Amazon AWS Certified DevOps Engineer – Professional DOP-C02 Exam Dumps and Practice Test Questions Set 3 Q 41- 60

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Question 41

A DevOps team is deploying microservices to Amazon EKS. They want to implement progressive deployments with automatic rollback if new pods fail health checks. Traffic should be shifted gradually, and the solution must integrate natively with Kubernetes manifests. What is the best solution?

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 a Kubernetes resource designed to protect applications from downtime during voluntary disruptions such as node maintenance or draining. PDBs define the minimum number of pods that must remain available during maintenance or scaling events. However, PDBs do not manage deployments, traffic shifting, or rollback. They ensure that a minimum number of pods remain available during disruptions but cannot control the rollout of new versions, detect failing pods automatically, or shift traffic gradually to new pods. Using PDBs alone does not satisfy the requirement for progressive deployment with rollback and traffic management.

B) Argo Rollouts is a Kubernetes controller specifically designed for progressive delivery. It supports canary deployments, blue/green deployments, traffic shifting, health checks, and automatic rollback based on metrics such as response codes or latency. With Argo Rollouts, you define the rollout strategy in Kubernetes manifests, and it integrates natively into existing deployment pipelines. Traffic can be gradually shifted using service mesh integrations like Istio, App Mesh, or NGINX ingress. Metrics from Prometheus or other monitoring systems can trigger rollback automatically if new pods fail health checks. This solution directly addresses the requirements: progressive rollout, traffic control, native Kubernetes integration, and automated rollback. It is purpose-built for EKS and modern microservice architectures.

C) ALB slow start mode is a feature that gradually increases the amount of traffic to new targets over a specified period to allow them to “warm up.” While this mitigates sudden spikes, it does not implement canary deployments, monitor pod health in the same integrated way as a rollout controller, or perform automatic rollback. Slow start only controls initial traffic distribution, and ALB cannot detect application-level failures in pods or revert traffic automatically. This option partially mitigates traffic issues but does not satisfy the requirement for automated rollback and progressive deployment control.

D) Kubernetes Horizontal Pod Autoscaler (HPA) adjusts the number of pods in a deployment based on metrics such as CPU, memory, or custom metrics. While HPA helps scale applications in response to load, it does not control deployment strategies, traffic shifting, or rollback behavior. HPA is focused on scaling, not deployment safety or gradual traffic management.

Why the correct answer is B): Argo Rollouts is specifically designed for Kubernetes to implement canary and blue/green deployments, gradual traffic shifting, and automated rollback. It natively integrates with Kubernetes manifests, leverages existing monitoring systems, and provides full control over deployment progress and failure response. Other options (PDBs, ALB slow start, HPA) address partial concerns but do not fulfill all requirements simultaneously.

Question 42

A company processes large volumes of logs in Amazon S3. They want serverless analytics that can extract fields, index data, and enable fast search queries without managing servers. Which solution meets this requirement?

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 involves manually provisioning Elasticsearch nodes, managing scaling, upgrades, high availability, and monitoring. Although ELK (Elasticsearch, Logstash, Kibana) can index and query logs effectively, it is not serverless, requires significant operational effort, and scales manually. The requirement explicitly states “serverless analytics,” which EC2-based ELK does not satisfy.

B) S3 Select allows querying data within individual S3 objects using SQL expressions. While useful for filtering or extracting data from objects, S3 Select does not provide full-text indexing or search across multiple objects. It also cannot automatically extract fields and index logs at scale for fast query performance. S3 Select is effective for ad hoc queries but does not meet the requirement for analytics with indexed search capabilities.

C) Amazon OpenSearch Serverless is a fully managed, serverless option for log analytics. It can ingest logs directly from S3 using ingestion pipelines, automatically extract fields, create indexes, and scale dynamically with demand. Queries are fast and provide analytics capabilities similar to traditional Elasticsearch clusters, but without server management overhead. This solution meets all requirements: serverless, scalable, automated field extraction, indexing, and search capability. It integrates natively with S3, supports automated pipelines, and adapts to workload spikes without user intervention.

D) DynamoDB is a key-value and document database optimized for low-latency reads and writes. While Global Secondary Indexes enable efficient query capabilities on structured attributes, DynamoDB is not suitable for full-text search or unstructured log data analytics. Implementing search on arbitrary log content would require additional components (like Elasticsearch), which violates the “serverless and fully managed” requirement. DynamoDB can store logs but is inadequate for analytics and search workloads described.

Why the correct answer is C): OpenSearch Serverless combines serverless scalability with field extraction, indexing, and search. It eliminates operational overhead while supporting fast log analytics directly from S3. Other solutions (EC2 ELK, S3 Select, DynamoDB) either require server management, do not provide full-text indexing, or are not suitable for large-scale log analytics.

Question 43

A company uses AWS Lambda for serverless APIs. Cold starts are causing inconsistent latency. The team wants minimal code changes, optimized cost, and reduced cold start latency for frequently used endpoints. What is the best solution?

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-initializes a specified number of Lambda execution environments so that they are ready to respond immediately to requests. For high-traffic functions, this eliminates cold starts, reduces latency, and can be configured dynamically. Only frequently invoked functions require Provisioned Concurrency, which optimizes cost because rarely accessed functions can remain on-demand. Minimal code changes are needed—mostly configuration in Lambda or infrastructure-as-code scripts. This is a serverless-native solution that directly addresses cold start latency.

B) Increasing memory allocation improves CPU resources and can reduce execution time. While this can indirectly reduce cold start duration because initialization might happen faster, it does not eliminate cold starts. The impact is partial and inconsistent. Higher memory allocations also increase costs for all invocations, not just high-traffic endpoints, making it less cost-effective than selective Provisioned Concurrency.

C) Enabling VPC for Lambda functions historically increased cold start latency due to ENI attachment overhead. Modern Hyperplane networking mitigates some of this, but it does not reduce cold starts inherently and can worsen latency. It is not a solution for reducing initialization delays for serverless functions.

D) Replacing Lambda with ECS Fargate avoids cold starts but introduces operational complexity. Fargate tasks require management, scaling configuration, and container orchestration. This contradicts the requirement for minimal code changes and serverless cost efficiency. While cold starts are avoided, this approach is heavier operationally and financially.

Why the correct answer is A): Provisioned Concurrency provides targeted cold start elimination, reduces latency for high-traffic endpoints, is cost-effective, and requires minimal operational effort. Other options either do not eliminate cold starts, are cost-inefficient, or increase operational complexity.

Question 44

A DevOps team wants to enforce that all IAM roles deployed via CloudFormation include mandatory tags. If a role does not meet the requirement, deployment must fail automatically. What is the best solution?

A) Use AWS Config rules to detect missing tags post-deployment.

B) Use IAM policies to block role creation without tags.

C) Use CloudFormation Guard rules in the CI/CD pipeline to validate templates.

D) Use AWS Trusted Advisor to identify untagged roles.

Answer: C)

Explanation

A) AWS Config evaluates deployed resources for compliance after they exist. While Config can trigger notifications or remediation for missing tags, it cannot prevent deployment of noncompliant resources. The requirement is pre-deployment enforcement, which Config does not provide. Post-deployment detection introduces potential risk windows where untagged roles exist in production.

B) IAM policies can restrict certain actions but cannot reliably enforce that tags are included during role creation. Using IAM conditions to enforce tags is possible but complex and error-prone. Additionally, this approach blocks API calls rather than providing template-level enforcement in CI/CD, which limits visibility and integration with deployment pipelines.

C) CloudFormation Guard (cfn-guard) provides policy-as-code enforcement for CloudFormation templates before deployment. Rules can specify required tags, allowed values, and other organizational policies. When integrated into CI/CD pipelines, templates that violate these rules fail validation automatically, preventing deployment. This ensures pre-deployment compliance, aligns with governance requirements, and is fully automated. It is declarative, scalable, and integrates natively with CloudFormation-based infrastructure pipelines.

D) AWS Trusted Advisor identifies untagged resources post-deployment. It provides recommendations but cannot enforce pre-deployment rules or fail deployments automatically. It is primarily a reporting and advisory tool.

Why the correct answer is C): CloudFormation Guard validates templates before resources are created, providing automated, pre-deployment enforcement of required tags. Other options either act after deployment, are manual, or are unreliable for automated CI/CD enforcement.

Question 45

A company wants to detect unauthorized or unexpected changes to compliance documents in S3. They require automated drift detection, version comparison, alerts, and a serverless solution. What is the best solution?

A) Enable S3 Versioning and compare versions manually.

B) Use AWS Glue to crawl and compare metadata.

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

D) Use CloudTrail object-level logging.

Answer: C)

Explanation

A) Manual comparison using S3 Versioning preserves previous versions but is not scalable for drift detection or alerting. It is labor-intensive and cannot automatically detect changes or notify stakeholders in real time. Manual processes introduce the risk of human error and delay, making them unsuitable for automated governance.

B) AWS Glue can crawl S3 buckets and extract metadata, but it does not analyze content changes or provide version comparison for drift detection. Glue is more appropriate for schema discovery and ETL, not monitoring compliance at the object-content level. Using Glue for this purpose adds unnecessary complexity and operational overhead.

C) EventBridge with S3 event notifications enables automated, serverless monitoring of S3 objects. When an object is created, modified, or deleted, S3 can trigger EventBridge events that invoke a Lambda function. Lambda can retrieve previous versions of the object using version IDs, perform content comparison, and detect unauthorized changes. If a change is detected, Lambda can send alerts via SNS, EventBridge, or other notification services. This approach is fully serverless, scalable, and automated, providing near real-time drift detection and content comparison. It also leverages native AWS services and integrates easily with existing compliance workflows.

D) CloudTrail logs API calls for S3 object operations. While it provides audit trails of who accessed or modified objects, it does not provide content comparison or automated alerts for unauthorized changes. CloudTrail is reactive and does not actively prevent or notify on content drift without additional Lambda processing. It is complementary but not sufficient on its own.

Why the correct answer is C): EventBridge-triggered Lambda with S3 Versioning provides a serverless, automated, scalable method to compare object versions, detect drift, and alert in real time. It meets all requirements for automated compliance monitoring, content-level analysis, and serverless operation, unlike manual comparison, Glue, or CloudTrail alone.

Question 46

A company uses multiple AWS Lambda functions to process high-throughput data streams from Amazon Kinesis. During spikes, Lambda invocations overwhelm downstream DynamoDB tables, causing throttling and occasional data loss. The team needs a serverless solution that preserves ordering per device, smooths spikes, and prevents throttling. Which solution is best?

A) Increase the Kinesis shard count.

B) Limit Lambda concurrency using reserved concurrency.

C) Insert an SQS FIFO queue between Kinesis and Lambda.

D) Implement retries with exponential backoff in Lambda code.

Answer: C)

Explanation

A) Increasing Kinesis shard count distributes incoming records across more shards, which may seem like a way to improve throughput. Each shard in Kinesis can be consumed independently by Lambda, increasing the number of concurrent Lambda invocations. While this increases raw processing capacity, it also increases Lambda concurrency, potentially overwhelming downstream services like DynamoDB even more. Moreover, while shards do maintain ordering within themselves, assigning device IDs to specific shards for ordering purposes is complex and not always feasible. Additionally, this approach does not provide a durable buffer to absorb spikes or handle backpressure effectively. It is an infrastructure-scaling solution rather than a traffic-smoothing or throttling mechanism.

B) Limiting Lambda concurrency via reserved concurrency settings can control the maximum number of concurrent invocations, which might prevent DynamoDB from being overloaded. However, this introduces backpressure on Kinesis. Messages that cannot be processed immediately remain in the shard iterator, causing increased iterator age and potential message expiration. There is also no guarantee of per-device ordering, and throttling may result in delayed or lost events if the system cannot process spikes efficiently. While concurrency limits provide partial control, they do not satisfy all requirements.

C) Inserting an SQS FIFO queue between Kinesis and Lambda is the most comprehensive solution. FIFO queues maintain strict ordering per message group, which can correspond to device IDs. Lambda can poll the queue with controlled concurrency, allowing predictable throughput to downstream DynamoDB tables. This approach absorbs spikes in a durable buffer and prevents throttling while ensuring ordering is preserved. Event-driven Lambda consumption also allows serverless scaling, and failures can be retried automatically with DLQs if needed. This design decouples ingestion from processing, providing a robust, serverless, and fault-tolerant solution that meets all stated requirements.

D) Retries with exponential backoff can slow down processing of failed requests but do not control concurrency or smooth traffic spikes. Lambda invocations will still surge during peak ingestion, potentially overwhelming DynamoDB. Backoff is reactive, not proactive, and does not preserve ordering or prevent data loss effectively. It is useful only for transient errors, not for managing high-throughput spikes.

Why the correct answer is C): SQS FIFO provides a serverless buffer that smooths traffic, preserves ordering, and prevents throttling. Other options either increase concurrency (A), limit concurrency without buffering (B), or react to errors without preventing them (D).

Question 47

A DevOps team wants to enforce organizational security policies on all Terraform modules deployed via CI/CD, including mandatory tagging, encryption enforcement, and prohibiting public resources. Violations must block deployment. What solution is best?

A) AWS Config rules.

B) Sentinel policies with Terraform Cloud/Enterprise.

C) Git pre-commit hooks.

D) CloudFormation Guard.

Answer: B)

Explanation

A) AWS Config rules can detect compliance issues after resources are deployed, providing notifications or automated remediation. However, they cannot block deployment before resources are provisioned. The requirement explicitly specifies pre-deployment enforcement in CI/CD, which AWS Config cannot achieve. Using Config alone is reactive, allowing noncompliant resources to exist temporarily, which violates the stated requirements.

B) Sentinel policies in Terraform Cloud/Enterprise provide policy-as-code that is evaluated during terraform plan or terraform apply. These policies can enforce mandatory tagging, encryption settings, and resource restrictions. Violations cause the Terraform run to fail, blocking deployment. Sentinel integrates with CI/CD pipelines, ensuring automated enforcement across environments and teams. It allows fine-grained logic for organizational compliance, supports modular policies, and scales across multiple Terraform workspaces. This solution meets the requirement for pre-deployment policy enforcement with automation, reliability, and centralized governance.

C) Git pre-commit hooks are limited to developer workstations and local commits. While they can check for some policy violations locally, they cannot enforce CI/CD compliance reliably because developers can bypass hooks or commit from other sources. They do not integrate centrally with Terraform plans in pipelines and cannot block actual resource provisioning. This method is insufficient for automated enforcement at scale.

D) CloudFormation Guard (cfn-guard) is designed to validate CloudFormation templates, not Terraform modules. It can enforce pre-deployment policies for CloudFormation but cannot validate Terraform plans, making it irrelevant for Terraform-based CI/CD pipelines. Using Guard would require converting Terraform to CloudFormation, which is impractical and operationally inefficient.

Why the correct answer is B): Sentinel policies proactively enforce Terraform compliance in CI/CD, blocking violations before deployment. Other options either act post-deployment (Config), rely on local enforcement (Git hooks), or are incompatible with Terraform (CloudFormation Guard).

Question 48

A company wants end-to-end distributed tracing for their serverless API, tracking requests from API Gateway through Lambda, DynamoDB, and S3. They require minimal code changes, serverless integration, and visualization of bottlenecks. What solution is best?

A) Enable AWS X-Ray active tracing.

B) Use CloudWatch Logs Insights to correlate logs manually.

C) Deploy OpenTelemetry on EC2 to collect traces.

D) Implement manual correlation IDs across services.

Answer: A)

Explanation

A) AWS X-Ray active tracing provides a fully managed solution for distributed tracing. Enabling X-Ray on Lambda and API Gateway automatically propagates trace headers to downstream AWS services like DynamoDB, S3, and other Lambda functions. It generates a service map, showing execution flow, latency, and error rates across all components. Minimal code changes are required, primarily enabling X-Ray in Lambda and optionally using the X-Ray SDK for custom segments. X-Ray automatically collects trace data, visualizes bottlenecks, and integrates with CloudWatch dashboards. It scales serverlessly with request volume and provides near-real-time observability without requiring additional infrastructure.

B) CloudWatch Logs Insights allows querying logs and correlating events manually. While it can help identify performance issues, it does not provide automatic distributed tracing or visual service maps. Manual correlation is error-prone, time-consuming, and unsuitable for high-throughput serverless applications.

C) OpenTelemetry on EC2 can collect traces but requires deploying and managing infrastructure. It does not integrate seamlessly with serverless AWS services like Lambda and API Gateway. Implementing OpenTelemetry introduces operational overhead, complexity, and ongoing maintenance, contradicting the requirement for minimal operational effort and serverless integration.

D) Manual correlation IDs across services require pervasive instrumentation and discipline. While they can help trace requests, they do not provide automated visualization, service maps, or integration with AWS-native monitoring. Maintaining and enforcing correlation IDs is error-prone and does not scale well for production workloads.

Why the correct answer is A): X-Ray automatically provides end-to-end tracing, visualization, and bottleneck analysis across AWS services with minimal code changes. Other options either require manual effort, infrastructure management, or are impractical at scale.

Question 49

A company wants to ensure that all IAM roles created via CloudFormation have required tags. Deployment must fail if tags are missing. Which solution is best?

A) Use AWS Config rules to detect missing tags after deployment.

B) Use IAM policies to block creation of untagged roles.

C) Use CloudFormation Guard rules in CI/CD pipelines to validate templates.

D) Use AWS Trusted Advisor to identify untagged roles.

Answer: C)

Explanation

A) AWS Config rules operate post-deployment. While they can detect untagged roles after creation and optionally remediate, they cannot block deployment. The requirement explicitly requires pre-deployment enforcement, which Config does not provide. Using Config in this context introduces a window where noncompliant roles may exist.

B) IAM policies can restrict actions but cannot reliably enforce that roles include specific tags during creation. Using IAM conditions to enforce tags is complex and can be bypassed. IAM policies cannot fail CloudFormation deployments directly, making this solution inadequate for CI/CD pre-deployment enforcement.

C) CloudFormation Guard (cfn-guard) allows policy-as-code enforcement for CloudFormation templates. Rules can specify mandatory tags and allowed values. When integrated into CI/CD pipelines, templates violating these rules fail validation, preventing deployment. Guard works at the template level, enforcing compliance before resources are created, which meets organizational governance requirements. It is fully automated, declarative, scalable, and natively integrates with CloudFormation and pipeline workflows.

D) AWS Trusted Advisor identifies untagged resources after they are deployed. It provides recommendations but cannot enforce tagging during deployment. Using Trusted Advisor would require manual remediation or additional automation to prevent deployment, which violates the requirement for automated pre-deployment enforcement.

Why the correct answer is C): CloudFormation Guard validates templates before deployment, ensuring required tags are present and automatically failing noncompliant deployments. Other options act after deployment, are unreliable, or cannot enforce template-level compliance.

Question 50

A company wants to automatically detect unauthorized changes to compliance documents in S3. Requirements include serverless operation, 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 object versions.

D) Use CloudTrail object-level logging.

Answer: C)

Explanation

A) Manual comparison with S3 Versioning preserves prior versions, but this process is labor-intensive, error-prone, and cannot scale for real-time monitoring. It does not provide automated alerts or drift detection, making it unsuitable for compliance requirements. Manual comparison also introduces delay and risk of human error, violating the need for automation.

B) AWS Glue can crawl S3 and collect metadata, but it does not compare object content for changes or detect unauthorized modifications. Glue is designed for schema discovery and ETL, not real-time drift detection. Using Glue would add unnecessary operational overhead without meeting the content-comparison requirement.

C) EventBridge with S3 notifications provides a fully serverless, automated solution. S3 can trigger EventBridge events whenever objects are created, modified, or deleted. A Lambda function can then retrieve previous object versions via version IDs, perform content comparison, and detect unauthorized or unexpected changes. If anomalies are detected, Lambda can send alerts through SNS or EventBridge. This architecture is fully scalable, serverless, and real-time, meeting all requirements: drift detection, version comparison, alerting, and automation without manual intervention.

D) CloudTrail object-level logging records API operations on S3 objects, including who accessed or modified them. While this provides auditability, it does not detect content differences, and alerts are not generated automatically. CloudTrail is reactive and does not analyze object versions directly. It is complementary but insufficient for the requirement of automated, real-time drift detection with version comparison.

Why the correct answer is C): EventBridge-triggered Lambda with S3 Versioning provides automated, serverless, and scalable detection of unauthorized changes, compares versions, and sends alerts in real time. Other solutions are manual, non-scalable, or reactive only.

Question 51

A DevOps team is deploying a new microservice to Amazon ECS on Fargate. During deployments, some tasks fail health checks because container initialization times vary. The team wants safe rollouts with configurable bake times, traffic shifting, monitoring, and automatic rollback. Which solution is best?

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

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

C) Use CloudFormation stack updates with automatic rollback.

D) Use ALB slow start mode to gradually warm new targets.

Answer: B)

Explanation

A) ECS rolling updates allow gradual replacement of tasks within a service. Adjusting the health check grace period can prevent new tasks from being marked unhealthy immediately during startup. However, rolling updates do not automatically rollback based on health failures, and they do not provide fine-grained traffic shifting. ECS rolling updates focus on replacing tasks and ensuring minimum availability, but they lack the ability to monitor application-level metrics or enforce a baked-in stabilization period where traffic is gradually shifted to new tasks. This partial mitigation does not fully satisfy the requirement for automatic rollback and controlled traffic management.

B) AWS CodeDeploy blue/green deployments for ECS provide a comprehensive solution. CodeDeploy creates a separate target group for the new version of the service and shifts traffic from the old group to the new one in configurable increments. Health monitoring is fully integrated, using CloudWatch alarms, ALB health checks, or custom metrics. If new tasks fail health checks during the bake period, traffic is automatically rolled back to the previous version, and new tasks are terminated. This approach guarantees safe rollouts, controlled traffic shifting, monitoring, configurable bake times, and automatic rollback. It integrates seamlessly with ECS, ALB, and CloudWatch, providing a fully managed, serverless deployment pipeline without manual intervention. CodeDeploy blue/green is designed specifically to reduce deployment risk and maintain availability during microservice rollouts.

C) CloudFormation stack updates provide rollback only for infrastructure-level failures, such as failed resource creation or parameter issues. They do not monitor runtime health or application-level failures of ECS tasks. While stack rollback protects against deployment errors, it cannot prevent unhealthy containers from serving traffic or automatically shift traffic between versions. CloudFormation rollback is reactive and does not satisfy the requirement for progressive deployment with bake times and health monitoring.

D) ALB slow start mode gradually ramps up traffic to new targets to avoid sudden load spikes. While this helps “warm up” containers, it does not provide rollback, bake-time control, or health-based monitoring. Slow start is a traffic pacing mechanism only, and it does not detect application-level failures or integrate with ECS deployment strategies. It partially mitigates the problem but cannot fully automate safe deployment.

Why the correct answer is B): CodeDeploy blue/green deployments for ECS provide automated traffic shifting, monitoring, configurable bake times, and rollback, ensuring safe deployments. Rolling updates, ALB slow start, or CloudFormation rollback address only partial aspects of the problem.

Question 52

A company is deploying serverless applications with AWS Lambda and wants to reduce cold start latency while optimizing cost. Some endpoints are frequently accessed, others rarely. Which solution is best?

A) Enable Provisioned Concurrency on high-traffic Lambda functions.

B) Increase memory allocation on all Lambda functions.

C) Enable VPC for Lambda functions.

D) Replace Lambda with ECS Fargate.

Answer: A)

Explanation

A) Provisioned Concurrency pre-initializes a fixed number of Lambda execution environments, eliminating cold starts for those functions. Applying it only to high-traffic endpoints ensures that latency-sensitive functions are always ready while keeping costs low, since rarely accessed functions can remain on-demand. Minimal code changes are needed; configuration is done via Lambda settings or infrastructure-as-code scripts. This approach directly addresses cold start latency and optimizes cost, providing a serverless-native solution.

B) Increasing memory allocation improves CPU and I/O throughput, which can indirectly reduce execution time. However, it does not eliminate cold starts and increases cost for all invocations, including low-traffic functions. It is an incomplete solution to the cold start problem, especially when selective optimization is desired.

C) Enabling VPC for Lambda historically increased cold start latency, as Elastic Network Interfaces (ENIs) must be attached. Even with Hyperplane improvements, VPC does not proactively eliminate cold starts and can complicate networking and initialization. It is not a solution for improving startup time.

D) Replacing Lambda with ECS Fargate avoids cold starts, but introduces operational complexity, including task definitions, container images, scaling policies, and deployment orchestration. This violates the requirement for minimal code changes and adds cost and management overhead, making it a less optimal solution.

Why the correct answer is A): Provisioned Concurrency targets high-traffic functions to eliminate cold starts, optimize latency, and control costs. Other options either do not eliminate cold starts, increase operational burden, or are inefficient.

Question 53

A company needs to enforce that all IAM roles created via CloudFormation have required tags. Deployments must fail if roles lack the tags. Which solution is best?

A) Use AWS Config rules to detect missing tags post-deployment.

B) Use IAM policies to block role creation without tags.

C) Use CloudFormation Guard in CI/CD pipelines to validate templates.

D) Use AWS Trusted Advisor to identify untagged roles.

Answer: C)

Explanation

A) AWS Config evaluates resources after deployment. It can detect untagged roles and optionally remediate, but it cannot block deployment. The requirement specifies pre-deployment enforcement, which Config does not provide. Using Config introduces a time window where noncompliant resources may exist.

B) IAM policies can restrict role creation actions with conditions on tags. However, enforcing specific tags through IAM policies is complex, error-prone, and does not integrate naturally with CloudFormation pipelines. Policies may block API calls but cannot guarantee template-level enforcement in CI/CD pipelines, making them insufficient for automated deployment enforcement.

C) CloudFormation Guard (cfn-guard) enforces policy-as-code on CloudFormation templates. Rules can specify mandatory tags, acceptable values, or resource attributes. When integrated into CI/CD pipelines, templates violating these rules fail validation, preventing deployment. This approach provides pre-deployment enforcement, automation, and scalability. It aligns with organizational governance by ensuring resources comply with tagging standards before creation, avoiding manual enforcement or reactive detection.

D) AWS Trusted Advisor provides recommendations post-deployment, highlighting untagged resources. It does not block deployment and cannot enforce template-level compliance automatically. Using Trusted Advisor alone does not satisfy the requirement for automated CI/CD enforcement.

Why the correct answer is C): CloudFormation Guard validates templates before deployment, ensuring required tags and compliance automatically. Config and Trusted Advisor are post-deployment tools; IAM policies are limited and complex for template enforcement.

Question 54

A company wants automated detection of unauthorized changes to compliance documents in S3. Requirements include drift detection, version comparison, real-time alerts, and serverless operation. 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 object versions.

D) Use CloudTrail object-level logging.

Answer: C)

Explanation

A) Manual comparison with S3 Versioning preserves previous versions but is labor-intensive, error-prone, and not scalable. It cannot detect unauthorized changes in real-time or provide automated alerts. Manual processes introduce delays and risk human error, failing the serverless automation requirement.

B) AWS Glue can crawl S3 to extract metadata but cannot compare object content. Glue is suited for schema discovery and ETL workflows, not for real-time compliance monitoring or content-level drift detection. Using Glue adds unnecessary complexity without solving the core problem.

C) EventBridge with S3 notifications provides a serverless, automated, and scalable solution. When an object is created, updated, or deleted, S3 triggers EventBridge events. A Lambda function can fetch previous versions using version IDs, compare contents, and detect unauthorized changes. Alerts can be sent via SNS or EventBridge if anomalies are detected. This architecture satisfies all requirements: drift detection, version comparison, real-time alerts, and serverless operation, with minimal operational overhead.

D) CloudTrail object-level logging records API calls on S3 objects, providing auditability. While it can show who modified an object, it cannot detect content differences or automatically alert on unauthorized changes. CloudTrail is reactive and insufficient for real-time, automated compliance monitoring.

Why the correct answer is C): EventBridge-triggered Lambda functions with S3 Versioning provide automated, serverless, scalable drift detection with version comparison and real-time alerts. Other options are manual, non-scalable, or reactive only.

Question 55

A DevOps team wants to implement end-to-end distributed tracing for serverless APIs. Requirements include minimal code changes, serverless integration, and visualization of latency and bottlenecks across API Gateway, Lambda, DynamoDB, and S3. Which solution is best?

A) Enable AWS X-Ray active tracing.

B) Use CloudWatch Logs Insights for manual log correlation.

C) Deploy OpenTelemetry on EC2 to collect traces.

D) Implement manual correlation IDs in the application.

Answer: A)

Explanation

A) AWS X-Ray active tracing automatically instruments API Gateway, Lambda, and other AWS services such as DynamoDB and S3. It captures segments and subsegments representing each service interaction and builds a service map showing latency, errors, and bottlenecks. Minimal code changes are required—just enabling active tracing in Lambda and optionally using the X-Ray SDK for custom instrumentation. It integrates seamlessly with CloudWatch dashboards, scales automatically, and requires no additional infrastructure, meeting all requirements for serverless end-to-end observability.

B) CloudWatch Logs Insights allows querying and analyzing logs, but does not provide automatic distributed tracing or visual service maps. Manual correlation of logs is error-prone and does not scale for high-throughput serverless applications. It lacks visualization of bottlenecks or latency between services.

C) Deploying OpenTelemetry on EC2 introduces operational overhead and requires manual instrumentation of all services. It does not integrate seamlessly with serverless AWS services such as Lambda and API Gateway. Managing collectors, scaling, and trace aggregation increases complexity and maintenance burden, violating the “serverless” and minimal code-change requirements.

D) Manual correlation IDs require pervasive instrumentation across all services. While they can assist debugging, they do not provide automated service maps, latency visualization, or integration with AWS-native monitoring. Maintaining and enforcing correlation IDs at scale is error-prone and cumbersome.

Why the correct answer is A): X-Ray provides automatic, serverless, end-to-end tracing, visualization, and latency analysis across AWS services with minimal code changes. Other options either require manual effort, additional infrastructure, or cannot provide automated end-to-end visibility.

Question 56

A DevOps team is deploying a microservice on Amazon ECS with Fargate. During rolling deployments, some tasks fail because container startup times vary. The team requires safe, automated rollouts with traffic shifting, health monitoring, and automatic rollback. 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 to new tasks.

Answer: B)

Explanation

A) ECS rolling updates replace old tasks with new ones gradually while ensuring a minimum number of healthy tasks remain. Adjusting the health check grace period prevents new tasks from being marked unhealthy immediately, but ECS rolling updates do not perform automatic rollback based on application-level failures. They also lack granular traffic shifting, monitoring, and bake-time controls. Rolling updates primarily focus on infrastructure replacement, so they address availability but not controlled, safe progressive deployment. While health checks help prevent immediate failures, this approach alone cannot guarantee safe deployment during variable container initialization.

B) AWS CodeDeploy blue/green deployments for ECS provide a fully managed solution for safe rollouts. A new target group is created for the new version, and traffic is shifted gradually from the old version to the new one according to configurable percentages or time intervals. CodeDeploy monitors health through ALB health checks or CloudWatch alarms. If failures are detected during the rollout, it automatically rolls back traffic to the previous version, terminating unhealthy tasks. This approach ensures safe deployments, monitoring, configurable bake times, gradual traffic shifting, and automated rollback. It integrates seamlessly with ECS and ALB, allowing for zero-downtime deployments. CodeDeploy blue/green is specifically designed to reduce risk during ECS deployments and provide predictable, repeatable, and automated rollout strategies.

C) CloudFormation stack updates provide rollback only for resource creation failures, not application-level health issues. While stack rollback is effective for infrastructure errors, it does not prevent unhealthy ECS tasks from serving traffic, nor does it manage traffic shifting or progressive deployment. CloudFormation rollback is reactive and limited to template-level failures, making it unsuitable for the requirement of safe, monitored, and automated ECS rollouts.

D) ALB slow start mode gradually ramps up traffic to new targets to allow warming of resources. While this prevents traffic spikes and helps with initial performance stabilization, it does not provide automated rollback, health monitoring, or integrated deployment control. Slow start simply adjusts the rate of request flow to targets and cannot ensure safe progressive deployment or rollback if application-level failures occur.

Why the correct answer is B): CodeDeploy blue/green for ECS provides all required features: traffic shifting, health monitoring, automatic rollback, and safe deployments. Rolling updates and slow start address only partial concerns, and CloudFormation rollback is limited to infrastructure failures.

Question 57

A company wants to reduce Lambda cold start latency for frequently accessed serverless APIs 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 functions with ECS Fargate tasks.

Answer: A)

Explanation

A) Provisioned Concurrency pre-initializes a specified number of Lambda execution environments so that requests do not experience cold start delays. It can be applied selectively to frequently invoked functions, while low-traffic functions continue to use on-demand execution, optimizing cost. This approach reduces latency for high-traffic endpoints while minimizing unnecessary spending on rarely used functions. Minimal configuration changes are required, making it an efficient serverless-native solution for cold start mitigation.

B) Increasing memory allocation improves CPU resources and can marginally reduce cold start duration. However, this is an indirect effect and does not eliminate cold starts. Additionally, memory increases raise costs for every invocation, regardless of traffic, making it less cost-effective than targeted Provisioned Concurrency.

C) Deploying Lambda in a VPC historically increased cold start latency due to ENI attachment overhead. Even with Hyperplane networking improvements, VPC does not proactively prevent cold starts and may actually worsen startup latency in some cases. It does not solve the cold start problem effectively.

D) Replacing Lambda with ECS Fargate avoids cold starts since containers are long-lived, but introduces operational complexity. Fargate tasks require task definitions, container images, scaling policies, and management overhead, which violates the requirement for minimal code changes. While it addresses cold start latency, it is less cost-efficient and operationally heavier than Provisioned Concurrency.

Why the correct answer is A): Provisioned Concurrency selectively eliminates cold starts, reducing latency for frequently used functions while optimizing costs for low-traffic functions. Other options are either indirect, costly, or introduce operational overhead.

Question 58

A company wants pre-deployment enforcement of organizational security policies on Terraform modules, including mandatory tagging, encryption, and resource restrictions. Violations must block deployment. Which solution is best?

A) AWS Config rules.

B) Sentinel policies with Terraform Cloud or Enterprise.

C) Git pre-commit hooks.

D) CloudFormation Guard.

Answer: B)

Explanation

A) AWS Config rules evaluate resource compliance after deployment. While Config can alert or remediate violations, it cannot prevent noncompliant Terraform modules from being applied. Using Config would leave a window where noncompliant resources exist, violating pre-deployment enforcement requirements.

B) Sentinel policies in Terraform Cloud/Enterprise provide policy-as-code that is evaluated during terraform plan or terraform apply. Policies can enforce tagging, encryption, resource restrictions, and other compliance requirements. Violations fail the Terraform run, blocking deployment automatically. Sentinel integrates with CI/CD pipelines to provide automated, repeatable enforcement at scale. It allows granular control, modular policies, and central governance. This proactive approach ensures that no noncompliant Terraform module is deployed, satisfying all stated requirements.

C) Git pre-commit hooks operate on local developer machines and are limited to local enforcement. Developers can bypass hooks, and they do not integrate directly with Terraform plan/apply in CI/CD pipelines. This makes them unsuitable for automated, pre-deployment compliance enforcement.

D) CloudFormation Guard is designed for CloudFormation templates, not Terraform modules. While it enforces policies at template level, it cannot evaluate Terraform plans, making it incompatible for this scenario.

Why the correct answer is B): Sentinel enforces compliance before deployment, integrates with CI/CD, and blocks noncompliant Terraform modules. Config and Guard act post-deployment or are incompatible, and Git hooks are unreliable.

Question 59

A company wants serverless, automated 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 with S3 Versioning preserves old versions, but is labor-intensive, error-prone, and not scalable. Manual processes cannot detect changes in real-time or trigger alerts automatically. This violates the requirement for automation and serverless operation.

B) AWS Glue can crawl S3 buckets and extract metadata, but it does not compare object contents for unauthorized changes. Glue is better suited for ETL and schema discovery, not compliance monitoring. Using Glue introduces unnecessary operational overhead without fulfilling content-level drift detection.

C) EventBridge with S3 notifications provides a serverless, automated, and scalable solution. S3 can trigger EventBridge events for object creation, update, or deletion. A Lambda function can retrieve previous object versions using version IDs, compare contents, and detect unauthorized changes. Alerts can be sent via SNS or EventBridge if anomalies are found. This fully satisfies the requirements: drift detection, version comparison, real-time alerts, and serverless operation. The solution decouples detection logic from storage, scales automatically, and requires minimal operational management.

D) CloudTrail object-level logging captures API calls, showing who modified or accessed objects. While useful for auditing, it does not compare object content, provide drift detection, or trigger automated alerts. CloudTrail is reactive and requires additional automation to meet the requirements.

Why the correct answer is C): EventBridge-triggered Lambda functions provide automated, serverless, scalable detection and alerting for unauthorized S3 changes. Manual comparison, Glue, and CloudTrail alone do not satisfy automation, real-time detection, or content-level comparison requirements.

Question 60

A DevOps team wants end-to-end distributed tracing for serverless APIs, tracking API Gateway, Lambda, DynamoDB, and S3 interactions. They require minimal code changes, visualization of latency, and bottleneck analysis. Which solution is best?

A) Enable AWS X-Ray active tracing.

B) Use CloudWatch Logs Insights for manual log correlation.

C) Deploy OpenTelemetry on EC2.

D) Implement manual correlation IDs in code.

Answer: A)

Explanation

A) AWS X-Ray active tracing provides fully managed distributed tracing for AWS services, including Lambda, API Gateway, DynamoDB, and S3. It automatically instruments requests, captures segments and subsegments, and builds a service map showing latency, errors, and bottlenecks. Minimal code changes are needed—primarily enabling active tracing on Lambda and optionally adding X-Ray SDK instrumentation for custom segments. X-Ray integrates with CloudWatch dashboards and scales serverlessly with request load. It enables engineers to identify performance bottlenecks, monitor latency end-to-end, and trace errors across serverless components. This solution meets all requirements: serverless, minimal code changes, visualization, and latency analysis.

B) CloudWatch Logs Insights allows querying logs and attempting manual correlation. While useful for ad hoc troubleshooting, it does not provide automatic distributed tracing, service maps, or latency analysis. Manual correlation is error-prone, time-consuming, and impractical at scale.

C) Deploying OpenTelemetry on EC2 introduces infrastructure overhead and requires manual instrumentation for all services. It does not integrate natively with Lambda and API Gateway, increasing operational complexity. Maintaining collectors, scaling, and data aggregation adds unnecessary effort, violating the minimal code change requirement.

D) Manual correlation IDs require pervasive instrumentation across services. While helpful for debugging, this approach does not provide automated service maps, visualization of latency, or integration with AWS-native observability services. It is labor-intensive and difficult to maintain for large-scale serverless applications.

Why the correct answer is A): AWS X-Ray provides automatic, serverless, end-to-end tracing, visualization, and bottleneck analysis with minimal code changes. Other options require manual effort, additional infrastructure, or are insufficient for automated end-to-end observability.