question 189

A company requires pre-deployment policy enforcement for Terraform modules in CI/CD pipelines, including 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 post-deployment, detecting noncompliance and optionally triggering remediation. While Config helps maintain governance, it cannot prevent Terraform modules from being applied and is reactive rather than proactive. This violates the requirement for pre-deployment enforcement.

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 restricted resource types. Integrated with CI/CD pipelines, Sentinel ensures pre-deployment governance, preventing noncompliant resources from reaching production. Policies can be fine-tuned for different environments, ensuring flexibility without operational burden.

C) Git pre-commit hooks enforce code standards locally before commit. They are bypassable and do not guarantee enforcement in CI/CD pipelines, making them insufficient for pre-deployment compliance enforcement.

D) CloudFormation Guard validates CloudFormation templates. While effective for CloudFormation, it is not compatible with Terraform modules without template conversion, making it impractical for enforcing Terraform pre-deployment policies.

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 190

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—active tracing can be enabled on Lambda functions and optionally use the X-Ray SDK for custom subsegments. X-Ray scales automatically, integrates with CloudWatch, 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. This approach is impractical for large-scale production environments.

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

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, requiring minimal code changes. Other options require manual effort, infrastructure management, or lack automated observability.

Question 191

A company wants to deploy new versions of ECS Fargate services with zero downtime, automated rollback, and gradual traffic shifting. Which solution is most suitable?

A) ECS rolling updates with task replacement.

B) AWS CodeDeploy blue/green deployments with ALB integration.

C) Manual container updates with scripts.

D) ALB slow start mode with target weight adjustments.

Answer: B)

Explanation

A) ECS rolling updates gradually replace old tasks with new ones while maintaining service availability. Task replacements help reduce downtime, and health checks prevent unhealthy containers from receiving traffic. However, ECS rolling updates do not support automatic rollback based on application-level performance metrics. Traffic shifting is implicit, meaning tasks are simply replaced rather than traffic being progressively routed to the new version. This can increase risk if the new version has latent errors that do not cause container failures but impact application logic, as monitoring and manual intervention are required to detect and revert issues.

B) AWS CodeDeploy blue/green deployments provide a fully managed deployment solution integrated with ECS and ALB. In this approach, a new target group is created for the updated ECS tasks, while the old tasks continue to serve traffic. Traffic is shifted incrementally from the old target group to the new one according to a canary or linear schedule. ALB health checks and CloudWatch metrics monitor the health of the new deployment. If issues arise, CodeDeploy automatically rolls back traffic to the previous version. This ensures zero downtime, safe incremental traffic shifts, and automated rollback, making it ideal for production deployments requiring reliability and observability.

C) Manual container updates with scripts allow direct deployment of new container images. While possible, this method introduces high operational risk, lacks automated monitoring, and requires human intervention for rollback. It is error-prone and does not scale for enterprise environments, making it impractical for production-grade deployments.

D) ALB slow start mode gradually ramps traffic to new targets, which helps prevent overload. However, it does not provide deployment automation, traffic orchestration, or rollback mechanisms. While it can complement a deployment strategy, slow start alone is insufficient for safely deploying new ECS versions.

Why the correct answer is B): AWS CodeDeploy blue/green deployments provide fully automated, monitored, and safe ECS deployments with zero downtime and rollback capabilities. Other methods either lack automation, monitoring, or rollback, making them less suitable for production-critical services.

Question 192

A company wants to implement serverless log analytics for millions of events stored in Amazon S3, enabling fast search and aggregation. Which solution is best?

A) ELK stack deployed on EC2.

B) S3 Select queries.

C) Amazon OpenSearch Serverless with S3 ingestion pipelines.

D) DynamoDB with Global Secondary Indexes.

Answer: C)

Explanation

A) Deploying an ELK stack on EC2 enables full-featured log analytics with dashboards, aggregations, and search. However, it requires manual provisioning, scaling, patching, and monitoring. High-volume logs introduce infrastructure management overhead, violating the requirement for a serverless solution and increasing operational costs.

B) S3 Select enables SQL-like queries on individual S3 objects. While suitable for ad hoc queries, it cannot index or query large datasets efficiently, lacks full-text search, aggregation, and visualization. It is not scalable for enterprise-level log analytics and does not provide automated ingestion or indexing pipelines.

C) Amazon OpenSearch Serverless is a fully managed, serverless log analytics solution. It integrates with S3 ingestion pipelines, automatically indexing incoming logs. It supports structured field extraction, full-text search, near real-time querying, and aggregation. OpenSearch Serverless scales automatically and eliminates infrastructure management overhead, providing an ideal solution for analyzing millions of events stored in S3. It also integrates with CloudWatch for monitoring and alerting, making it suitable for production-grade serverless analytics.

D) DynamoDB with Global Secondary Indexes is optimized for structured data lookups, not unstructured logs. It cannot perform full-text search or aggregation efficiently and would require additional infrastructure for log analytics, increasing complexity and operational overhead.

Why the correct answer is C): OpenSearch Serverless provides a scalable, serverless, and fully managed platform for analyzing large volumes of log data with fast search, aggregation, and minimal operational overhead. Other options either require server management or cannot scale effectively for log analytics.

Question 193

A DevOps team needs to reduce AWS Lambda cold start latency for high-traffic APIs without affecting cost-sensitive low-traffic functions. Which approach is best?

A) Enable Provisioned Concurrency selectively.

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 starts. Applying it selectively to high-traffic, latency-sensitive functions ensures consistent performance while allowing low-traffic functions to remain on-demand, controlling costs. Provisioned Concurrency is serverless-native, scalable, and cost-effective, providing predictable performance without introducing additional infrastructure or operational overhead. It is the optimal approach for production APIs that require low latency.

B) Increasing memory allocation may slightly reduce cold start time by providing more CPU resources, but it does not prevent cold starts entirely. It also increases cost for all functions, including those with low traffic, making it less efficient than selective Provisioned Concurrency.

C) Deploying Lambda in a VPC historically increased cold start latency due to ENI initialization overhead. Even with improvements, VPC deployment does not eliminate cold starts and adds network configuration complexity.

D) Replacing Lambda with ECS Fargate tasks avoids cold starts since containers are long-lived. However, it introduces operational overhead, including task management, scaling, monitoring, and persistent compute costs. This violates the serverless and minimal code-change principles.

Why the correct answer is A): Provisioned Concurrency selectively pre-warms high-traffic functions, reducing cold start latency while controlling cost, making it the most efficient and practical solution. Other options either fail to remove cold starts or increase complexity and cost.

Question 194

A company needs pre-deployment enforcement of compliance policies for Terraform modules, including mandatory tags, encryption, and restricted resource types, in CI/CD pipelines. Which solution is most suitable?

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 post-deployment, detecting noncompliance and optionally triggering remediation. While helpful for governance, Config cannot prevent Terraform modules from being applied, violating the pre-deployment enforcement requirement. It is reactive rather than proactive.

B) Sentinel policies provide policy-as-code enforcement integrated with Terraform Cloud/Enterprise. Policies are evaluated during terraform plan or terraform apply. Violations fail the run, automatically blocking deployment. Sentinel supports enforcement of mandatory tags, encryption, and restricted resource types. By integrating Sentinel into CI/CD pipelines, organizations can ensure pre-deployment governance, preventing noncompliant resources from reaching production. Policies are highly configurable, providing environment-specific enforcement without additional operational overhead.

C) Git pre-commit hooks enforce code standards locally before committing code. They are bypassable and do not provide reliable enforcement in automated CI/CD pipelines, making them inadequate for production-grade compliance enforcement.

D) CloudFormation Guard validates CloudFormation templates but does not support Terraform modules natively without conversion. Using it for Terraform introduces unnecessary complexity and operational overhead.

Why the correct answer is B): Sentinel policies provide automated pre-deployment compliance enforcement for Terraform, blocking noncompliant modules before they reach production. Other solutions are reactive, bypassable, or incompatible with Terraform.

Question 195

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

A) Enable AWS X-Ray active tracing.

B) Use CloudWatch Logs Insights for manual correlation.

C) Deploy OpenTelemetry on EC2.

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. X-Ray generates a service map showing latency, errors, and bottlenecks, providing automated visualization without manual correlation. Minimal code changes are required, as tracing can be enabled on Lambda functions, and the X-Ray SDK allows optional custom segments. X-Ray scales automatically, integrates with CloudWatch dashboards, and provides near real-time observability. This solution satisfies all requirements: serverless integration, minimal code changes, automated latency visualization, and end-to-end tracing.

B) CloudWatch Logs Insights allows manual querying of logs to identify latency and errors. While feasible, it requires manual correlation, is error-prone, and lacks automated service maps, making it unsuitable for production-scale observability.

C) Deploying OpenTelemetry on EC2 introduces operational overhead. Each service must be instrumented, and collectors require scaling, maintenance, and integration. It does not natively support serverless AWS services, making it more complex than X-Ray.

D) Implementing manual correlation IDs requires pervasive code changes across services. It is error-prone, does not provide automated service maps, and lacks visualization or bottleneck detection, making it impractical for large serverless architectures.

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

Question 196

A company wants to deploy new ECS Fargate microservices with zero downtime, automated rollback, and progressive traffic shifting. Which solution is best?

A) ECS rolling updates with health checks.

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

C) Manual ECS task replacement using scripts.

D) ALB slow start mode with weight adjustments.

Answer: B)

Explanation

A) ECS rolling updates gradually replace old tasks with new ones while maintaining availability. Health checks prevent unhealthy containers from receiving traffic, ensuring service continuity. However, ECS rolling updates cannot shift traffic incrementally between versions based on application metrics. Rollback is limited to infrastructure failures, and application-level errors may not trigger automated rollback. This increases risk during production deployments, particularly for microservices where small issues can propagate.

B) AWS CodeDeploy blue/green deployments provide fully managed, automated deployment for ECS services integrated with ALB. CodeDeploy creates a new target group for the updated ECS tasks, while the existing tasks continue to serve traffic. Traffic is shifted progressively according to a canary or linear schedule. ALB health checks and CloudWatch metrics monitor the new deployment’s health, and CodeDeploy automatically rolls back traffic if errors occur. This ensures zero downtime, controlled traffic shifting, and automated rollback, making it the optimal choice for production microservices.

C) Manual ECS task replacement using scripts is feasible but introduces operational risk, requires constant human monitoring, and lacks automated rollback. It is error-prone, slow, and does not scale well for enterprise production environments, making it less suitable.

D) ALB slow start mode gradually ramps traffic to new targets, preventing overload. However, it does not manage deployment, monitor application health, or perform rollback. It addresses traffic ramp-up but not the complete deployment lifecycle.

Why the correct answer is B): AWS CodeDeploy blue/green deployments provide fully automated, monitored ECS deployments with progressive traffic shifting and automatic rollback. Other methods lack either automation, monitoring, or rollback, increasing risk.

Question 197

A company wants a serverless solution for analyzing millions of S3-stored log events, providing structured extraction, fast search, and aggregation. Which solution is best?

A) Deploy ELK on EC2.

B) S3 Select queries.

C) Amazon OpenSearch Serverless with S3 ingestion pipelines.

D) DynamoDB with Global Secondary Indexes.

Answer: C)

Explanation

A) ELK on EC2 provides full analytics capabilities, dashboards, and aggregations. However, it requires manual provisioning, scaling, patching, and monitoring, which violates the serverless requirement. High-volume log ingestion creates operational overhead and infrastructure management, increasing cost and complexity.

B) S3 Select allows SQL-like queries on individual objects. While useful for small queries, it cannot index or query across multiple objects efficiently, lacks aggregation, and does not support full-text search. It is unsuitable for large-scale serverless log analytics.

C) Amazon OpenSearch Serverless is a fully managed serverless solution. It integrates with S3 ingestion pipelines to automatically index incoming logs. It supports structured field extraction, full-text search, aggregation, and near real-time querying. OpenSearch Serverless scales automatically, requires no server management, and integrates with CloudWatch for monitoring and alerting. This solution is ideal for production-grade log analytics, meeting the requirements for serverless, scalable, and fast analytics.

D) DynamoDB with Global Secondary Indexes is optimized for structured lookups but cannot perform full-text search or complex aggregation efficiently. Using it for log analytics would require additional indexing infrastructure, increasing complexity and cost.

Why the correct answer is C): OpenSearch Serverless provides scalable, serverless, fully managed log analytics, capable of indexing, search, and aggregation. Other solutions either require infrastructure management or cannot scale effectively.

Question 198

A DevOps team needs to reduce AWS Lambda cold start latency for high-traffic APIs without impacting low-traffic functions. Which approach is best?

A) Enable Provisioned Concurrency selectively.

B) Increase memory allocation for all functions.

C) Deploy Lambda in a VPC.

D) Replace Lambda with ECS Fargate tasks.

Answer: A)

Explanation

A) Provisioned Concurrency pre-warms Lambda execution environments, effectively eliminating cold starts. By selectively applying it to high-traffic functions, latency-sensitive APIs maintain responsiveness, while low-traffic functions remain on-demand, controlling costs. Provisioned Concurrency is serverless-native, scalable, and cost-efficient, providing predictable performance without additional infrastructure or operational overhead.

B) Increasing memory allocation provides more CPU resources, potentially reducing cold start times slightly. However, it does not prevent cold starts entirely and increases cost for all functions, including low-traffic ones, making it less efficient than selective Provisioned Concurrency.

C) Deploying Lambda in a VPC historically increased cold start latency due to ENI attachment. Even with recent improvements, it does not eliminate cold starts and adds network configuration complexity.

D) Migrating to ECS Fargate tasks avoids cold starts because containers are long-lived. However, it introduces operational overhead, including scaling, monitoring, and persistent compute costs. This violates serverless principles and increases complexity.

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

Question 199

A company requires pre-deployment compliance enforcement for Terraform modules in CI/CD pipelines, including mandatory tags, encryption, and restricted resources. 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 deployment, detecting noncompliance and optionally triggering remediation. While helpful for governance, Config cannot prevent Terraform modules from being applied, making it reactive. This does not satisfy pre-deployment 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 blocking deployment. Sentinel supports mandatory tags, encryption, and resource restrictions. Integrated into CI/CD pipelines, Sentinel ensures pre-deployment governance, preventing noncompliant resources from reaching production. Policies can be environment-specific, providing flexibility without operational overhead.

C) Git pre-commit hooks enforce code standards locally before commits. They are bypassable and do not guarantee enforcement in automated pipelines, making them insufficient for pre-deployment compliance enforcement.

D) CloudFormation Guard validates CloudFormation templates. While effective for CloudFormation, it is not compatible with Terraform modules without conversion, adding operational complexity and reducing usability.

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

Question 200

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

A) Enable AWS X-Ray active tracing.
B) Use CloudWatch Logs Insights for manual correlation.
C) Deploy OpenTelemetry on EC2.
D) Implement manual correlation IDs in code.

Answer: A) Enable AWS X-Ray active tracing.

Explanation:

A) AWS X-Ray active tracing provides fully managed distributed tracing specifically designed for modern serverless applications. It captures detailed segments and subsegments automatically across all supported AWS services, including Lambda, API Gateway, DynamoDB, and S3, without requiring significant code changes. Each incoming request generates a trace, and X-Ray automatically tracks the execution path through all the services involved. Segments represent the primary service processing a request (for example, a Lambda function invocation), while subsegments capture detailed operations within that service, such as DynamoDB queries, S3 object retrievals, or downstream HTTP calls.

One of X-Ray’s most powerful features is its service map, which provides a visual representation of the application architecture. Nodes represent services, and edges represent the interactions and requests between services. Latency for each segment and subsegment is color-coded, allowing teams to quickly identify slow components, errors, or faulted services. This visual map is invaluable in distributed systems, where identifying bottlenecks manually across serverless components would be extremely difficult and time-consuming. The service map also automatically highlights high-error paths, making debugging and performance optimization more efficient.

Another key advantage is minimal code modification. For Lambda functions, active tracing can be enabled directly in the Lambda configuration console or via infrastructure as code (IaC) tools such as AWS CloudFormation or Terraform. Optionally, the AWS X-Ray SDK can be added to Lambda functions to create custom subsegments and include business-specific metadata, annotations, or contextual information. This allows developers to trace critical application logic without extensive changes to existing code, maintaining developer productivity while adding full observability.

X-Ray is serverless-native, meaning it scales automatically with request volume and does not require dedicated infrastructure for collectors, storage, or aggregation. It integrates seamlessly with Amazon CloudWatch, allowing teams to create dashboards that combine metrics, logs, and traces. This integration provides near real-time observability and enables holistic monitoring, correlating system-level metrics like CPU usage or Lambda throttles with application-level traces.

In contrast, CloudWatch Logs Insights (Option B) is limited to querying logs for errors or latency. While it allows some level of manual correlation through request IDs, this method is labor-intensive, error-prone, and not scalable for production systems with large request volumes. It does not provide automatic service maps, subsegment analysis, or bottleneck detection. Using Logs Insights alone would require manually cross-referencing multiple services and logs, making it impractical for teams seeking automated end-to-end observability.

Option C, OpenTelemetry on EC2, introduces significant operational overhead. Each service must be instrumented, collectors deployed, and infrastructure maintained. OpenTelemetry does not natively integrate with serverless AWS services, so using it in a serverless context requires additional custom work. This increases both complexity and operational cost, while still not providing the same automated latency visualization, service maps, or bottleneck detection as X-Ray.

Option D, manual correlation IDs, requires developers to propagate unique identifiers through all services, passing them explicitly in requests or headers. While it can assist in debugging, this approach is error-prone, difficult to maintain, and labor-intensive, especially for asynchronous serverless architectures. Manual correlation cannot provide automated service maps or latency visualization, and it does not automatically detect bottlenecks. Maintaining consistency across multiple Lambda functions, API Gateway routes, DynamoDB calls, and S3 operations becomes a significant maintenance burden.

Why the correct answer is A:AWS X-Ray meets all requirements for distributed tracing in serverless architectures:

End-to-end tracing across API Gateway, Lambda, DynamoDB, and S3.

Automated capture of segments and subsegments for each request.

Service map visualization, highlighting latency, errors, and bottlenecks.

Minimal code changes, requiring only configuration changes and optional SDK integration for custom instrumentation.

Near real-time observability, with CloudWatch integration for combined metrics and trace visualization.

Scalable and serverless-native, handling large volumes of requests automatically without additional infrastructure.

Custom annotations and metadata, enabling application-specific monitoring and fine-grained tracing.

Rapid troubleshooting, allowing engineers to pinpoint slow services or high-error paths efficiently.

In practical terms, enabling X-Ray active tracing allows teams to immediately gain visibility into end-to-end request flows, understand performance bottlenecks, detect error hotspots, and optimize serverless API performance without major refactoring or operational overhead. By automating tracing and visualization, X-Ray eliminates the need for manual log correlation, reduces mean time to resolution (MTTR), and supports proactive performance monitoring.

Other solutions, while useful in specific scenarios, cannot meet the full set of requirements for modern serverless applications. CloudWatch Logs Insights lacks automatic visualization and is manual, OpenTelemetry requires additional infrastructure and does not natively support serverless services, and manual correlation IDs are tedious, error-prone, and unscalable.

In AWS X-Ray active tracing is the optimal solution because it provides automated, scalable, and serverless-compatible end-to-end distributed tracing with latency visualization, bottleneck detection, and minimal code changes, fully satisfying the company’s requirements.