Amazon AWS Certified DevOps Engineer – Professional DOP-C02 Exam Dumps and Practice Test Questions Set 5 Q 81-100

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Question 81

A company wants to deploy multiple microservices on Amazon ECS Fargate using safe deployments. They require progressive traffic shifting, monitoring, and automatic rollback if health checks fail. Which solution is best?

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

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 ones while maintaining availability. Adjusting the health check grace period helps prevent marking slow-starting containers as unhealthy. However, rolling updates do not provide automatic rollback based on application-level failures, nor do they allow fine-grained, traffic-controlled progressive deployments. While rolling updates can help with service availability, they do not meet the full requirement for monitoring and controlled traffic shifting with rollback.

B) AWS CodeDeploy blue/green deployments is a fully managed solution for ECS services. It creates a new target group for the updated tasks and gradually shifts traffic from the old group to the new group based on configurable bake times and health monitoring. If the new tasks fail health checks, traffic is automatically reverted to the previous version, providing automatic rollback. ALB health checks ensure application-level validation, and metrics can integrate with CloudWatch alarms for monitoring. This approach guarantees safe deployments, progressive traffic control, and rollback without manual intervention, fully satisfying the requirements.

C) CloudFormation stack updates can rollback infrastructure-level changes when stack creation or update fails. However, CloudFormation rollback does not monitor application-level health checks nor manage traffic between old and new services. It is reactive to template errors, not runtime failures, so it does not fully meet the safe deployment requirement.

D) ALB slow start mode gradually increases traffic to new targets, which can prevent sudden load spikes. However, it does not implement blue/green deployments, monitor health metrics, or provide automatic rollback. Slow start modulates traffic but does not orchestrate deployment or ensure safety for new microservice versions.

Why the correct answer is B): CodeDeploy blue/green deployments provide controlled traffic shifting, health monitoring, bake times, and automatic rollback. Rolling updates, CloudFormation rollback, and ALB slow start only address partial concerns.

Question 82

A company wants to reduce AWS Lambda cold start latency for frequently accessed functions while keeping costs low for infrequently used functions. Which solution is best?

A) Enable Provisioned Concurrency for high-traffic Lambda 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-initializes Lambda execution environments, eliminating cold start latency for frequently invoked functions. By applying it selectively to high-traffic functions, latency is minimized while low-traffic functions remain on-demand, keeping costs under control. This solution is serverless-native, requires minimal configuration, and directly addresses both performance and cost optimization.

B) Increasing memory allocation provides additional CPU resources, which may slightly reduce initialization time. However, it does not remove cold starts, and higher memory increases cost for all invocations, including infrequently accessed functions. This approach is less targeted and cost-efficient than Provisioned Concurrency.

C) Deploying Lambda in a VPC historically increases cold start latency due to ENI initialization overhead. Even with Hyperplane networking improvements, this placement does not prevent cold starts and adds operational complexity.

D) Replacing Lambda with ECS Fargate tasks eliminates cold starts because containers are long-lived. However, it introduces operational overhead, including task definitions, scaling policies, monitoring, and deployment management. It also violates the minimal code-change requirement and increases cost.

Why the correct answer is A): Provisioned Concurrency selectively eliminates cold starts for high-traffic functions, optimizing latency and cost. Other options either fail to prevent cold starts or add operational complexity.

Question 83

A company wants to enforce pre-deployment compliance on all 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 evaluates compliance after resources are deployed. Config can detect violations and trigger alerts or remediation, but it cannot prevent Terraform modules from being applied. Config is reactive, and noncompliant resources could exist temporarily, violating 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 enforcement, encryption requirements, and restrictions on specific resource types. Integration with CI/CD pipelines ensures automated, centralized governance. This solution guarantees pre-deployment enforcement without manual intervention.

C) Git pre-commit hooks execute locally on developer machines and can be bypassed. They do not reliably enforce compliance in CI/CD pipelines and cannot prevent Terraform apply operations, making them unsuitable for automated enforcement.

D) CloudFormation Guard (cfn-guard) validates CloudFormation templates, not Terraform modules. It is incompatible with Terraform unless modules are converted to CloudFormation templates, which adds unnecessary complexity.

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

Question 84

A company wants automated, serverless detection of unauthorized changes to compliance documents in S3, including drift detection, version comparison, 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 preserves previous versions but is labor-intensive, error-prone, and cannot provide real-time alerts. Manual processes are not scalable, do not meet serverless requirements, and cannot reliably detect unauthorized changes across multiple files.

B) AWS Glue can crawl S3 and extract metadata, but it cannot perform content-level comparison or detect unauthorized changes automatically. Glue is designed for ETL and schema discovery rather than automated compliance monitoring, adding operational complexity without meeting the requirements.

C) EventBridge with S3 notifications provides a serverless, automated solution. S3 triggers events on object creation, update, or deletion. A Lambda function can retrieve previous versions via version IDs, compare content, detect unauthorized changes, and send alerts via SNS or EventBridge. This approach meets all requirements: automated drift detection, version comparison, real-time alerts, and serverless operation. It scales automatically, requires minimal operational effort, and integrates seamlessly with AWS services.

D) CloudTrail object-level logging records API activity but cannot detect content-level changes or automatically trigger alerts. CloudTrail is reactive and would require additional automation to meet compliance monitoring requirements.

Why the correct answer is C): EventBridge-triggered Lambda functions provide automated, serverless detection of unauthorized changes in S3, with version comparison and real-time alerting. Manual comparison, Glue, and CloudTrail alone cannot meet all requirements.

Question 85

A company wants end-to-end distributed tracing for serverless APIs covering API Gateway, Lambda, DynamoDB, and S3. They require minimal code changes, visualization of latency, 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 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, integrates with CloudWatch dashboards, and provides near real-time insights. This approach meets all requirements: serverless integration, minimal coding, visualization, and end-to-end tracing.

B) CloudWatch Logs Insights allows querying logs for latency and errors. Manual correlation of request IDs can be performed, but this is labor-intensive, error-prone, and does not provide automated service maps or bottleneck detection. It is impractical for production-scale tracing and fails the minimal code-change requirement.

C) Deploying OpenTelemetry on EC2 introduces operational overhead. Each service must be manually instrumented, and collectors must be managed, scaled, and maintained. OpenTelemetry does not integrate natively with serverless AWS services, adding complexity and violating minimal code-change requirements.

D) Implementing manual correlation IDs requires pervasive instrumentation across all services. While helpful for debugging, it does not provide automated service maps, visualization, or bottleneck analysis. 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 do not provide automated end-to-end observability.

Question 86

A company is deploying microservices to Amazon EKS and wants progressive deployments with automatic rollback if new pods fail health checks. Traffic should be gradually shifted, and the solution must integrate natively with Kubernetes manifests. 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) are Kubernetes resources designed to ensure minimum availability of pods during voluntary disruptions like node maintenance or scaling events. They prevent too many pods from being evicted simultaneously, ensuring high availability. However, PDBs do not manage deployments, traffic shifting, or rollback. They cannot detect failing pods during deployments or control traffic between old and new versions. While they are essential for operational stability, they do not address the progressive deployment requirements of the scenario.

B) Argo Rollouts is a Kubernetes-native controller for progressive delivery. It supports canary and blue/green deployment strategies, traffic shifting, and automated rollback. Traffic can be incrementally routed to new pods using service mesh integrations or ingress controllers, allowing fine-grained rollout control. Health checks, metrics, and thresholds can automatically trigger rollback if new pods fail. Because Argo Rollouts is Kubernetes-native, deployment strategies are defined declaratively in manifests, making it fully compatible with EKS environments. This solution satisfies all requirements: safe progressive deployment, traffic control, automated rollback, and integration with Kubernetes manifests.

C) ALB slow start mode gradually increases traffic to new targets to avoid sudden spikes. While this helps in stabilizing application load, it does not provide automatic rollback, progressive deployment strategies, or pod-level health validation. It only moderates traffic at the load balancer level and cannot ensure safe deployment of new microservice versions.

D) Horizontal Pod Autoscaler (HPA) adjusts pod counts based on metrics such as CPU, memory, or custom metrics. While useful for scaling, HPA does not control deployment strategy, traffic routing, or rollback. It only addresses performance scaling and does not guarantee safe rollout of new versions.

Why the correct answer is B): Argo Rollouts provides end-to-end support for progressive deployment strategies with traffic shaping and automated rollback. PDBs, ALB slow start, and HPA address only partial concerns, making them insufficient for safe deployment of EKS microservices.

Question 87

A company processes high-volume logs stored in Amazon S3. They want a serverless solution that can extract fields, index data, and provide fast search queries without managing servers. Which solution 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 allows indexing and searching logs, but it requires manual server provisioning, scaling, and management. ELK is not serverless and incurs operational overhead. Scaling for high-volume logs requires careful planning of nodes, memory, and storage, which conflicts with the serverless requirement.

B) S3 Select allows querying individual S3 objects using SQL expressions. While it is suitable for ad hoc filtering of single objects, it cannot index multiple objects or provide full-text search across large datasets. S3 Select does not meet the requirement for high-performance analytics on large log volumes.

C) Amazon OpenSearch Serverless provides a fully managed, serverless solution for log analytics. Logs can be ingested from S3 using ingestion pipelines, automatically indexed, and queried quickly. OpenSearch Serverless supports field extraction, indexing, and full-text search with near real-time performance. It scales automatically based on load, requires no server management, and integrates natively with S3. This solution satisfies all requirements: serverless operation, indexing, search capabilities, and S3 integration.

D) DynamoDB with Global Secondary Indexes is optimized for structured key-value or document data, providing low-latency access. However, it cannot provide full-text search or analytics across unstructured logs efficiently. Using DynamoDB for log analytics would require additional infrastructure for search, increasing complexity and operational cost.

Why the correct answer is C): OpenSearch Serverless delivers fully managed, serverless log analytics for S3, meeting the requirements for indexing, search, and integration. Other options require manual server management, lack full-text search, or are unsuitable for unstructured data.

Question 88

A DevOps team uses AWS Lambda for serverless APIs. Cold starts are causing inconsistent latency. They want minimal code changes, optimized cost, and reduced latency for frequently accessed endpoints. Which solution is best?

A) Enable Provisioned Concurrency for high-traffic functions.

B) Increase memory allocation for all Lambda functions.

C) Enable VPC for Lambda functions.

D) Replace Lambda with ECS Fargate.

Answer: A)

Explanation

A) Provisioned Concurrency pre-warms Lambda execution environments so that invocations do not experience cold start latency. Applying it to high-traffic functions reduces latency while low-traffic functions remain on-demand, controlling cost. Minimal configuration is needed, making it serverless-native and cost-efficient. It directly addresses cold start latency while optimizing for frequently accessed endpoints.

B) Increasing memory allocation provides more CPU and I/O, which can slightly reduce initialization time. However, it does not eliminate cold starts, and higher memory increases cost for all functions, including low-traffic endpoints. This solution is less targeted than Provisioned Concurrency.

C) Enabling a VPC for Lambda historically increases cold start latency due to ENI initialization overhead. While some improvements exist, VPC placement does not eliminate cold starts and adds operational complexity. It is counterproductive for the problem at hand.

D) Replacing Lambda with ECS Fargate avoids cold starts because containers are long-lived. However, this introduces operational overhead, including task definitions, scaling policies, monitoring, and deployment management. It also violates the minimal code-change requirement and increases cost.

Why the correct answer is A): Provisioned Concurrency selectively eliminates cold starts for high-traffic functions, reducing latency and optimizing cost. Other options either do not remove cold starts or increase operational complexity.

Question 89

A company wants to enforce pre-deployment compliance on Terraform modules deployed via CI/CD, including mandatory tags, encryption, and prohibiting specific 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. Config can detect violations and trigger alerts or remediation, but it cannot prevent Terraform modules from being deployed. Config is reactive, leaving temporary noncompliance, which violates the requirement for pre-deployment enforcement.

B) Sentinel policies provide pre-deployment enforcement in Terraform Cloud/Enterprise. Policies are evaluated during terraform plan or terraform apply. Violations automatically fail the run, blocking deployment. Sentinel supports tagging, encryption, and restrictions on resource types. Integration with CI/CD pipelines ensures automated governance and compliance. This solution fully satisfies the requirement for automated pre-deployment policy enforcement.

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

D) CloudFormation Guard (cfn-guard) validates CloudFormation templates, not Terraform modules. It is incompatible unless Terraform modules are converted, adding unnecessary complexity.

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

Question 90

A company wants automated, serverless detection of unauthorized changes to compliance documents stored in S3, including drift detection, version comparison, 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 preserves prior versions but is labor-intensive, error-prone, and cannot provide real-time alerts. This approach is not scalable and fails the requirement for automated, serverless operation.

B) AWS Glue can crawl S3 and extract metadata but cannot perform content comparison or detect unauthorized changes automatically. Glue is designed for ETL and schema discovery, not compliance monitoring.

C) EventBridge with S3 notifications provides a fully serverless, automated solution. S3 triggers events on object creation, update, or deletion. A Lambda function can retrieve previous versions via version IDs, compare content, detect unauthorized changes, and send alerts via SNS or EventBridge. This solution satisfies all requirements: automated drift detection, version comparison, real-time alerts, and serverless operation. It scales automatically, requires minimal operational effort, and integrates seamlessly with AWS services.

D) CloudTrail object-level logging records API calls on S3 objects for auditing but cannot detect content-level changes or trigger real-time alerts automatically. It is reactive and requires additional automation to meet compliance monitoring requirements.

Why the correct answer is C): EventBridge-triggered Lambda functions provide automated, serverless detection of unauthorized changes in S3 with version comparison and real-time alerting. Manual comparison, Glue, and CloudTrail alone cannot meet all requirements.

Question 91

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

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

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 are the default deployment strategy for ECS services. This approach gradually replaces old tasks with new ones while attempting to maintain service availability. Adjusting the health check grace period helps to avoid premature failure detection for new containers that may take longer to start. Despite these features, ECS rolling updates do not provide automated rollback triggered by application-level failures. Additionally, rolling updates cannot fully control traffic between old and new versions at a granular level; they primarily manage replacement of tasks in the cluster. While rolling updates improve availability and can handle minor task failures, they cannot guarantee safe, monitored deployments with full progressive traffic control or automatic rollback. For teams requiring robust deployment safety, this option is only partially effective.

B) AWS CodeDeploy blue/green deployments offers a fully managed solution for ECS services. CodeDeploy creates a separate target group for the new version of the service and gradually shifts traffic from the old version to the new version based on configurable bake times and health monitoring. If new tasks fail health checks, traffic is automatically routed back to the old version, and the new deployment is terminated. Integration with ALB health checks ensures application-level validation rather than just infrastructure-level checks. CloudWatch metrics or alarms can provide additional monitoring and trigger rollback if thresholds are breached. This approach ensures safe deployment by controlling traffic shifting, monitoring, and rollback without manual intervention. CodeDeploy blue/green deployments fully meet the requirement for a safe, automated ECS deployment strategy.

C) CloudFormation stack updates provide rollback for infrastructure-level issues, such as template errors or failure to create resources. However, CloudFormation rollback does not handle application-level failures such as unhealthy microservice tasks, nor does it provide fine-grained traffic control between old and new deployments. While useful for managing template consistency, relying solely on CloudFormation stack updates would not prevent user requests from reaching unhealthy services during deployment, making it insufficient for safe microservice rollouts.

D) ALB slow start mode gradually ramps up traffic to newly registered targets to prevent overload during initial startup. This feature reduces the chance of sudden traffic spikes overwhelming new tasks, but it does not implement automated rollback, canary deployments, or traffic shaping between old and new versions. Slow start mode operates at the load balancer level and cannot ensure safe application-level deployment. It is best used in conjunction with other deployment strategies rather than as a primary solution.

Why the correct answer is B): AWS CodeDeploy blue/green deployments provide complete support for safe microservice deployment in ECS: gradual traffic shifting, health monitoring, bake times, and automatic rollback. Rolling updates, CloudFormation rollback, and ALB slow start address only partial concerns and cannot guarantee application-level deployment safety.

Question 92

A company processes large volumes of logs in Amazon S3 and wants a serverless solution to extract fields, index data, and provide fast search queries without managing infrastructure. Which solution 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 provides full-featured log search and analytics with Kibana dashboards. While ELK is powerful, it requires manual server provisioning, scaling, and maintenance. High-volume log processing requires careful planning of storage, memory, and CPU resources to ensure indexing performance. Operational overhead increases significantly, making this approach inconsistent with the serverless requirement. Additionally, ELK on EC2 lacks automatic scaling to handle unpredictable log ingestion spikes without preconfigured infrastructure.

B) S3 Select allows querying individual S3 objects using SQL expressions. This is useful for ad hoc analysis of individual objects but does not provide indexing across multiple objects or full-text search. S3 Select cannot efficiently handle analytics for large-scale log datasets or support the rapid, search-friendly queries required for operational observability.

C) Amazon OpenSearch Serverless is a fully managed, serverless solution for log analytics. It enables ingestion of S3 logs through pipelines, automatically indexes the data, and allows fast search queries. OpenSearch Serverless supports field extraction, aggregation, and full-text search, delivering near real-time performance. It scales automatically based on query load and log ingestion volume, eliminating infrastructure management. The service integrates seamlessly with S3, providing a true serverless solution for log analytics. This approach satisfies all requirements: automated indexing, fast search, scalability, serverless operation, and integration with existing S3 log storage.

D) DynamoDB with Global Secondary Indexes provides low-latency access for structured data but cannot perform full-text search or analytics on unstructured log data efficiently. Using DynamoDB for large-scale log analytics would require additional components such as Elasticsearch or OpenSearch to provide search capabilities, adding unnecessary complexity and operational burden.

Why the correct answer is C): OpenSearch Serverless offers a serverless, fully managed, scalable solution for indexing and searching S3 logs. Other options require server management, do not provide full-text search, or cannot handle large-scale unstructured data efficiently.

Question 93

A company uses AWS Lambda for serverless APIs. Cold starts cause latency spikes for frequently invoked endpoints. Minimal code changes and cost optimization are required. 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 to avoid cold start latency. Applying it selectively to high-traffic functions ensures consistent, low-latency performance for frequently invoked endpoints while allowing low-traffic functions to remain on-demand, minimizing costs. This approach is serverless-native, requires minimal configuration, and directly addresses cold start issues, meeting all requirements without additional operational overhead.

B) Increasing memory allocation slightly boosts CPU and I/O resources, which can reduce execution time and initialization duration. However, it does not eliminate cold starts, and higher memory increases cost for all functions, including low-traffic endpoints, reducing cost efficiency.

C) Deploying Lambda in a VPC historically increases cold start latency due to ENI initialization overhead. While Hyperplane networking has improved this, VPC placement does not remove cold starts and introduces operational complexity, making it counterproductive for latency reduction.

D) Replacing Lambda with ECS Fargate tasks avoids cold starts because containers are long-lived. However, this introduces operational overhead, including container management, task definitions, scaling, monitoring, and deployment, while violating the minimal code-change requirement. Costs may also increase due to running long-lived containers, making it less efficient.

Why the correct answer is A): Provisioned Concurrency eliminates cold start latency selectively for high-traffic functions while maintaining cost efficiency and serverless operation. Other options either fail to remove cold starts, increase costs, or add complexity.

Question 94

A company wants to enforce pre-deployment compliance for Terraform modules in 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 evaluate compliance after resources are deployed. While they can detect noncompliance and trigger alerts or remediation, Config cannot prevent Terraform modules from being applied, leaving temporary noncompliant resources. Config is reactive and fails the pre-deployment enforcement requirement.

B) Sentinel policies provide policy-as-code enforcement within Terraform Cloud/Enterprise. Policies are evaluated during terraform plan or terraform apply. Violations fail the run, automatically blocking deployment. Sentinel supports tagging, encryption, and resource restrictions. Integration with CI/CD pipelines ensures centralized, automated governance. This approach fully meets the requirement for pre-deployment enforcement, eliminating noncompliant deployments before they reach production.

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

D) CloudFormation Guard (cfn-guard) validates CloudFormation templates, not Terraform modules. Without converting modules, it is incompatible and introduces unnecessary complexity, making it unsuitable.

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

Question 95

A company needs automated, serverless detection of unauthorized changes to compliance documents in S3, including drift detection, version comparison, 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 preserves prior versions but is labor-intensive, error-prone, and cannot provide real-time alerts. It does not scale for automated detection of unauthorized changes and violates the serverless requirement.

B) AWS Glue can crawl S3 and extract metadata, but it cannot compare object content or detect unauthorized changes automatically. Glue is optimized for ETL and schema discovery rather than real-time compliance monitoring, adding unnecessary complexity.

C) EventBridge with S3 notifications provides a fully serverless, automated solution. S3 emits events for object creation, updates, or deletions. A Lambda function can retrieve previous versions via version IDs, compare content, detect unauthorized changes, and send real-time alerts via SNS or EventBridge. This solution fulfills all requirements: automated drift detection, version comparison, real-time notifications, and serverless operation. It scales automatically, requires minimal operational effort, and integrates seamlessly with AWS services.

D) CloudTrail object-level logging captures API activity for auditing but cannot detect content-level changes or trigger automated real-time alerts. Additional automation would be required, increasing complexity.

Why the correct answer is C): EventBridge-triggered Lambda provides automated, serverless detection of unauthorized changes in S3, including version comparison and real-time alerting. Manual comparison, Glue, and CloudTrail alone cannot satisfy all requirements efficiently.

Question 96

A company is deploying multiple microservices to Amazon ECS on Fargate and wants safe, progressive deployments with automatic rollback if new tasks fail health checks. Traffic should be gradually shifted, and the solution must integrate natively with ECS. 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, attempting to maintain service availability. Adjusting the health check grace period can prevent slow-starting tasks from being marked as unhealthy prematurely. However, rolling updates cannot provide automated rollback based on application-level health checks and have limited support for controlled traffic shifting between old and new tasks. They manage task replacement at the ECS level but do not integrate with monitoring systems to enforce rollback when application failures occur. While rolling updates improve availability, they do not ensure fully safe deployments with traffic management and automatic rollback, which is a critical requirement for production microservices.

B) AWS CodeDeploy blue/green deployments is a fully managed solution for ECS. It creates a new target group for the updated ECS service and shifts traffic from the old service to the new service incrementally based on configurable bake times. Health monitoring is integrated with ALB and CloudWatch metrics, ensuring that unhealthy deployments are automatically rolled back to the previous version. CodeDeploy supports automatic rollback, progressive traffic shifting, and monitoring with minimal manual intervention. Its integration with ECS allows teams to define deployment strategies declaratively, providing a robust and reliable method for safe microservice rollouts. This approach addresses all requirements: progressive deployments, traffic control, automated rollback, and ECS-native integration.

C) CloudFormation stack updates provide rollback for template or infrastructure creation failures. While rollback is useful for handling resource-level errors, CloudFormation does not manage application-level health or traffic shifting. It cannot automatically revert deployments based on unhealthy ECS tasks or monitor metrics for safe deployment decisions. Using only CloudFormation stack updates would leave potential service downtime or failed deployments unaddressed.

D) ALB slow start mode gradually increases traffic to new targets to avoid sudden spikes. While this prevents load shock on new tasks, it does not implement progressive deployments, monitor health, or provide automatic rollback. Slow start helps with traffic ramp-up but does not manage deployment orchestration at the ECS task level.

Why the correct answer is B): AWS CodeDeploy blue/green deployments provide a fully managed, ECS-integrated solution with progressive traffic shifting, health monitoring, and automatic rollback. Rolling updates, CloudFormation rollback, and ALB slow start address only partial concerns, making them insufficient for safe microservice deployment.

Question 97

A company wants serverless, automated detection of unauthorized changes to compliance documents in S3. Requirements include drift detection, version comparison, 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 preserves previous versions of objects, allowing administrators to detect changes. However, manual comparison is labor-intensive, error-prone, and cannot provide real-time alerts. It does not scale to large document repositories and requires significant human effort to maintain compliance. Manual processes fail to meet the requirements for automated, serverless detection.

B) AWS Glue can crawl S3 and extract metadata or schema information. While Glue is suitable for ETL and analytics tasks, it cannot detect unauthorized changes at the content level automatically. Glue lacks native support for version comparison and real-time alerting, making it unsuitable for drift detection in compliance-critical workflows.

C) EventBridge with S3 notifications provides a fully serverless, automated solution. S3 emits events when objects are created, updated, or deleted. These events can trigger Lambda functions that retrieve previous versions of objects, compare content for unauthorized changes, and send real-time alerts through SNS or EventBridge. This solution meets all requirements: automated drift detection, content-level version comparison, real-time notifications, and serverless operation. It scales seamlessly, requires minimal operational effort, and integrates natively with AWS services. This approach ensures proactive compliance monitoring without manual intervention.

D) CloudTrail object-level logging captures API activity on S3 objects, providing audit trails for object operations. However, CloudTrail cannot detect content-level changes automatically or provide real-time alerts without additional automation. It is reactive rather than proactive, and using it alone would require building a custom detection and alerting mechanism, increasing operational complexity.

Why the correct answer is C): EventBridge-triggered Lambda functions provide a fully automated, serverless solution for detecting unauthorized changes in S3, including version comparison and real-time alerts. Manual S3 comparison, Glue, and CloudTrail alone cannot satisfy all requirements efficiently.

Question 98

A DevOps team wants to reduce AWS Lambda cold start latency for high-traffic functions while keeping costs low 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. By applying it selectively to high-traffic functions, latency is minimized for frequently used endpoints while low-traffic functions remain on-demand, reducing costs. This solution is serverless-native, requires minimal configuration, and directly addresses cold start latency without additional operational overhead. It provides an optimized balance between performance and cost efficiency.

B) Increasing memory allocation provides more CPU resources, which can slightly improve initialization speed. However, it does not eliminate cold starts, and higher memory allocation increases cost for all invocations, including low-traffic functions. This approach is less targeted and less efficient than Provisioned Concurrency.

C) Deploying Lambda in a VPC historically increases cold start latency due to ENI initialization. Even with improvements in Hyperplane networking, VPC placement does not remove cold starts and adds operational complexity. This approach is counterproductive for reducing latency.

D) Replacing Lambda with ECS Fargate tasks avoids cold starts because containers are long-lived. However, this introduces operational overhead, including task management, scaling, monitoring, and deployment, violating the requirement for minimal code changes. Costs may also increase due to always-on containers.

Why the correct answer is A): Provisioned Concurrency selectively eliminates cold starts for high-traffic functions while controlling costs for infrequently used functions. Other options either fail to remove cold starts, increase costs, or add complexity.

Question 99

A company wants pre-deployment enforcement of organizational policies on Terraform modules deployed via CI/CD. Policies include 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 resources are deployed. While they can detect noncompliant resources and trigger alerts or remediation, they cannot prevent Terraform modules from being applied. Config is reactive and cannot enforce pre-deployment compliance, leaving temporary noncompliance in the environment.

B) Sentinel policies provide policy-as-code enforcement within Terraform Cloud/Enterprise. Policies are evaluated during terraform plan or terraform apply. Violations fail the run, automatically blocking deployment. Sentinel supports tagging, encryption enforcement, and restrictions on specific resource types. Integration with CI/CD pipelines ensures automated, centralized governance. This approach meets the requirement for pre-deployment enforcement, ensuring that noncompliant modules never reach production.

C) Git pre-commit hooks enforce compliance rules locally on developer machines. However, they are bypassable and do not guarantee CI/CD compliance. They cannot prevent 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 templates, this tool is incompatible and introduces unnecessary complexity.

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

Question 100

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 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, integrates with CloudWatch dashboards, and provides near real-time insights into performance and errors. This solution meets all requirements: serverless integration, minimal coding, visualization, and end-to-end tracing across multiple AWS services.

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 the minimal code-change requirement.

C) Deploying OpenTelemetry on EC2 introduces operational overhead. Each service must be instrumented, and collectors must be deployed, scaled, and maintained. OpenTelemetry does not integrate natively with serverless AWS services, increasing complexity and violating minimal code-change requirements.

D) Implementing manual correlation IDs requires extensive code changes across services. While it can help with debugging, it does not provide automated service maps, visualization, 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 do not provide automated end-to-end observability.