Amazon AWS Certified DevOps Engineer – Professional DOP-C02 Exam  Dumps and Practice Test Questions Set 1  Q 1 -20 

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Question 1

A company runs a microservices application on Amazon ECS (Fargate) behind an Application Load Balancer. They want to deploy a new version using a blue/green strategy that will (1) shift traffic gradually to the new version, (2) automatically roll back if error rates spike, and (3) minimize downtime. Which combination of AWS services and features will best meet these requirements?

A) Use ECS service with rolling update deployment controller, CloudWatch alarms for error rate, and an Automation runbook to perform manual rollback when alarm triggers.

B) Use ECS with CodeDeploy blue/green deployment (ECS integration), Application Load Balancer target group switching, CodeDeploy traffic shifting with automatic rollback on CloudWatch alarms.

C) Use two ECS services (blue and green), an external DNS change to switch weighted traffic between them, and a Lambda that monitors CloudWatch and updates DNS weights to rollback.

D) Use ECS with an Application Load Balancer and rely on ECS scheduled tasks to bring up the new version, then switch listener rules manually in the console if problems occur.

Answer: B)

Explanation

A) The rolling update deployment controller for Amazon ECS provides an in-place rolling deployment where tasks are replaced gradually. While it can minimize downtime, it is not a true blue/green deployment and does not provide built-in traffic shifting between distinct target groups. Relying on CloudWatch alarms plus a manual Automation runbook to perform rollback introduces human latency and risk: manual rollback is slower, can be error-prone, and does not satisfy the requirement for automatic rollback. In addition, automated rollback logic would need to be orchestrated outside the deployment pipeline, and rolling update cannot atomically switch traffic between entirely separate task sets in the same way blue/green does. This choice fails the automatic rollback/gradual traffic shift requirements.

B) CodeDeploy integrates with Amazon ECS to provide a blue/green deployment model for ECS services. With this integration, CodeDeploy creates separate target groups for the original (blue) and replacement (green) task sets, and the Application Load Balancer listener can be configured to route traffic according to the deployment’s traffic-shifting plan (e.g., canary or linear shifting). CodeDeploy supports specifying CloudWatch alarms and automatic rollback behavior so that if an alarm is triggered (e.g., error rate spike), CodeDeploy will automatically stop the deployment and roll back to the previous task set. This approach supports gradual traffic shifting, automated monitoring-based rollback, and minimal downtime because the old task set stays available until the deployment is deemed successful. Therefore, this combination aligns precisely with the three stated requirements.

C) Running two separate ECS services for blue and green and manipulating DNS weights is conceptually a blue/green style approach, but it has significant limitations. Using DNS weighted records to shift traffic is subject to DNS caching and propagation delays—clients and resolvers may cache the old IP address leading to inconsistent or delayed traffic switching. Moreover, ALB-level features like stickiness and connection draining are not leveraged. Implementing rollback using a Lambda that updates DNS requires building custom monitoring and rollback logic, which increases complexity and operational burden. Another problem is that with DNS-based switching there is no fine-grained control of listener-level routing, and you cannot use ALB health checks and connection draining as effectively. Thus, while technically possible, DNS weight switching is not the best practice for minimizing downtime and achieving reliable automatic rollback.

D) Using ECS with ALB and scheduled tasks to bring up a new version and then manually switching listener rules in the console is ad hoc and manual. Scheduled tasks are not designed for controlled deployments; they’re for time-based task runs. Manual listener rule changes are slow, error-prone, and cannot provide gradual traffic shifting or automated rollback. This approach fails the requirement for automation and minimal downtime because human intervention is required to redirect traffic and handle problems, and there’s no built-in monitoring-triggered rollback mechanism.

Why the correct answer is B): CodeDeploy’s ECS blue/green integration with ALB target groups gives you first-class, AWS-native support for separate task sets, controlled traffic shifting (linear/canary/other), integration with CloudWatch alarms, and built-in automatic rollback behavior. It leverages ALB target groups so existing connections can drain on the old tasks while the new ones receive traffic, providing near-zero downtime. The deployment pipeline can be fully automated—CodePipeline + CodeBuild + CodeDeploy or CI/CD tooling—so monitoring and rollback are immediate and deterministic. This pattern addresses all three requirements (gradual shifting, automatic rollback on increased errors, minimal downtime) in a supported, low-operational-overhead manner, making it the best overall choice.

Question 2

An AWS Lambda function processes messages from an SQS FIFO queue. Recently, the team observed duplicate processing even though the queue is FIFO and uses content-based deduplication. Which change will most reliably prevent duplicates when the Lambda polls messages from the queue?

A) Increase Lambda’s reserved concurrency to a higher value so multiple instances process messages concurrently but rely on the queue’s deduplication.

B) Configure the SQS FIFO message group ID properly so messages with the same ordering key are processed in order and ensure your function’s processing is idempotent.

C) Switch to a standard SQS queue because FIFO queues have higher delivery duplication risk with Lambda.

D) Use SQS visibility timeout and implement an external deduplication store (e.g., DynamoDB with message IDs) so that the function checks whether a message has been processed already before performing work.

Answer: D)

Explanation

A) Increasing Lambda’s reserved concurrency increases the number of function instances that can run simultaneously. That can improve throughput but does not prevent duplicates. SQS, including FIFO queues, can occasionally deliver messages more than once (at-least-once delivery semantics). Content-based deduplication on FIFO only deduplicates messages that are sent within the deduplication window and matches on content; when Lambda polls a message and the function fails or times out before deleting it, the message will become visible again and may be retried. Having more concurrent Lambda instances can increase the chance of overlapping visibility windows and duplicate processing. Therefore simply increasing concurrency won’t reliably solve duplicate processing.

B) Properly configuring the message group ID on FIFO queues is important for ordering: messages sharing the same group ID are processed sequentially. That prevents reordering, but it does not guarantee no duplicates. FIFO message deduplication prevents duplicate messages at send time if the messages are identical within the deduplication window, but once a message is successfully delivered, visibility timeouts, retries, and partial failures can cause redelivery. Making processing idempotent is a good practice and helps with duplicates but by itself does not prevent duplicates; idempotency reduces the impact but may not be sufficient if the user requires strict exactly-once semantics. So while this helps, it’s not the most reliable method to prevent duplicate side effects.

C) Switching to a standard SQS queue does not reduce duplication risk; in fact, standard queues have at-least-once delivery and do not provide ordering or deduplication guarantees. They are more likely to deliver duplicates than FIFO in many scenarios. So moving to a standard queue would not solve the problem and is the wrong approach.

D) Using SQS visibility timeout correctly combined with an explicit deduplication store is the most reliable way to prevent duplicate processing side effects when an at-least-once delivery system like SQS is in use. The Lambda handler can read the message ID (or a producer-supplied id in the message body) and consult a deduplication table (e.g., DynamoDB with conditional write / TTL) to atomically check-and-set whether that message has already been processed. If the conditional write succeeds, process the message; if it fails because an entry already exists, skip processing. This pattern provides idempotency enforcement on the consumer side and handles redeliveries due to function timeouts or transient failures. The SQS visibility timeout should be long enough to allow processing to complete and the deduplication write to finish; if processing fails and the message becomes visible again, the dedup table prevents duplicate side effects. This approach yields reliable de-duplication for at-least-once delivery systems.

Why the correct answer is D): SQS’s delivery model can produce duplicates in various failure scenarios. The only robust approach to preventing duplicate side effects is to implement idempotency at the consumer using a persistent deduplication mechanism (DynamoDB is commonly used) combined with proper use of visibility timeouts and conditional writes. This prevents processing the same logical message more than once regardless of redelivery by SQS, giving the strongest guarantee against duplicate side effects.

Question 3

Your team manages an autoscaling fleet of EC2 instances in private subnets behind an Application Load Balancer. You want to collect detailed application logs and metrics and forward them to a centralized account for analysis. Which architecture provides secure, reliable, and scalable log and metric forwarding while minimizing changes to the application?

A) Install an agent on each instance that pushes logs and metrics directly to the centralized account’s CloudWatch using cross-account IAM roles.

B) Configure each instance to write logs to a shared EFS mount, run a centralized collector on one instance that reads EFS and forwards to the centralized account.

C) Use the CloudWatch agent on each instance to publish logs and metrics to the local account’s CloudWatch, then use CloudWatch Logs subscription (Kinesis Data Firehose) with cross-account role to deliver data to the centralized account’s S3/Analytics.

D) Configure instances to send logs to the ALB access logs bucket and use Athena in the centralized account to query logs directly from there.

Answer: C)

Explanation

A) Installing a custom agent on each instance that pushes logs/metrics directly to the centralized account’s CloudWatch is possible but operationally complex and less secure. Cross-account pushing to CloudWatch is non-trivial: CloudWatch APIs typically require sending to the account where the namespace resides. Even with cross-account IAM, managing credentials and agents across autoscaling groups increases maintenance. Additionally, metrics and logs are generally best sent to the local account first and then centrally aggregated, enabling local troubleshooting and retention policies. Direct cross-account writes from each instance increases blast radius and operational overhead.

B) Writing logs to a shared EFS mount and having a single centralized collector introduces several problems: EFS introduces an extra dependency and potential bottleneck; a single collector instance is a single point of failure and can struggle to scale with large, autoscaling fleets; network and permission complexity increases. Also, many applications already write logs locally; forcing them to use a shared filesystem requires application changes. This approach does not minimize changes to the application and reduces reliability.

C) Using the CloudWatch agent on each EC2 instance to publish logs and metrics locally to the account’s CloudWatch is a standard and supported pattern. Once logs are in CloudWatch Logs, you can create subscription filters that forward log events to Kinesis Data Firehose or to a Lambda. Kinesis Data Firehose can deliver to S3 in the centralized account using a cross-account IAM role that grants the Firehose delivery stream permission to write to the central bucket. This approach is secure, scalable, and minimizes application changes because the CloudWatch agent handles collection and forwarding. It also provides near-real-time streaming, buffering, retry logic, and transformations if needed. Metrics can be aggregated locally and forwarded using CloudWatch cross-account mechanisms (e.g., exporting metrics or using metric streams) or exported via the same Firehose path. This architecture supports lifecycle, access control, and meets centralization requirements without placing single-instance bottlenecks.

D) ALB access logs capture HTTP request data for the load balancer but do not include application logs or application-level metrics. Relying solely on ALB access logs misses detailed app logs, structured logs, and internal metrics emitted by the application. While storing ALB access logs in S3 and querying with Athena is useful for LB-level analysis, it does not provide centralized application logging/metrics for deeper observability. Therefore, this approach is incomplete for the stated goal.

Why the correct answer is C): The CloudWatch agent → CloudWatch Logs → subscription to Kinesis Data Firehose → centralized S3/analytics account architecture is widely used for secure, scalable log centralization. It requires minimal changes to applications (install and configure CloudWatch agent), supports buffering and retry, integrates with IAM for secure cross-account delivery, and scales with autoscaling groups. This pattern avoids single points of failure and provides the necessary robustness for production workloads.

Question 4

A build pipeline in CodeBuild occasionally fails because of transient network errors when downloading dependencies. The project owner wants retries and exponential backoff when fetching external dependencies during the build without modifying every buildspec. Which approach minimizes changes to buildspecs while implementing retries for external network calls?

A) Create a custom Docker image for CodeBuild that includes a network proxy tool (e.g., a retrying wrapper) and configure builds to use that image.

B) Add retry commands before each external call in every buildspec to handle transient network failures.

C) Increase the timeout of CodeBuild projects so that transient failures have more time to succeed, avoiding the need for retries.

D) Migrate dependency resolution to a private Artifactory service so CodeBuild can access dependencies only from within AWS.

Answer: A)

Explanation

A) Building a custom Docker image for CodeBuild that includes tools or configuration to add retries (for example, a wrapper script that replaces common tools like curl/npm/pip with wrapper scripts that implement retries and exponential backoff) allows the team to control behavior centrally. Because CodeBuild can run with a custom image, all projects that use this image inherit the retry behavior without modifying individual buildspec files. This minimizes per-project changes, centralizes maintenance, and is consistent with immutable, repeatable build environments. The custom image can also include vetted dependency mirrors, cached package indexes, and network tuning. This approach is operationally efficient and meets the requirement to minimize buildspec edits.

B) Adding retry logic into every buildspec would work but requires changing potentially many buildspecs (one per project), which is exactly what the owner wanted to avoid. It’s error-prone, hard to maintain, and inconsistent across projects—some buildspecs might be updated while others are not. Thus this approach does not minimize changes.

C) Increasing the CodeBuild timeout might help if builds are failing because they exceed the overall timeout, but it does not implement retries or exponential backoff for transient network errors. A longer timeout may simply wait longer without addressing failures from immediate transient network errors. Retries require repeated attempts, not just more time. So this is not an effective remedy for transient network failures.

D) Migrating to a private artifact repository (Artifactory or an AWS-managed repository) can improve reliability because dependencies are fetched from a stable, internal source. However, this is a larger operational change—setting up, mirroring, and maintaining a private registry may be non-trivial and could be out of scope for the immediate desire to add retries without modifying buildspecs. While it’s a valid longer-term strategy to improve reliability, it requires significant work and does not fulfill the “minimize changes to buildspecs” constraint in the short term.

Why the correct answer is A): A custom CodeBuild image provides a centralized, maintainable way to add retry and backoff behavior for network calls used by build tools. It allows changing the runtime behavior for many projects at once without editing each buildspec. This pattern supports controlled rollout and consistent behavior, making it the best fit for the described requirement.

Question 5

A distributed application uses DynamoDB for session state. Sessions must expire after 30 minutes of inactivity. The team implemented a Time To Live (TTL) attribute and a CloudWatch scheduled job to scan and purge expired sessions, but they notice high read/write capacity and eventual consistency issues during peak. What is the best redesign to minimize read/write costs and reliably expire sessions?

A) Keep using DynamoDB TTL but increase table RCUs/WCUs during peak hours and rely on TTL to expire items.

B) Move session state to ElastiCache (Redis) with key TTLs and use the application to set an expiration when creating the session.

C) Keep sessions in DynamoDB, add a background process that queries by lastActivity timestamp using a GSI, and delete expired items.

D) Use DynamoDB Streams to trigger Lambda functions that compute TTL and delete items when appropriate.

Answer: B)

Explanation

A) Increasing provisioned read/write capacity to handle peaks simply raises cost and does not address inefficiencies. DynamoDB TTL is implemented as a best-effort background process; TTL expiry can take up to 48 hours to be removed after expiry and does not immediately free capacity. Additionally, relying on scheduled scans is expensive because scanning the table during peaks drives strong read load and cost. Thus simply increasing capacity doesn’t solve the latency of TTL removals nor the high read/write costs caused by scans.

B) Moving session state to an in-memory store like ElastiCache (Redis) with explicit key TTLs is a common design for session management. Redis supports per-key expirations with precise TTL semantics: keys are automatically evicted when TTL expires, and expirations are enforced with low latency. Using ElastiCache offloads frequent reads/writes away from DynamoDB, reduces DynamoDB costs, and provides fast, low-latency access for session lookups. The application sets a TTL when creating the session and refreshes it on activity. This pattern minimizes read/write costs in DynamoDB, provides reliable session expiry behavior aligned with the 30-minute inactivity requirement, and is widely used for session stores.

C) Keeping sessions in DynamoDB and implementing a background process that queries a GSI on lastActivity to delete expired items reduces the need for full table scans but still requires additional read and delete operations that grow with traffic. The background purge still consumes capacity and introduces complexity. Also deletions create write activity. While more efficient than table scans, it is still more costly and complex than using an in-memory TTL store for ephemeral sessions.

D) DynamoDB Streams can capture item modifications and trigger Lambda functions, but streams show changes as they happen; they do not natively signal “expiry” at the TTL time. Because TTL deletions by DynamoDB are handled internally and may be delayed, relying on streams to delete items would not produce predictable expiry at the exact 30-minute inactivity boundary. Also using streams to compute TTL and delete items would add complexity and extra write/delete cycles. This approach is not a clean fit for session expiry.

Why the correct answer is B): Session data is ephemeral and access patterns are low-latency, high-frequency reads/writes with simple expiration semantics. A managed in-memory cache like ElastiCache Redis with per-key TTLs provides precise expiry behavior, low latency, and reduced cost compared to DynamoDB scans or high provisioned capacity. It also simplifies application logic: set TTL on session creation and refresh on activity. For many web session use cases, Redis is the recommended pattern and reduces load and cost on DynamoDB.

Question 6

An organization uses AWS Systems Manager Parameter Store for application configuration. The DevOps team wants to implement automated multi-account configuration deployment so that parameters are created, updated, and versioned consistently across all accounts. They want central governance, auditability, and the ability to roll back changes. Which solution best meets these requirements?

A) Store parameters manually in each account and rely on IAM cross-account access for developers to update them directly.

B) Use AWS CloudFormation StackSets to push Parameter Store configuration templates from a central account to all target accounts.

C) Use AWS CodeCommit repositories in each account and configure CodeBuild jobs locally to deploy Parameter Store key/value pairs.

D) Use AWS Secrets Manager replication combined with Lambda functions to synchronize configuration parameters to all accounts.

Answer: B)

Explanation

A) Manually storing parameters in each account with developers updating them through cross-account IAM access introduces inconsistency and lack of governance. There is no template-based enforcement, no built-in rollback, and no audit trail aside from CloudTrail logs. Manual updates across accounts often lead to drift, making configurations diverge over time. This approach also increases operational overhead because every change requires coordinating edits in multiple accounts. Therefore, this method does not achieve central governance or reliable, automated multi-account deployment.

B) Using AWS CloudFormation StackSets allows configuration templates to be centrally managed and deployed automatically to multiple AWS accounts and regions. Parameter Store parameters can be defined inside CloudFormation templates as AWS::SSM::Parameter resources, giving versioned, declarative, repeatable deployments. StackSets support automatic account onboarding, drift detection, rollback, history tracking, and centralized auditing. This matches the requirement for centralized governance, consistent deployment, version-controlled configuration, and rollback support. With StackSets, the DevOps team can maintain a single template and push updates across accounts in a controlled and auditable manner. This solution directly addresses multi-account consistency, governance, and rollback requirements.

C) Maintaining separate CodeCommit repositories and CodeBuild jobs in each account is operationally heavy and defeats the goal of centralization. It requires duplicate pipelines in every account, making governance and auditing harder. Configuration drift can easily occur because each account has its own repository and build process. The lack of a single authoritative template makes auditing and rollback complex. Additionally, propagating updates to all accounts requires orchestrating multiple pipelines manually. This increases operational burden and does not meet the requirement for centralized governance.

D) Secrets Manager replication is designed for replicating secrets—not general configuration parameters—and only works within Secrets Manager, not Parameter Store. Configuration parameters such as feature flags, API endpoints, and non-secret values are not always appropriate to store as secrets. Using Lambda to manually push updates adds complexity, lacks versioned declarative configuration, and does not enforce consistency. This design creates an operational pipeline that must be maintained manually and does not provide the governance and rollback features CloudFormation provides. Thus, it is not an optimal solution.

Why the correct answer is B): CloudFormation StackSets allow fully automated, centrally governed, version-controlled deployment of parameters across multiple accounts and regions. They provide rollback, auditing, and drift detection—all of which meet the stated requirements. They eliminate configuration drift by establishing a central authoritative source. This makes StackSets the most suitable solution for consistent, reliable multi-account configuration management.

Question 7

A company operates a CI/CD pipeline that deploys a containerized application to Amazon EKS. The DevOps team wants to implement automated container vulnerability scanning to block deployments containing high-severity issues. They also want scans to occur before images are pushed to production. Which solution provides the most integrated, automated approach?

A) Configure the pipeline to run Amazon Inspector network scanning on each running EKS node before deployment.

B) Use Amazon ECR image scanning integrated with the CI/CD pipeline, enabling automated scans on image push and blocking deployment on high-severity findings.

C) Run a scheduled Amazon GuardDuty scan on the EKS cluster every hour to identify container image vulnerabilities.

D) Add a custom script in the pipeline that downloads CVE databases manually and performs local vulnerability scans before image push.

Answer: B)

Explanation

A) Running network scanning on EKS nodes with Amazon Inspector provides host-based and network-level insights but does not scan container images before deployment. EKS nodes might show OS-level vulnerabilities, but this does not block vulnerable images from being deployed. Network scanning does not identify CVEs inside container layers and is not integrated with image build pipelines. This method cannot enforce pre-deployment vulnerability blocking and therefore does not meet the requirement.

B) Amazon ECR image scanning is designed specifically for scanning container images for vulnerabilities. It integrates with ECR repositories and scans images automatically when they are pushed. Results appear in the AWS Console and API, allowing CI/CD pipelines (e.g., CodePipeline, Jenkins, GitHub Actions) to check scan results and block deployments when high-severity vulnerabilities are detected. This method focuses on the actual image artifacts, ensuring that the exact container version deployed to EKS is scanned before deployment. This aligns perfectly with the requirement to block deployments on severe vulnerabilities and provide automated, pre-production scanning.

C) Amazon GuardDuty is a threat detection service, not a vulnerability scanner. While GuardDuty EKS Runtime Monitoring can detect anomalous behavior, compromised containers, or suspicious API calls, it does not scan image layers for known CVEs before deployment. It cannot preemptively block deployments containing vulnerabilities. Therefore, it does not satisfy the requirement for pre-deployment vulnerability scanning.

D) Running a custom CVE scanning script increases operational effort and may lead to inconsistent or outdated vulnerability databases. Manual management of CVE signatures and custom scripts commonly results in inaccurate scanning. It also lacks native integration with the CI/CD system and requires ongoing maintenance. This approach does not match the fully integrated and automated workflow that the DevOps team is seeking.

Why the correct answer is B): Amazon ECR image scanning scans containers at push time using a managed vulnerability database. When combined with a CI/CD pipeline, the scan results can enforce automated deployment gates. This provides deep image-level vulnerability analysis, automation, and enforcement—all built into AWS and requiring minimal maintenance.

Question 8

A company’s Terraform setup is managed by multiple DevOps engineers. They want to prevent accidental drift, ensure that infrastructure changes are applied only through CI/CD pipelines, and require approval before any Terraform apply step. What is the best strategy to meet these requirements?

A) Allow engineers to run Terraform locally but require them to commit plans to Git before applying infrastructure changes.

B) Use Terraform Cloud or Terraform Enterprise with policy enforcement, remote state locking, and enforced apply-via-pipeline workflow.

C) Use S3 for Terraform state and allow only administrators to change bucket permissions manually when adjustments are needed.

D) Use AWS Config to detect drift and notify the DevOps team via SNS whenever drift occurs.

Answer: B)

Explanation

A) Allowing engineers to run Terraform locally still permits unreviewed changes, manual apply actions, and state corruption risks. Even if they commit plans to Git, they could bypass approval processes and apply changes from their workstation. This violates the requirement that Terraform apply must occur through CI/CD and that infrastructure modifications are centrally controlled. Local execution also risks misconfigured credentials or mismatched provider versions.

B) Terraform Cloud or Terraform Enterprise provides remote state management, locking, workspace controls, VCS integration, policy-as-code enforcement (Sentinel), role-based access control, and mandatory review workflows. Engineers submit pull requests, which trigger Terraform plan runs in Terraform Cloud. Apply steps can be gated by manual or automated approvals before being executed. State is secured, drift is reduced, and no local user can apply changes directly. This meets all requirements: no local applies, centralized review/approval, drift prevention, and CI/CD enforcement. It is the most complete solution for secure multi-engineer Terraform workflows.

C) Using S3 for state is common, but alone it does not prevent local apply operations. Engineers can still run Terraform locally and update state. Changing bucket permissions manually is not a scalable governance strategy and does not enforce CI/CD workflows or approval gates. S3 state locking (via DynamoDB) prevents concurrent applies but cannot enforce policy or multi-step approvals.

D) AWS Config drift detection helps detect changes in live infrastructure, but it is reactive, not preventative. It does not prevent engineers from applying Terraform locally and does not provide gating, approvals, or apply restrictions. Drift detection alone does not meet the governance requirements.

Why the correct answer is B): Terraform Cloud/Enterprise provides centralized, policy-enforced, fully governed Terraform execution with controlled apply workflows and mandatory approval stages. It prevents unauthorized modifications and ensures consistent infrastructure lifecycle management.

Question 9

An application deployed on Amazon ECS frequently experiences delays because image pulls take too long during scale-out events. The DevOps team wants to reduce image pull time significantly while continuing to store the images in Amazon ECR. What is the most effective way to accomplish this?

A) Increase the ECS service’s minimum healthy percent so more tasks stay running and prevent scaling events.

B) Use Amazon ECR’s pull-through cache and put a CloudFront distribution in front of the ECR repository to accelerate image delivery.

C) Enable Amazon ECS image caching on each container host using ephemeral volumes to retain recently pulled images.

D) Configure the ECS service to use a different container registry that has global caching built in by default.

Answer: C)

Explanation

A) Adjusting minimum healthy percent affects deployment behavior, not pull performance. It may reduce turnover of tasks but does not speed up pulling of large images during scale-outs. Slow pulls will still occur when new tasks start. This does not address the problem of long image fetch times.

B) ECR pull-through cache is for pulling from external registries (like Docker Hub) into ECR, not for accelerating internal ECR-to-ECS delivery. Putting CloudFront in front of ECR is not supported as ECR uses authorization headers, presigned URLs, and APIs incompatible with CDN caching. CloudFront cannot effectively cache private ECR image layers. Therefore, this is not a viable solution for accelerating pulls.

C) ECS supports image caching on container hosts such as EC2 launch types. On ECS EC2, once an image is pulled, it remains on the host unless evicted, so subsequent tasks start much faster. By using image caching strategies (for example, container instance warm pools, golden AMIs with pre-loaded images, or ECS capacity providers that maintain instances with cached images), the team can dramatically reduce pull time during scale-out. This addresses the root cause: freshly provisioned hosts need to fetch large images. With caching, hosts already contain the image layers, making container startup near-instant. This is the most direct and effective solution.

D) Switching to a different registry is unnecessary and may introduce new operational challenges. ECR is tightly integrated with ECS, IAM, and lifecycle management. Other registries may not significantly reduce pull time and may break existing workflows. The team’s requirement specifically mentions continuing to use ECR, so this option is inappropriate.

Why the correct answer is C): By keeping images cached on ECS hosts—via golden AMIs, warm pools, or persistent cache volumes—image pulls occur only once instead of during every scale-out. This directly reduces scaling latency and improves task startup time, making it the best solution.

Question 10

A company is using AWS CodePipeline with multiple stages for testing and approval. They need to ensure that deployments to production occur only when a specific compliance check is passed. The compliance check involves scanning the infrastructure codebase against organization-wide policies. Which approach best meets this requirement?

A) Add a manual approval step in the pipeline and require the security team to review every deployment.

B) Integrate AWS Config rules to block pipeline execution whenever noncompliant resources exist in the account.

C) Use a CodeBuild stage that runs a policy-as-code checker (such as Open Policy Agent or Checkov) and fails the stage if any compliance violation is found.

D) Rely on CloudTrail logs to audit deployments after they occur and revert if a problem is detected.

Answer: C)

Explanation

A) A manual approval step increases human overhead and does not enforce automated policy evaluation. Manual inspection is error-prone and inconsistent. It also slows down deployments unnecessarily and cannot guarantee policy correctness. The requirement specifies automated compliance checks, not manual review.

B) AWS Config rules evaluate deployed resources, not code before deployment. They are reactive rather than preventative. If a pipeline deploys noncompliant infrastructure, Config may alert afterward, but the resource is already live. This fails the requirement to ensure compliance before deployment reaches production.

C) Adding a CodeBuild stage to run policy-as-code scanning allows the pipeline to evaluate infrastructure code (Terraform, CloudFormation, Kubernetes manifests, etc.) before it reaches production. Tools like OPA or Checkov enforce standardized organizational rules and fail the pipeline if violations exist. This ensures production deployments occur only when code satisfies all compliance policies. This meets the requirement for automated, consistent, policy-based gating.

D) Auditing after deployment does not block noncompliant deployments. It is reactive and relies on rollback procedures, which may introduce downtime or risk. This does not satisfy the requirement to prevent noncompliant code from being deployed.

Why the correct answer is C): A policy-as-code compliance stage in CodePipeline evaluates infrastructure code before deployment, enforces rules automatically, prevents human error, and blocks noncompliant changes. It fulfills the organization’s need for automated compliance enforcement at the correct stage in the pipeline.

Question 11

A team runs a large-scale data ingestion pipeline on Amazon Kinesis Data Streams. During traffic spikes, consumers fall behind and iterator age increases significantly. The DevOps team wants to ensure scaling is automatic and that consumers always keep up with producer throughput. Which solution provides the most reliable, automatic consumer scaling without custom autoscaling logic?

A) Run consumer applications on EC2 and use CloudWatch alarms to trigger EC2 Auto Scaling policies based on iterator age.

B) Use Kinesis Data Firehose as the consumer instead of custom applications so scaling is handled automatically.

C) Implement enhanced fan-out consumers using AWS Lambda with automatic scaling of concurrent executions based on shard throughput.

D) Use DynamoDB Streams instead of Kinesis Data Streams to eliminate iterator age concerns.

Answer: C)

Explanation

A) Running consumer applications on EC2 requires manual scaling using CloudWatch alarms and EC2 Auto Scaling. Iterator age can trigger alarms, but these adjustments commonly lag behind traffic spikes, and scaling based on EC2 capacity does not guarantee matching per-shard throughput. This introduces operational complexity and still requires custom scaling logic for partition distribution across instances. It does not automatically scale at the granularity required by Kinesis shards.

B) Kinesis Data Firehose is used for delivery into storage destinations and does not function as a general-purpose consumer replacement. It cannot run custom ingestion logic, does not handle application-level transformations unless using Lambda integration, and is not suitable as a direct drop-in replacement for consumer applications that require custom processing. Firehose also does not replace consumers of Kinesis Data Streams. Thus, it cannot satisfy the requirement to scale custom consumer logic.

C) Enhanced fan-out consumers with AWS Lambda provide dedicated throughput per consumer per shard, removing consumer competition. Lambda scales automatically with the number of shards and the ingestion rate. Lambda’s concurrency model ensures one invocation per shard event batch, maintaining high availability and elasticity. Kinesis triggers for Lambda include checkpointing and retries, providing a resilient and automatically scaling consumer pattern. There is no need for custom scaling code—Lambda handles scaling natively, and enhanced fan-out ensures that consumer lag is minimized. This directly satisfies the requirement for automated scaling.

D) DynamoDB Streams is a separate service used for table change tracking. It does not replace the ingestion use case for high-throughput, sequential event streams typical in Kinesis. Switching to DynamoDB Streams would change the entire architecture, is not designed for massive ingestion load, and does not solve iterator age issues related to Kinesis.

Why the correct answer is C): Lambda enhanced fan-out consumers give dedicated read throughput, automatic scaling, and low-latency delivery without custom autoscaling logic. This aligns perfectly with the requirement for reliable, automatic consumer scaling.

Question 12

A company needs to secure its CI/CD pipeline so that only trusted container images are deployed. They want to enforce digital signing of all images and require deployment pipelines to verify the signature before pulling the image. Which approach best satisfies this requirement?

A) Use ECR lifecycle policies to restrict which images can be pulled to production.

B) Use Amazon ECR image scanning and block all images with vulnerabilities.

C) Use AWS Signer to sign container images and configure the pipeline to verify signatures before deployment.

D) Store container images in an encrypted S3 bucket and require IAM access control for all pipeline roles.

Answer: C)

Explanation

A) ECR lifecycle policies help manage storage and retention but do not enforce trust or authenticity. They cannot enforce cryptographic validation or prevent tampered images. Lifecycle rules cannot control pull-time verification, so they do not meet the requirement for image trust enforcement.

B) Image scanning checks for vulnerabilities, not image integrity. A vulnerability-free image can still be compromised if modified by an unauthorized party. Scanning alone does not assert authenticity or verify that an image was produced by a trusted party. It also does not verify signatures or enforce cryptographic integrity.

C) AWS Signer supports signing container images stored in ECR. Pipelines can verify signatures before deployment, ensuring images are authentic and unmodified. This provides cryptographic guarantees that images came from trusted sources. Signatures can be enforced directly in CI/CD workflows, ensuring only approved images progress to production. This provides the security guarantees required and integrates cleanly with AWS tooling.

D) Encrypting images in an S3 bucket with IAM controls secures storage but does not verify trust or integrity. Unauthorized modifications could still occur if credentials were compromised, and no cryptographic verification exists at deploy time. IAM alone does not provide attestation of image authenticity.

Why the correct answer is C): Only AWS Signer provides cryptographic signing and verification that ensures images are trusted and unmodified, fulfilling the requirement for enforced container integrity in CI/CD.

Question 13

A distributed application deployed on EC2 uses a shared configuration file stored on an EFS mount. Updates to the configuration must propagate instantly across all instances. Recently, the DevOps team observed delays due to EFS consistency behavior. They want faster, near-real-time configuration distribution without modifying every application instance. What solution provides the fastest propagation with minimal application changes?

A) Use AWS AppConfig and let instances poll for configuration updates at runtime.

B) Store configuration in DynamoDB and install a watcher process on each instance to read periodically.

C) Use Amazon S3 with versioning enabled and configure S3 event notifications to notify instances upon configuration updates.

D) Use AWS AppConfig with configuration push via AppConfig extensions or Lambda-based hosted configuration deployment.

Answer: D)

Explanation

A) Basic polling with AppConfig is effective but still relies on periodic checks. If polling intervals are long, propagation is delayed. Making polling intervals extremely short increases load and may still introduce slight delays. This does not offer near-real-time propagation and may require some application adjustment.

B) DynamoDB-based storage requires custom watcher scripts, new permissions, and periodic polling. Like other polling-based methods, propagation is not instant, and significant operational complexity is introduced. This violates the requirement to minimize modifications and achieve rapid propagation.

C) S3 event notifications can inform systems when a configuration file is updated, but delivering notifications directly to EC2 instances requires additional infrastructure such as SNS, SQS, or Lambda. Instances must subscribe to or poll messages, adding complexity. S3 also lacks native configuration distribution semantics, so integrating application-level updates requires custom code. This is not minimal change.

D) AWS AppConfig supports push-based configuration distribution through extensions, Lambda channels, and managed workflows. AppConfig can deliver updates to applications almost immediately without requiring polling. It is designed for fast, controlled rollout of configuration changes, enabling near-real-time propagation across distributed systems. Applications can consume configurations through standard AppConfig SDK methods or environment injectors with minimal modifications. This provides controlled rollouts, versioning, rollback, and instant distribution.

Why the correct answer is D): AWS AppConfig push-based distribution provides near-real-time, centrally managed configuration delivery with minimal application changes. It avoids polling delays and offers built-in safety, making it the best-fit solution.

Question 14

A company runs a multi-account architecture and wants to centralize DevOps logging. They want all CloudTrail, VPC Flow Logs, and CloudWatch Logs from each workload account to be delivered to a dedicated logging account. What is the most secure and scalable way to achieve this?

A) Configure each account to deliver logs directly to the logging account’s S3 bucket using cross-account bucket policies.

B) Export logs to local S3 buckets and use Lambda functions to copy them into the central logging account.

C) Use AWS Organizations trusted access with centralized CloudTrail and centralized CloudWatch Logs delivery into the logging account.

D) Use SCPs (Service Control Policies) to force each account to upload logs manually to the logging account.

Answer: C)

Explanation

A) Direct delivery to cross-account S3 buckets is valid but requires configuring each account manually. It does not provide centralized governance, standardization, or automated setup. Management overhead increases as accounts grow, and errors in bucket policies or delivery configuration can compromise log integrity.

B) Exporting logs to local buckets and copying via Lambda is slower, less secure, and inherently more expensive. It introduces multiple moving parts, increases latency, and creates operational burden. It also risks data duplication, partial transfers, and inconsistent logging.

C) AWS Organizations supports centralized CloudTrail and unified logging from all member accounts. CloudTrail can be configured organizationally to deliver logs from all accounts into a single S3 bucket or CloudWatch Logs group in the logging account. Similarly, CloudWatch Logs and VPC Flow Logs can use cross-account delivery with organization-trusted access, simplifying onboarding of new accounts automatically. This provides the most scalable, secure, governed method for multi-account logging.

D) SCPs define permission boundaries but cannot force log delivery. They can prevent disabling logging, but they cannot orchestrate the actual log transfer. Forcing manual uploads is operationally impractical and insecure.

Why the correct answer is C): Centralized organizational logging using AWS Organizations is the most secure, automated, scalable method for aggregating logs across accounts. It requires minimal per-account configuration and ensures governance and consistency.

Question 15

A team operates an event-driven system using Amazon EventBridge to route events to multiple consumers. They want strict ordering guarantees for certain event types and also want exactly-once processing behavior. What is the best pattern to achieve ordering and deduplication?

A) Use EventBridge with standard event buses and rely on retries for consistent processing.

B) Use EventBridge pipes with SQS FIFO queues as targets for ordered event types.

C) Use EventBridge archive and replay to rebuild ordering when needed.

D) Use EventBridge with Lambda destinations to handle failed events and reconstruct order manually.

Answer: B)

Explanation

A) Standard EventBridge buses provide at-least-once delivery and do not guarantee ordering. Consumers may receive events out of order, and duplicates are possible during retries. This cannot meet strict ordering or exactly-once requirements.

B) EventBridge Pipes allow routing events directly into SQS FIFO queues. FIFO queues guarantee ordering within a message group and support deduplication via message group IDs and deduplication IDs. This ensures strict ordering and predictable processing. Combining EventBridge Pipes with FIFO queues gives exactly-once semantics at the consumer level via deduplication logic. This satisfies both ordering and deduplication requirements without additional custom logic.

C) Archive and replay is used for debugging or backfilling, not for real-time ordering guarantees. It cannot enforce ordering or deduplication during normal operation. Replay reconstructs historical data, not real-time event delivery guarantees.

D) Lambda destinations handle success or failure routing but do not reorder events or enforce strict delivery guarantees. Manual reconstruction is error-prone and does not provide reliable ordering or deduplication during normal processing.

Why the correct answer is B): EventBridge Pipes integrated with SQS FIFO provides real-time ordering and deduplication in a managed, scalable pattern—meeting both strict ordering and exactly-once processing requirements.

Question 16

A company uses AWS CodePipeline to deploy microservices into Amazon ECS using blue/green deployments controlled by AWS CodeDeploy. They want to ensure automated rollback when new deployments increase error rates beyond a threshold. The solution must also analyze logs and produce rollback triggers based on patterns of application failures. Which solution meets these requirements?

A) Configure CloudWatch alarms based on ECS CPUUtilization to trigger rollbacks in CodeDeploy.

B) Use CodeDeploy automatic rollback with CloudWatch alarms that monitor application error metrics and integrate with CloudWatch Logs Insights for pattern detection.

C) Configure a Lambda function to continuously scan logs and call the CodeDeploy API to stop deployments.

D) Use ECS deployment circuit breaker capability with manual rollback steps enabled by operators.

Answer: B)

Explanation

A) CPU utilization is not an accurate metric for application errors. High or low CPU does not necessarily correlate with failure conditions. A deployment that produces functional errors may still show normal or even low CPU usage. This choice does not meet the requirement to detect application-level failures or interpret log patterns. Rollbacks based on CPU metrics are unreliable and not aligned with the goal of analyzing logs and error rates.

B) CodeDeploy supports automatic rollback based on CloudWatch alarms tied to application error metrics such as 4XX/5XX rates, custom application counters, or latency spikes. CloudWatch Logs Insights can generate embedded metrics using log patterns. These metrics can feed alarms that trigger rollback automatically. This provides a fully automated rollback mechanism that leverages log analysis, error detection, and threshold-based triggers. It satisfies the requirement for analyzing logs and initiating rollbacks based on error patterns without manual intervention.

C) A Lambda function scanning logs continuously introduces unnecessary complexity. It also creates a custom, brittle monitoring loop that is not as reliable as native rollback mechanisms. Continuous scanning increases cost, adds latency, and risks inconsistent rollback behavior. While Lambda can call CodeDeploy APIs, this approach does not meet the requirement for a managed, scalable, integrated rollback solution.

D) ECS deployment circuit breakers detect failures in some scenarios but do not integrate with log pattern analysis or custom error detection from CloudWatch Logs. Circuit breakers stop bad deployments but do not automate rollbacks with the sophistication required, such as analyzing logs and triggering based on error patterns. Manual rollback steps also violate the requirement for automated rollback.

Why the correct answer is B): CodeDeploy rollback integrates directly with CloudWatch alarms and supports log-based metrics, allowing detection of error patterns and automatic rollback. This matches the exact requirements of automated, log-driven rollback behavior.

Question 17

A DevOps team manages an API backend running on AWS Lambda and API Gateway. Recently, deployments caused intermittent production outages because newly deployed Lambda versions introduced bugs. They want safe deployments with automated rollback, traffic shifting, and the ability to gradually direct traffic to new Lambda versions. Which approach should they use?

A) Use Lambda versions with API Gateway stage variables to manually adjust which version receives traffic.

B) Configure Lambda aliases with traffic shifting using AWS CodeDeploy for canary or linear deployment patterns.

C) Use CloudFormation to update Lambda functions and rely on stack rollback behavior.

D) Use AWS SAM CLI to deploy functions incrementally.

Answer: B)

Explanation

A) Using stage variables to point to Lambda versions requires manual adjustments. It does not provide automated traffic shifting, rollback capabilities, or controlled canary analysis. Managing traffic with stage variables is error-prone and cannot automate rollbacks based on metrics. This approach fails to meet requirements for safe, gradual deployments.

B) Lambda aliases integrated with CodeDeploy allow traffic shifting between old and new Lambda versions using canary or linear patterns. CodeDeploy also supports automatic rollback by evaluating CloudWatch alarms. This fully satisfies the requirement of gradual rollout, safety, built-in rollback, and controlled traffic shifting. It is the AWS-recommended method for safe Lambda deployments.

C) CloudFormation rollback reverts stack resources when deployments fail at infrastructure level, but it does not provide traffic shifting or canary deployments. CloudFormation cannot manage gradual directing of live traffic to new Lambda versions. This option does not meet the requirement for progressive rollout or rollback based on runtime errors.

D) AWS SAM CLI supports deploying and managing Lambda functions but does not inherently provide automated rollback, traffic shifting, or controlled canaries. It performs infrastructure deployment, not progressive traffic management. Therefore, it cannot meet the requirement for gradual rollout.

Why the correct answer is B): CodeDeploy integrated with Lambda aliases is the only approach that provides traffic shifting, automated rollback, and safe incremental release management.

Question 18

A company runs a distributed workflow system using Amazon SQS queues feeding AWS Lambda consumers. During sudden bursts of traffic, Lambda concurrency spikes and downstream databases become overloaded. The company wants the ability to smooth out traffic spikes while still keeping messages highly available but processed at a controlled rate. Which solution achieves this?

A) Increase SQS visibility timeout so messages take longer to process.

B) Add SQS dead-letter queues to capture messages that cannot be processed quickly enough.

C) Use SQS with Lambda but enable maximum concurrency limits on the Lambda function coupled with a reserved concurrency value for predictable throughput.

D) Convert the architecture to use Kinesis Data Streams instead of SQS.

Answer: C)

Explanation

A) Increasing visibility timeout prevents messages from reappearing prematurely but does not control the processing rate or limit concurrency. It may actually worsen the problem by delaying reprocessing while still allowing sudden spikes to cause high Lambda concurrency. It does not provide rate smoothing capability.

B) Dead-letter queues do not smooth traffic. They only store messages that repeatedly fail processing. Adding DLQs does not solve database overload or concurrency issues. It only helps catch problematic messages, not regulate throughput.

C) Lambda triggered by SQS scales based on queue depth. However, you can apply Lambda concurrency limits and reserved concurrency to cap maximum parallel executions. This allows a controlled, predictable processing rate that prevents downstream overload while still ensuring messages remain available in the queue. SQS stores messages durably, so limiting concurrency does not risk data loss. This combination provides natural smoothing of spikes and protects databases.

D) Switching to Kinesis would require major architectural change and is not necessary. Kinesis also scales differently and does not inherently solve database overload without additional consumer-side throttling. It is not justified for the requirement and introduces unnecessary complexity.

Why the correct answer is C): Controlled Lambda concurrency ensures stable, predictable throughput and prevents downstream overload while SQS provides high message availability. This directly meets the need to smooth spikes.

Question 19

A DevOps engineer needs an automated solution for scanning AWS CloudFormation templates to detect misconfigurations, security issues, and policies violating organizational compliance rules. The process must run in CI/CD pipelines and enforce compliance before changes reach production. Which solution best meets these requirements?

A) Use CloudFormation StackSets with SCPs to prevent deployment of noncompliant resources.

B) Use AWS Config to evaluate templates before deployment.

C) Use cfn-lint and AWS CloudFormation Guard during CI/CD stages to validate templates against rules.

D) Deploy templates only in test accounts and manually review the output.

Answer: C)

Explanation

A) StackSets and SCPs operate at deployment time, not during template authoring. They block actions in accounts but do not give template developers immediate feedback. SCPs enforce account-wide permissions, not template validation. This does not meet the requirement for template scanning before deployment.

B) AWS Config evaluates deployed resources, not templates. It cannot scan CloudFormation templates before provisioning. Config detects drift and configuration violation after resources exist, so it cannot enforce pre-deployment compliance.

C) CloudFormation Guard (cfn-guard) allows writing rules that enforce structural and compliance requirements on templates before deployment. cfn-lint checks template syntax, schema violations, and best practices. Together, they provide comprehensive validation during CI/CD pipelines. They run pre-deployment, enforce compliance, analyze templates, and prevent misconfigurations. This meets the requirement entirely.

D) Manual review is slow, inconsistent, and unscalable. It cannot enforce automated compliance in pipelines and contradicts the requirement for automation. It is also error-prone and time-consuming.

Why the correct answer is C): cfn-lint plus CloudFormation Guard provide automated template analysis and compliance enforcement directly in CI/CD, satisfying all requirements.

Question 20

A system uses Amazon OpenSearch Service for log analytics. During index rollovers, nodes experience high CPU usage and slow query performance. The DevOps team needs to stabilize performance, ensure consistent ingestion rates, and reduce pressure on the cluster during rollover. What is the most appropriate solution?

A) Increase OpenSearch instance sizes to the next larger class.

B) Use UltraWarm storage for all log indices to reduce compute pressure on data nodes.

C) Implement Index State Management (ISM) policies with rollover based on shard count and size, while using hot-warm architecture.

D) Reduce ingestion rate during rollover by pausing log producers.

Answer: C)

Explanation

A) Increasing instance sizes may temporarily help but does not address underlying rollover inefficiencies. Larger nodes still face heavy CPU spikes if rollover thresholds are poorly configured. This approach also increases cost rapidly and does not improve long-term stability.

B) UltraWarm storage is optimized for infrequently queried data. Using it for active log ingestion is not recommended. It would worsen performance for new logs and increase query latency. UltraWarm cannot replace proper rollover management for hot indices.

C) ISM policies allow automatic management of index lifecycle stages. Using shard count and size as rollover triggers ensures that indices remain at optimal sizes, reducing the cost of segment merges and cluster strain. A hot-warm architecture isolates ingestion workloads to hot nodes and moves older indices to warm nodes, balancing performance and capacity. This stabilizes CPU usage, ensures consistent ingestion performance, and reduces rollover pressure.

D) Pausing log producers is disruptive and may cause data loss or backlog buildup. It does not solve cluster performance issues and breaks ingestion continuity. This violates operational requirements for log systems.

Why the correct answer is C): ISM with proper rollover thresholds and hot-warm architecture ensures stable performance, consistent ingestion, and minimized rollover impact without downtime.