Amazon AWS Certified DevOps Engineer – Professional DOP-C02 Exam Dumps and Practice Test Questions Set 6 Q 101-120

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Question 101

A company is running multiple microservices on Amazon EKS and wants progressive deployments with automatic rollback if new pods fail health checks. The solution must integrate natively with Kubernetes manifests and allow gradual traffic shifting. Which solution is best?

A) Use EKS managed node groups with PodDisruptionBudgets.

B) Use Argo Rollouts for canary and blue/green deployments.

C) Configure ALB slow start mode.

D) Use Kubernetes Horizontal Pod Autoscaler (HPA).

Answer: B)

Explanation

A) PodDisruptionBudgets (PDBs) ensure minimum availability of pods during voluntary disruptions such as node maintenance or scaling events. While they help maintain service availability, PDBs do not provide deployment orchestration, traffic control, or rollback capabilities. They are limited to eviction protection and cannot detect unhealthy pods during deployments or automatically revert failed updates. PDBs are important for stability but cannot achieve safe progressive deployments on their own.

B) Argo Rollouts is a Kubernetes-native controller that supports canary, blue/green, and progressive delivery strategies. It allows incremental traffic shifting between old and new versions using ingress controllers or service mesh integrations. Health checks and metrics thresholds can trigger automatic rollback if new pods fail or underperform. Argo Rollouts integrates directly with Kubernetes manifests, requiring minimal additional operational overhead while providing declarative control over deployment strategies. It fully satisfies the requirements: progressive deployment, traffic control, automated rollback, and native Kubernetes integration.

C) ALB slow start mode gradually increases traffic to new targets to avoid sudden spikes, but it does not orchestrate deployments, provide automated rollback, or monitor pod health. It only moderates traffic at the load balancer level, which is insufficient for progressive deployment of microservices.

D) Horizontal Pod Autoscaler (HPA) dynamically scales pod counts based on metrics such as CPU, memory, or custom metrics. While it ensures adequate capacity, HPA does not control deployment strategy, traffic routing, or rollback. It addresses performance scaling, not safe deployment orchestration.

Why the correct answer is B): Argo Rollouts provides native Kubernetes support for progressive delivery, traffic shaping, and automated rollback. PDBs, ALB slow start, and HPA address only partial concerns and cannot achieve safe deployment orchestration.

Question 102

A company processes high-volume logs stored in Amazon S3 and wants a serverless solution to extract fields, index data, and provide fast search queries. The solution should require no server management. Which is best?

A) Deploy an ELK stack on EC2.

B) Use S3 Select to query logs.

C) Use 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-featured log analytics and Kibana dashboards. However, it requires managing EC2 instances, scaling, and maintenance, which conflicts with the serverless requirement. High-volume log ingestion requires careful resource planning and ongoing monitoring, making it operationally intensive and not truly serverless.

B) S3 Select enables SQL queries against individual S3 objects. While it is useful for ad hoc queries, it cannot index multiple objects or provide full-text search across large datasets. It lacks analytics capabilities required for rapid log search and aggregation at scale.

C) Amazon OpenSearch Serverless provides a fully managed, serverless solution for log analytics. It integrates with S3 ingestion pipelines to automatically index logs, supports field extraction, full-text search, and near real-time query performance. OpenSearch Serverless scales automatically based on load, requires no server management, and provides operational dashboards for monitoring query performance and storage metrics. This approach meets all requirements: serverless operation, indexing, fast search queries, and seamless S3 integration.

D) DynamoDB with Global Secondary Indexes supports structured key-value queries efficiently. However, it cannot provide full-text search or analytics on unstructured logs. Implementing log analytics using DynamoDB would require additional infrastructure, increasing complexity and operational burden.

Why the correct answer is C): OpenSearch Serverless delivers fully managed, serverless log analytics for S3. Other options require manual server management, cannot perform full-text search efficiently, or are unsuitable for unstructured data.

Question 103

A company uses AWS Lambda for serverless APIs. High-traffic functions are experiencing cold start latency, and the company wants minimal code changes and cost optimization. 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.

Answer: A)

Explanation

A) Provisioned Concurrency pre-warms Lambda execution environments, ensuring consistent low-latency performance. Applying it selectively to high-traffic functions reduces cold start latency while allowing low-traffic functions to remain on-demand, minimizing cost. This approach is serverless-native and requires minimal configuration, meeting all requirements for latency reduction, cost optimization, and operational simplicity.

B) Increasing memory allocation slightly improves CPU resources and may reduce initialization time. However, it does not eliminate cold starts, and higher memory increases costs for all invocations, including low-traffic functions. This approach is less efficient and cost-effective than Provisioned Concurrency.

C) Deploying Lambda in a VPC historically increases cold start latency due to ENI initialization. While VPC networking improvements exist, placing Lambda in a VPC does not eliminate cold starts and adds operational complexity, making it counterproductive for latency reduction.

D) Replacing Lambda with ECS Fargate tasks eliminates cold starts because containers are long-lived. However, this adds operational overhead for task management, scaling, and monitoring. It also violates the requirement for minimal code changes and increases costs due to always-on containers.

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 prevent cold starts or introduce unnecessary complexity.

Question 104

A company wants pre-deployment enforcement of policies on Terraform modules deployed via CI/CD, including mandatory tags, encryption, and prohibited 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 compliance after deployment. While Config can detect noncompliance and trigger remediation, it cannot prevent Terraform modules from being applied. This reactive approach does not meet 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 tagging, encryption enforcement, and restricting specific resource types. Integration with CI/CD pipelines ensures centralized governance and automated pre-deployment compliance. This approach fully satisfies the requirement for automated pre-deployment enforcement, ensuring noncompliant resources never reach production.

C) Git pre-commit hooks run locally on developer machines but are bypassable. They cannot guarantee CI/CD enforcement and do not block Terraform apply operations, making them unreliable for automated pre-deployment compliance.

D) CloudFormation Guard (cfn-guard) validates CloudFormation templates, not Terraform modules. Without converting modules, it is incompatible, adding unnecessary complexity and operational burden.

Why the correct answer is B): Sentinel policies enforce pre-deployment compliance automatically in CI/CD pipelines. AWS Config is reactive, Git hooks are bypassable, and CloudFormation Guard is incompatible with Terraform.

Question 105

A company wants end-to-end distributed tracing for serverless APIs including API Gateway, Lambda, DynamoDB, and S3. They require 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. A service map visualizes latency, errors, and bottlenecks across 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 and integrates with CloudWatch dashboards, providing near real-time insights into performance and failures. This approach 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 of request IDs is possible, but this is labor-intensive, error-prone, and lacks automated service maps or bottleneck detection. It is impractical for large-scale production systems and violates the minimal code-change requirement.

C) Deploying OpenTelemetry on EC2 introduces significant operational overhead. Each service must be manually instrumented, and collectors must be deployed, scaled, and maintained. OpenTelemetry does not integrate natively with serverless AWS services, making it less suitable 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 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, additional infrastructure, or cannot provide automated end-to-end observability.

Question 106

A company runs multiple microservices on Amazon ECS Fargate and wants safe, progressive deployments with traffic shifting, monitoring, and automatic rollback if new tasks fail health checks. Which solution is best?

A) Use ECS rolling updates with a custom health check grace period.

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

C) Rely on CloudFormation stack updates with rollback enabled.

D) Use ALB slow start mode to gradually ramp traffic.

Answer: B)

Explanation

A) ECS rolling updates gradually replace old tasks with new tasks. Adjusting health check grace periods prevents slow-starting containers from being marked unhealthy too quickly. While this approach ensures basic availability during deployments, rolling updates cannot automatically rollback based on application-level failures or metrics. Traffic is managed at the task level, but there is no incremental traffic shifting mechanism between old and new services. As a result, rolling updates alone do not provide safe deployment orchestration for production microservices with high reliability requirements.

B) AWS CodeDeploy blue/green deployments is a fully managed deployment solution for ECS services. It creates a separate target group for the new version, allowing incremental traffic shifting from the old version. Health monitoring through ALB and CloudWatch metrics ensures that unhealthy deployments are automatically rolled back. Traffic shifting, bake times, and monitoring are all configurable, providing safe, progressive deployments with minimal manual intervention. CodeDeploy integrates natively with ECS and ALB, providing declarative deployment strategies that guarantee rollback and monitoring, making it ideal for high-availability microservices.

C) CloudFormation stack updates offer rollback for infrastructure-level failures, such as template errors or resource creation failures. However, CloudFormation does not handle application-level health monitoring or progressive traffic shifting. Using only CloudFormation for ECS deployment would not prevent users from being routed to unhealthy tasks, leaving a risk of downtime or failed deployments.

D) ALB slow start mode gradually increases traffic to newly registered targets to prevent sudden spikes. While useful for initial traffic ramp-up, it does not implement progressive deployments, monitor application health, or provide automatic rollback. Slow start is an auxiliary feature, not a primary deployment strategy.

Why the correct answer is B): AWS CodeDeploy blue/green deployments provide end-to-end safe deployment, including traffic shifting, health monitoring, and automatic rollback. Rolling updates, CloudFormation stack updates, and ALB slow start address only partial requirements and cannot ensure safe, production-ready deployments.

Question 107

A company wants serverless, automated detection of unauthorized changes to compliance documents stored in Amazon S3, including version comparison, drift detection, and real-time alerts. Which solution is best?

A) Enable S3 Versioning and manually compare versions.

B) Use AWS Glue to crawl and compare metadata.

C) Use EventBridge with S3 notifications triggering Lambda to compare versions.

D) Use CloudTrail object-level logging.

Answer: C)

Explanation

A) Manual comparison using S3 Versioning retains prior object versions. While this allows administrators to detect changes, it is manual, labor-intensive, and error-prone. Manual processes cannot scale to large document sets and cannot provide real-time alerts, making this approach impractical for automated compliance monitoring.

B) AWS Glue can crawl S3 and extract metadata or schema information. However, it cannot detect unauthorized changes at the content level automatically, nor does it provide real-time alerting. Glue is optimized for ETL and analytics tasks rather than drift detection, making it unsuitable for this use case.

C) EventBridge with S3 notifications provides a fully serverless, automated solution. S3 triggers events on object creation, modification, or deletion. A Lambda function can retrieve previous versions of objects, compare content for unauthorized changes, and send real-time alerts via SNS or EventBridge. This solution scales seamlessly, requires minimal operational effort, and integrates natively with AWS services. It provides automated drift detection, content-level comparison, and immediate notifications, fulfilling all requirements for compliance monitoring.

D) CloudTrail object-level logging captures API activity on S3 objects. While useful for auditing, it cannot detect content-level changes automatically or provide real-time alerts without additional automation. CloudTrail is reactive, and using it alone requires building a custom detection mechanism, increasing operational complexity.

Why the correct answer is C): EventBridge-triggered Lambda provides fully automated, serverless detection of unauthorized changes, including version comparison and real-time alerting. Manual comparison, Glue, and CloudTrail alone do not satisfy all requirements efficiently.

Question 108

A DevOps team wants to reduce AWS Lambda cold start latency for high-traffic functions while maintaining cost efficiency 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 execution environments for Lambda functions, ensuring that invocations do not experience cold start latency. Applying it selectively to high-traffic functions ensures low-latency performance for frequently invoked endpoints while allowing low-traffic functions to remain on-demand, controlling costs. This is a serverless-native, cost-optimized solution that requires minimal configuration and meets all requirements.

B) Increasing memory allocation slightly boosts CPU and initialization speed. However, it does not eliminate cold starts and increases cost for all invocations, including low-traffic functions, making it less efficient and cost-effective.

C) Deploying Lambda in a VPC historically increases cold start latency due to ENI initialization overhead. Even with improvements, VPC placement does not eliminate cold starts and adds operational complexity, making it counterproductive for latency reduction.

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

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 address cold starts effectively or introduce complexity and cost inefficiencies.

Question 109

A company wants pre-deployment enforcement of compliance policies on Terraform modules deployed via 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 compliance after resources are deployed. Config can detect noncompliance and trigger alerts or remediation, but it cannot prevent Terraform modules from being applied, leaving temporary noncompliant resources. Config is reactive, failing the pre-deployment 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 tagging, encryption enforcement, and restrictions on resource types. Integration with CI/CD pipelines ensures automated pre-deployment governance, guaranteeing that noncompliant modules never reach production.

C) Git pre-commit hooks enforce rules locally on developer machines, but they are bypassable and cannot guarantee CI/CD compliance. They do not block Terraform apply operations, making them unreliable for automated enforcement.

D) CloudFormation Guard (cfn-guard) validates CloudFormation templates, not Terraform modules. Without converting modules to CloudFormation, this tool is incompatible and adds operational complexity.

Why the correct answer is B): Sentinel policies enforce pre-deployment compliance automatically in CI/CD pipelines. AWS Config is reactive, Git hooks are bypassable, and CloudFormation Guard is incompatible with Terraform.

Question 110

A company wants end-to-end distributed tracing for serverless APIs including API Gateway, Lambda, DynamoDB, and S3. They require 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. A service map visualizes latency, errors, and bottlenecks. 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 and integrates with CloudWatch dashboards, providing 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 of request IDs is possible but labor-intensive, error-prone, and lacks automated service maps or bottleneck detection. It is impractical for large-scale production systems and does not meet minimal code-change requirements.

C) Deploying OpenTelemetry on EC2 introduces significant operational overhead. Each service must be instrumented, and collectors 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 extensive 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 error-prone and difficult to scale.

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, additional infrastructure, or cannot provide automated observability.

Question 111

A company is deploying microservices on Amazon ECS Fargate and wants automated canary deployments with traffic shifting, health monitoring, and rollback in case of failure. Which solution is best?

A) ECS rolling updates with health check grace period.

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 gradually replace old tasks with new ones while maintaining service availability. Configuring health check grace periods prevents slow-starting containers from being marked unhealthy too early. However, rolling updates do not provide automated rollback triggered by application-level failures and lack fine-grained traffic shifting. They replace tasks at the ECS level but cannot manage progressive traffic routing or integrate monitoring metrics to rollback failing deployments automatically. This method ensures basic availability but does not meet requirements for fully automated canary deployments.

B) AWS CodeDeploy blue/green deployments offers a fully managed deployment strategy for ECS. It creates a new target group for the updated service and allows progressive traffic shifting from the old version. Health monitoring through ALB and CloudWatch metrics ensures unhealthy deployments are automatically rolled back. Bake times, traffic weights, and rollback conditions can be configured to ensure safe deployment of microservices. CodeDeploy integrates natively with ECS and ALB, providing declarative deployment strategies with minimal operational overhead, making it the ideal solution for automated canary deployments with traffic management and rollback.

C) CloudFormation stack updates provide rollback for template-level failures during resource creation. While useful for infrastructure consistency, CloudFormation does not manage application-level health checks, traffic routing, or progressive deployments. It cannot ensure safe service rollout or automated rollback based on application performance, leaving users exposed to potential failures.

D) ALB slow start mode gradually ramps traffic to newly registered targets to avoid sudden spikes. While helpful for reducing initial load, it does not orchestrate deployments, monitor application health, or provide rollback. Slow start is only a traffic management feature, insufficient for full canary deployment management.

Why the correct answer is B): AWS CodeDeploy blue/green deployments provide fully automated canary deployments with traffic shifting, health monitoring, and automatic rollback. Rolling updates, CloudFormation, and ALB slow start address only parts of the problem and cannot meet full deployment requirements.

Question 112

A company stores logs in Amazon S3 and requires a serverless solution to extract structured fields, index, 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 full-featured log analytics, including Kibana dashboards and Elasticsearch indexing. However, it requires server provisioning, scaling, and maintenance, which violates the serverless requirement. Handling high log volumes demands careful planning of CPU, memory, and storage resources, along with operational overhead to maintain performance and availability.

B) S3 Select enables SQL-style queries on individual S3 objects. While useful for ad hoc data extraction, it cannot index multiple objects or perform fast full-text search across large datasets. S3 Select lacks the analytics and search capabilities needed for large-scale, production-grade log analysis.

C) Amazon OpenSearch Serverless is a fully managed, serverless solution for log analytics. It supports ingestion from S3 pipelines, automatic indexing, field extraction, full-text search, and near real-time queries. OpenSearch Serverless scales automatically based on traffic and data volume, requires no server management, and integrates seamlessly with other AWS services for monitoring and alerting. This solution fulfills all requirements: serverless operation, automated indexing, and fast search queries without operational burden.

D) DynamoDB with Global Secondary Indexes provides low-latency queries for structured data. However, it cannot perform full-text search or efficiently handle unstructured log data at scale. Using DynamoDB for log analytics would require additional components like OpenSearch, adding complexity and operational overhead.

Why the correct answer is C): OpenSearch Serverless provides a truly serverless, scalable, and fully managed solution for extracting, indexing, and searching S3 logs. Other options either require servers, cannot handle full-text search efficiently, or are unsuitable for unstructured data.

Question 113

A company 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.

Answer: A)

Explanation

A) Provisioned Concurrency pre-warms Lambda execution environments, ensuring that invocations avoid cold start latency. By selectively applying it to high-traffic functions, latency is minimized while allowing low-traffic functions to remain on-demand, reducing cost. This approach is serverless-native, requires minimal configuration, and directly addresses cold start latency without additional operational overhead.

B) Increasing memory allocation slightly increases CPU and I/O resources, reducing initialization time. However, it does not eliminate cold starts, and higher memory increases cost for all functions, including low-traffic ones. It is less efficient than Provisioned Concurrency for selective optimization.

C) Deploying Lambda in a VPC historically increases cold start latency due to ENI initialization. While improvements exist, VPC deployment does not remove cold starts and adds operational complexity, making it counterproductive.

D) Replacing Lambda with ECS Fargate tasks avoids cold starts because containers are long-lived. However, this adds operational overhead for task management, scaling, and monitoring. It violates the minimal code-change requirement and may increase costs due to always-on containers.

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

Question 114

A company wants pre-deployment enforcement of organizational policies on Terraform modules, including required 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 compliance after resources are deployed. While Config can detect noncompliance and trigger remediation, it cannot block Terraform deployments. This reactive approach does not meet the pre-deployment enforcement requirement.

B) Sentinel policies provide policy-as-code enforcement for Terraform Cloud/Enterprise. Policies are evaluated during terraform plan or terraform apply. Violations fail the run, automatically preventing deployment. Sentinel supports enforcement of tags, encryption, and allowed resource types. Integration with CI/CD pipelines ensures automated, centralized pre-deployment compliance, guaranteeing noncompliant modules do not reach production.

C) Git pre-commit hooks enforce rules locally, but they are bypassable and cannot guarantee CI/CD compliance. They do not block Terraform apply operations, making them unreliable for automated enforcement.

D) CloudFormation Guard validates CloudFormation templates, not Terraform modules. Without converting Terraform modules to CloudFormation, this solution is incompatible and adds unnecessary complexity.

Why the correct answer is B): Sentinel policies enforce pre-deployment compliance automatically in CI/CD pipelines, preventing noncompliant resources from being deployed. AWS Config is reactive, Git hooks are bypassable, and CloudFormation Guard is incompatible.

Question 115

A company wants end-to-end distributed tracing for serverless APIs including API Gateway, Lambda, DynamoDB, and S3. They require 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 needed—enabling active tracing on Lambda functions and optionally using the X-Ray SDK for custom subsegments. X-Ray scales automatically and integrates with CloudWatch dashboards, providing 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 of request IDs 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 does not satisfy minimal code-change requirements.

C) Deploying OpenTelemetry on EC2 introduces significant operational overhead. Each service must be instrumented, and collectors 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 across services. While helpful for debugging, it does not provide automated visualization, service maps, or bottleneck detection, and maintaining correlation across multiple services is error-prone and difficult to scale.

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, additional infrastructure, or cannot provide automated end-to-end observability.

Question 116

A company is deploying 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 a custom health check grace period.

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 old tasks gradually while attempting to maintain service availability. Adjusting health check grace periods ensures slow-starting containers are not prematurely marked unhealthy. However, rolling updates cannot automatically rollback based on application-level failures or metrics. Traffic is replaced at the task level, but there is no incremental control over traffic routing, and failure detection is limited. While rolling updates provide basic deployment safety, they cannot ensure fully automated canary deployments with progressive traffic control and rollback, making this option insufficient for production-grade deployment strategies.

B) AWS CodeDeploy blue/green deployments is a fully managed deployment service integrated with ECS and ALB. It allows the creation of a new target group for updated services and enables incremental traffic shifting from old to new tasks based on configurable weights. Health checks using ALB and CloudWatch metrics ensure that if new tasks are unhealthy, traffic can be automatically reverted to the previous version. The deployment is declarative and automated, allowing progressive deployment strategies, monitoring, and rollback without manual intervention. This solution satisfies all requirements: safe deployment, traffic shifting, health monitoring, and automatic rollback. It is ECS-native and minimizes operational overhead.

C) CloudFormation stack updates provide rollback capabilities for template-level failures during resource creation or modification. While useful for infrastructure consistency, CloudFormation does not manage application-level health checks, progressive traffic shifting, or rollback triggered by application performance. It is reactive for deployment errors at the resource level but does not provide safe, automated canary deployment orchestration.

D) ALB slow start mode ramps traffic gradually to new targets to avoid sudden spikes. While this mitigates initial load issues, it does not orchestrate deployments, monitor pod health, or trigger rollback. Slow start is a traffic moderation mechanism, insufficient for managing the full deployment lifecycle.

Why the correct answer is B): AWS CodeDeploy blue/green deployments provide fully automated canary deployment capabilities, including traffic shifting, health monitoring, and rollback. Rolling updates, CloudFormation, and ALB slow start address only partial aspects and cannot meet full deployment safety requirements.

Question 117

A company stores high-volume logs in Amazon S3 and requires a serverless solution to extract structured fields, index the 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 for full-featured log analytics and visualization via Kibana dashboards. However, this approach requires provisioning, scaling, and maintaining EC2 instances, which violates the serverless requirement. High-volume log ingestion introduces operational overhead for resource management, fault tolerance, and scalability, making it unsuitable for a fully serverless solution.

B) S3 Select allows querying individual S3 objects using SQL-like expressions. While useful for lightweight ad hoc queries, it cannot index multiple objects or provide fast, full-text search across large datasets. It is unsuitable for production-grade log analytics that requires structured field extraction, aggregation, and fast retrieval.

C) Amazon OpenSearch Serverless is a fully managed serverless solution that automatically indexes incoming log data from S3 ingestion pipelines. It supports structured field extraction, full-text search, aggregation, and near real-time queries. It scales automatically based on traffic and storage needs, requires no server management, and integrates with other AWS services for monitoring, alerting, and visualization. This solution meets all requirements for serverless, high-performance log analytics: automated indexing, fast search queries, and no operational burden.

D) DynamoDB with Global Secondary Indexes is designed for structured key-value access patterns. While it provides low-latency lookups, it cannot efficiently handle unstructured logs or full-text search, and implementing search capabilities would require additional infrastructure like OpenSearch. This adds complexity and operational overhead, violating the serverless simplicity requirement.

Why the correct answer is C): OpenSearch Serverless provides scalable, fully managed log analytics, automated indexing, and fast search capabilities for S3 logs. Other solutions either require manual server management, cannot efficiently search unstructured data, or do not scale seamlessly.

Question 118

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 eliminate cold starts. By applying it selectively to high-traffic functions, cold start latency is reduced for frequently invoked endpoints, while infrequently invoked functions remain on-demand, controlling cost. This is a serverless-native solution that requires minimal operational effort and configuration, ensuring fast execution without unnecessary expenses.

B) Increasing memory allocation slightly improves CPU and initialization speed. However, it does not eliminate cold starts entirely, and higher memory increases costs for all invocations, including low-traffic functions. This is not as cost-efficient or effective as selective Provisioned Concurrency.

C) Deploying Lambda in a VPC historically increased cold start latency due to ENI initialization overhead. While newer networking enhancements reduce this impact, VPC deployment does not fully prevent cold starts and adds operational complexity, making it counterproductive for latency optimization.

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, and monitoring. It also violates the requirement for minimal code changes and can increase costs due to always-on resources.

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

Question 119

A company requires pre-deployment enforcement of policies on Terraform modules deployed through CI/CD, including 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 resources are deployed. They can detect noncompliance and trigger remediation, but they cannot block Terraform deployments before resources are created. This reactive enforcement does not satisfy pre-deployment compliance requirements.

B) Sentinel policies provide policy-as-code enforcement for Terraform Cloud/Enterprise. Policies are evaluated during terraform plan or terraform apply. Violations fail the run, automatically preventing deployment. Sentinel supports tag enforcement, encryption requirements, and resource restrictions. Integration with CI/CD ensures automated, pre-deployment governance, guaranteeing that noncompliant resources never reach production. This fully meets the requirement for pre-deployment enforcement.

C) Git pre-commit hooks operate locally and can enforce some rules at the developer level. However, they are bypassable, do not integrate reliably with CI/CD pipelines, and cannot prevent Terraform applies, making them insufficient for automated enforcement.

D) CloudFormation Guard (cfn-guard) validates CloudFormation templates, not Terraform modules. Without converting modules, it is incompatible, and enforcing policies would require extra effort and operational complexity.

Why the correct answer is B): Sentinel policies enforce pre-deployment compliance automatically in CI/CD pipelines. AWS Config is reactive, Git hooks are bypassable, and CloudFormation Guard is incompatible with Terraform modules.

Question 120

A company wants end-to-end distributed tracing for serverless APIs including API Gateway, Lambda, DynamoDB, and S3. Requirements include 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 a fully managed, end-to-end distributed tracing solution specifically designed for serverless and microservices architectures. By enabling X-Ray active tracing on services like API Gateway, Lambda functions, DynamoDB, and S3, developers can automatically capture segments and subsegments representing each service invocation. Segments provide high-level overviews of service interactions, while subsegments capture granular details such as external HTTP calls, database queries, and downstream service calls. This automatic instrumentation greatly reduces the need for code modifications, making it ideal for serverless applications where minimal developer effort is desired.

One of X-Ray’s key advantages is the service map visualization, which presents a graphical representation of all services involved in handling requests. This map highlights latency contributions, error rates, and bottlenecks across the architecture, allowing developers to quickly identify underperforming services or abnormal response times. For example, if a Lambda function interacting with DynamoDB shows consistently higher latency, X-Ray highlights this in the service map, helping teams diagnose performance issues efficiently.

X-Ray integrates seamlessly with CloudWatch dashboards, allowing near real-time monitoring of traces, errors, and latencies. Traces include request IDs and metadata, making it easier to correlate requests across multiple serverless components. Furthermore, developers can optionally use the X-Ray SDK to create custom subsegments, add annotations, and include metadata for specialized monitoring needs without extensive refactoring of application code. This flexibility allows teams to instrument critical paths while leaving less critical parts untouched, maintaining minimal disruption to existing applications.

B) CloudWatch Logs Insights provides powerful querying capabilities over application logs, but it requires manual correlation of request IDs across services. This process is time-consuming, error-prone, and difficult to scale, particularly in serverless architectures where thousands of requests occur simultaneously. Additionally, CloudWatch Logs Insights does not provide automated service maps or bottleneck detection, and extracting meaningful latency information requires writing custom queries and correlating multiple logs. As a result, it fails to meet the requirements for minimal code changes, automated visualization, and end-to-end tracing.

C) OpenTelemetry on EC2 instances introduces significant operational overhead. Each service must be instrumented manually, and the tracing data must be collected, aggregated, and exported to a backend. OpenTelemetry can be highly effective in traditional server-based architectures but does not integrate natively with AWS serverless services like Lambda or API Gateway. Deploying collectors, managing scaling, and maintaining compatibility across multiple services adds complexity, violating the requirement for minimal code changes and automated tracing.

D) Manual correlation IDs require extensive changes to the application code to propagate identifiers across every service. While this approach can help with debugging, it does not provide automatic visualization, latency breakdowns, or bottleneck detection. Maintaining consistency across multiple services is difficult, especially in highly dynamic, serverless environments, and the approach is prone to human error.

Why the correct answer is A:AWS X-Ray provides a fully managed, serverless-compatible solution for end-to-end distributed tracing. It automatically instruments AWS services, captures detailed segments and subsegments, and produces service maps for latency visualization and bottleneck identification. The solution scales automatically, requires minimal code changes, integrates with CloudWatch dashboards, and allows optional SDK enhancements for custom monitoring. Compared to manual log correlation, OpenTelemetry on EC2, or custom correlation ID implementations, X-Ray is the most efficient, scalable, and practical approach for gaining comprehensive observability in serverless architectures.

By enabling X-Ray active tracing, organizations gain full-stack visibility across serverless APIs, improve performance monitoring, accelerate root-cause analysis, and maintain operational efficiency without introducing unnecessary infrastructure or operational complexity.