CCNA Sdlc Automation Questions

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Option A is wrong because the IAM role for CodePipeline lacking permissions to access the CodeCommit repository would cause the source stage to fail, not the deploy stage. Option B is wrong because build artifacts not being stored in an S3 bucket would cause the build or source stage to fail, as CodePipeline requires artifacts to be passed between stages; the deploy stage error specifically relates to host health, not artifact storage. Option C is wrong because EC2 instances not being registered with a Classic Load Balancer is not a requirement for CodeDeploy to work; CodeDeploy can deploy to instances directly via tags or Auto Scaling groups, and the error is about host health, not load balancer registration.

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Option A is wrong because the IAM role for CodeBuild typically has permissions to read from the repository when the build is triggered; the failure is specific to forked repository restrictions, not IAM. Option B is wrong because if the buildspec were missing, CodeBuild would fail with a 'buildspec not found' error, but the question states the buildspec includes a command, implying it exists. Option D is wrong because invalid syntax would cause a build failure at the parsing stage, but the question's context of a forked repository pull request points to a configuration-level restriction, not a syntax issue.

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Option A is wrong because building all microservices on every change wastes compute time and resources, and does not scale efficiently. Option B is wrong because it requires splitting the monorepo into separate repositories, which contradicts the stated monorepo requirement and adds operational overhead for managing multiple repos. Option D is wrong because a single pipeline with multiple build actions that each check if their microservice changed still triggers the entire pipeline on any change, and the conditional checks inside build actions are inefficient and do not prevent the pipeline from running for all microservices.

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Option B is wrong because it runs unit tests serially, which increases overall pipeline duration unnecessarily since there is no dependency between microservice unit tests. Option C is wrong because creating separate pipelines per microservice prevents a single coordinated integration test stage after all unit tests pass; triggering via SNS would require custom orchestration and lose CodePipeline's built-in state management. Option D is wrong because AWS CodePipeline does not natively support fan-out/fan-in patterns; CodeBuild batch builds can parallelize builds but lack the stage-level dependency control needed to run integration tests only after all unit tests complete.

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Option A is wrong because IAM policies control authentication and authorization for AWS API calls, not network-level traffic; RDS security groups control inbound traffic at the network layer, and IAM does not open ports. Option C is wrong because a VPC endpoint for RDS is used to access the RDS API (e.g., to modify DB instances) from within a VPC without internet traffic, not to connect to the database engine itself; database connections use the database port, not the RDS API endpoint. Option D is wrong because a NAT gateway provides outbound internet access for private subnets, but the RDS instance is in the same VPC, so traffic between CodeBuild and RDS stays within the VPC and does not require internet access.

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Option B is wrong because kms:Decrypt is only needed if the S3 bucket uses server-side encryption with AWS KMS (SSE-KMS), but the question does not mention encryption, so the missing permission is not KMS-related. Option C is wrong because s3:PutObject is for uploading objects to S3, not downloading them; the error is about downloading artifacts, not uploading. Option D is wrong because logs:DescribeLogGroups is for listing CloudWatch log groups, which is unrelated to S3 access; it would not cause an S3 access denied error.

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Option A is wrong because S3 event notifications are not applicable to CodeCommit repositories; CodeCommit uses Git events, not S3 bucket events, and CodePipeline integrates directly with CodeCommit via its source action, not through S3 notifications. Option B is wrong because adding a manual approval action before the build stage would block the pipeline from automatically triggering; it would require manual intervention to proceed, defeating the purpose of automatic triggering on branch pushes. Option D is wrong because CodeBuild webhooks are used to trigger a CodeBuild project directly from a repository, not to trigger a CodePipeline; CodePipeline manages its own polling or event-based triggers for CodeCommit, and creating a separate webhook on CodeCommit would be redundant and not integrated with the pipeline's execution.

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Option A is wrong because splitting the build into multiple parallel build actions does not address the root cause of repeated dependency downloads; it only parallelizes independent build steps, which may reduce overall wall-clock time but does not eliminate the redundant download overhead. Option B is wrong because using multiple build environments distributes the work across different compute instances but does not cache dependencies; each environment would still download dependencies from scratch, so the total download time remains unchanged. Option D is wrong because using a larger compute type (e.g., more CPU/memory) may speed up the build process itself but does not prevent the repeated download of dependencies; the network-bound download step remains a bottleneck regardless of compute size.

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Option B is wrong because the S3 bucket policy is not the issue; the build likely succeeded in uploading to S3, and the error is related to CloudFront invalidation, not S3 write access. Option C is wrong because the CodeBuild project is associated with a service role (otherwise it couldn't perform any AWS actions), but that role simply lacks the specific CloudFront permission. Option D is wrong because an incorrect CloudFront distribution ID would result in a different error (e.g., NoSuchDistribution), not an access denied error.

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Option A is wrong because manually creating a separate CodePipeline for each microservice via the console violates the requirement to minimize manual configuration and does not scale. Option B is wrong because the AWS CDK cannot dynamically discover repositories at runtime; it requires explicit repository references in the code and does not automatically react to new repository creation events. Option C is wrong because using a single pipeline with multiple stages triggered by different branches of the same repository assumes all microservices share a single repository, contradicting the requirement that each microservice has its own CodeCommit repository and must be built and deployed independently.

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Option A is wrong because CloudWatch alarms that trigger a rollback do not stop the old task set; they only initiate the rollback process, which CodeDeploy handles by shifting traffic back to the original task set. Option B is wrong because CodeDeploy with ECS blue/green deployments uses the 'original' task set's listener rule weight to shift traffic back during rollback; it does not terminate the old task set until the deployment succeeds, and connection draining is handled by the ALB's deregistration delay, not by CodeDeploy. Option C is wrong because ECS Service Auto Scaling policies do not reduce the desired count of the old task set during a deployment; CodeDeploy manages the desired count of both task sets independently, and auto-scaling is suspended or operates on the active service, not on the old task set being preserved for rollback.

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Option A is wrong because a CloudWatch alarm can trigger actions like scaling or notifications, but it cannot directly stop a CodeDeploy deployment; CodeDeploy has its own built-in rollback and health check mechanisms that should be used. Option B is wrong because deploying to one instance at a time reduces risk but does not prevent instances from failing health checks and being terminated by the Auto Scaling group; it only limits blast radius. Option D is wrong because increasing the desired capacity of the Auto Scaling group does not address the root cause of health check failures; it only masks the problem by adding more instances, and unhealthy instances will still be terminated.

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Option A is wrong because configuring the build to use a VPC with a NAT gateway would add network complexity and cost without solving the dependency download bottleneck; NAT gateways are for outbound internet access from private subnets, not for caching dependencies. Option B is wrong because while AWS CodeArtifact can serve as a proxy for Maven dependencies, it introduces additional service costs and setup overhead, making it less cost-effective than local caching for this specific scenario. Option C is wrong because changing the compute type to general1.2xlarge would increase cost by doubling compute capacity without addressing the root cause of slow dependency downloads; the build time is dominated by network latency, not CPU or memory constraints.

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Option A is wrong because drift detection compares the current state of a stack with its expected template configuration, but it does not prevent updates; it only reports differences after they occur. Option C is wrong because AWS CloudFormation StackSets are designed for deploying identical templates across multiple accounts and regions, not for reviewing or blocking changes based on IAM modifications in a single production stack. Option D is wrong because a custom resource that checks for IAM changes and fails the update would require complex custom logic and would not leverage the built-in change set review capability; it also risks breaking the update process entirely rather than providing a controlled review step.

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Option A is wrong because installing Node.js 12 from source via an install command would not override the runtime-versions section, and the build environment's runtime version validation occurs before the install phase, so the error would persist. Option B is wrong because removing the runtime-versions section and installing Node.js manually would still fail, as the build environment's default runtime (if any) might not match, and the error is triggered by the environment's lack of support for Node.js 12, not by the presence of the section. Option D is wrong because while using a custom image with Node.js 12 would work, it is an unnecessary and more complex solution compared to simply updating the runtime-versions to a supported version like nodejs: 14, which is the intended fix.

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Option B is wrong because CloudFormation does not have an 'UpdateReplace policy'; instead, it uses replacement behaviors based on the resource's 'RequiresRecreation' property, and the error here is about a duplicate identifier, not a missing policy. Option C is wrong because a 'DeletionPolicy' of 'Retain' would cause the old RDS instance to persist after stack deletion, but during an update replacement, CloudFormation creates the new instance before deleting the old one, leading to a duplicate identifier conflict — however, the error message specifically says 'already exists,' which is the direct cause, not the DeletionPolicy itself. Option D is wrong because deletion protection prevents the instance from being deleted via the AWS API, but it does not prevent CloudFormation from attempting to create a new instance with the same identifier; the error is about creation, not deletion.

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Option B is wrong because StackSets are used to deploy CloudFormation stacks across multiple accounts and regions, not to review or approve changes to a single stack. Option C is wrong because Drift Detection identifies whether a stack's actual resources have diverged from its template, but it does not provide a mechanism to preview or approve proposed modifications. Option D is wrong because a Stack Policy is a resource-level permission document that prevents accidental updates or deletions of specific stack resources, but it does not generate a preview of changes or enforce a review-and-approve workflow.

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Option A is wrong because adding a Lambda function introduces unnecessary complexity and cost; CodeCommit triggers invoke Lambda for custom actions, but EventBridge provides a built-in, simpler integration to start pipelines. Option B is wrong because polling every 5 minutes introduces latency (up to 5 minutes delay) and is inefficient compared to event-driven triggers; CodePipeline supports event-based triggers via EventBridge or webhooks. Option C is wrong because CodeCommit does not support webhooks for pipeline triggers; webhooks are used with third-party sources like GitHub or Bitbucket, not with CodeCommit, which relies on EventBridge or CloudWatch Events.

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Option A is wrong because the error is about missing IAM permissions, not about subnet internet access; Lambda functions in a VPC do not need internet access unless they explicitly require it (e.g., via a NAT gateway). Option B is wrong because while a security group is required for VPC-enabled Lambda, the error message specifically points to a missing IAM permission, not a missing security group specification. Option C is wrong because a syntax error in the Lambda code would cause a different error (e.g., 'CREATE_FAILED: Invalid function code' or a runtime error), not a permission-related failure during stack creation.

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Option B is wrong because the original task set's health checks are irrelevant — the original task set is not running (desired count is 0) and thus cannot fail health checks; the rollback failure is due to the inability to restore the task set, not health check failures. Option C is wrong because CloudFormation stacks are not involved in the CodeDeploy blue/green deployment process for ECS; the deployment is managed by CodeDeploy and ECS services, not CloudFormation stack deletion. Option D is wrong because deregistering the original task set from the target group would not prevent the rollback from scaling it up — the rollback fails because the original task definition is missing, not because of target group registration.

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Option B is wrong because AWS Elastic Beanstalk is a PaaS service that automates application deployment and scaling, but it does not allow you to define a CI/CD pipeline using a JSON/YAML file; it uses a configuration file (e.g., .ebextensions) for environment settings, not for pipeline stages. Option C is wrong because AWS CodePipeline is a CI/CD service that orchestrates source, build, and deploy stages, but it is the pipeline itself, not a tool to create the pipeline via a JSON/YAML file; while CodePipeline can be defined in a CloudFormation template, the question asks for the service to automate the creation of the pipeline, not the pipeline service itself. Option D is wrong because AWS CodeDeploy is a deployment service that automates code deployments to compute services like EC2 or Lambda, but it only handles the deploy stage and cannot define the entire pipeline (source, build, deploy) using a JSON/YAML file.

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Option B is wrong because if the pre_build phase failed, the build phase would not execute at all, and the error message would typically indicate a phase failure, not a 'No such file or directory' error for a specific file. Option C is wrong because an incorrect artifact definition would cause issues during the artifact packaging or upload phase, not during the build execution when accessing source files. Option D is wrong because environment variables in CodeBuild do not affect file path resolution; they are used for passing configuration values, not for locating files in the source directory.

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Option B is wrong because a Lambda function checking a parameter in Parameter Store does not inherently pause the pipeline or enforce a manual approval gate; it would require custom logic to block the pipeline and does not provide the native approval workflow with IAM-based authorization and notification. Option C is wrong because a CloudWatch Events rule can trigger a deployment based on events, but it cannot enforce a manual approval step within the pipeline; it would either start a new pipeline execution or invoke a target, not pause an existing pipeline for human review. Option D is wrong because setting the deploy action to manual invocation only means the action must be started manually outside the pipeline, which breaks the automated orchestration of CodePipeline and does not integrate the approval step as a gate within the pipeline flow.

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Option A is wrong because the 'artifacts' section is used to define output files to be uploaded to Amazon S3 or passed to downstream actions; it does not control build failure conditions or exit codes. Option B is wrong because the 'env' section is used to define environment variables, shell variables, or parameter-store references; setting a variable alone cannot cause the build to fail unless a command later uses it to exit non-zero. Option C is wrong because the 'reports' section is used to specify test report groups for CodeBuild to analyze and export to Amazon CloudWatch or a test report dashboard; it does not directly mark the build as failed if tests fail—only the exit code of commands in the 'phases' section determines build success or failure.

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Option A is wrong because a timeout that is too short would cause individual instance deployment failures, but the error message specifically points to the aggregate failure threshold being exceeded, not a timeout issue. Option B is wrong because insufficient permissions in the CodeDeploy service role would cause a different error, such as 'AccessDenied' or 'Unable to access the S3 bucket', not the specific healthy-instances threshold error. Option C is wrong because if instances were not running the CodeDeploy agent, they would appear as 'Unknown' or 'Not registered' in the deployment group, and the error would be about missing agents, not about too many failed instances relative to healthy ones.

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Option A is wrong because an IAM permission failure would produce an AccessDenied error, not 'bucket already exists'. Option C is wrong because S3 bucket names are global, not region-scoped, so a region mismatch would not cause this specific error. Option D is wrong because AWS::S3::Bucket is the correct resource type; using a wrong type would produce a template validation error, not a name-collision error.

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Option A is wrong because VPC peering connects two VPCs but does not provide internet access or a route to Amazon ECR; it would only allow communication between the peered VPCs, not to external services. Option B is wrong because a VPC gateway endpoint is only supported for Amazon S3 and DynamoDB, not for Amazon ECR; ECR requires an interface endpoint (PrivateLink) for private connectivity. Option C is wrong because attaching an internet gateway and adding a default route would give the VPC internet access, but CodeBuild in a VPC does not automatically use that route for pulling images; it would require a NAT gateway or instance in a public subnet, and even then, it is less secure and more complex than using a VPC interface endpoint.

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Option A is wrong because Amazon SNS is a pub/sub notification service that does not directly invoke CodeBuild; it would require a separate subscriber (e.g., a Lambda function) to parse the notification and call the CodeBuild API, adding unnecessary complexity. Option B is wrong because AWS CodePipeline is a CI/CD orchestration service that can integrate with CodeCommit and CodeBuild, but it is not designed to react to individual pull request events in real time without polling or webhook configuration; EventBridge is the native event-driven solution for this use case. Option C is wrong because Amazon CloudWatch Logs is used for storing, monitoring, and accessing log data, not for detecting repository events or triggering builds; it has no mechanism to capture CodeCommit pull request events.

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Option A is wrong because using S3 as the source in CodePipeline bypasses CodeCommit entirely, which breaks the existing developer workflow and version control history for binaries; it also does not solve the problem of slow clones from CodeCommit. Option B is wrong because storing binaries in a separate CodeCommit repository does not reduce the size impact on clone operations—each repository still contains the full binary history, and developers would need to clone both repositories, compounding the problem. Option C is wrong because increasing the repository size limit does not address the underlying issue of slow clones caused by large files; it only postpones hitting the limit while the repository continues to grow and performance degrades further.

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Option A is wrong because manually triggering builds via SNS notifications defeats automation and is neither efficient nor cost-effective for a CI/CD pipeline. Option C is wrong because a single pipeline building all services on every commit wastes resources and time, as unchanged services would be rebuilt unnecessarily. Option D is wrong because while EventBridge and Lambda can detect changes, this adds complexity and cost (Lambda invocations, custom logic) compared to the built-in webhook filtering in CodeBuild, which is more straightforward and cost-effective.

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Option B is wrong because a misspelled bucket name would cause a 'NoSuchBucket' error, not an access denied error. Option C is wrong because the developer's IAM user only needs permission to start the build (codebuild:StartBuild), not to read the source directly; the service role handles S3 access. Option D is wrong because CodeBuild can access S3 buckets in any region as long as the bucket policy and service role permissions allow cross-region access; there is no regional restriction for S3 sources in CodeBuild.

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Option A is wrong because AWS CodePipeline with ECS rolling update does not support automatic rollback on health check failure; manual intervention is required, violating the automatic rollback requirement. Option C is wrong because AWS Elastic Beanstalk is a PaaS service for single applications, not designed for microservices on ECS, and its rolling deployment with enhanced health reporting lacks native blue/green deployment and automatic rollback for ECS. Option D is wrong because AWS CloudFormation with ECS service update can update the service but does not provide built-in blue/green deployment or automatic rollback based on health checks; SNS notification only alerts, it does not trigger rollback.

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Option A is wrong because AWS CloudFormation StackSets are designed for deploying identical stacks across multiple accounts and regions from a single admin account, but they lack native integration with CodePipeline for step-by-step CI/CD orchestration, approval gates, and source stage triggers. Option B is wrong because creating separate pipelines in each account and triggering them manually defeats the purpose of automation and introduces operational overhead and inconsistency. Option D is wrong because using an S3 bucket with cross-account access and Lambda to invoke CloudFormation is an overly complex, brittle approach that bypasses CodePipeline's built-in cross-account role assumption mechanism, increasing maintenance burden and security risk.

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Option A is wrong because AWS CodePipeline is a CI/CD orchestration service that can poll CodeCommit for changes or be triggered by EventBridge, but it is not the direct service to connect CodeCommit events to CodeBuild for a single pull request trigger; it adds unnecessary complexity and cost. Option C is wrong because AWS Lambda can be used as a custom target to invoke CodeBuild, but it requires writing and maintaining custom code to parse the event and call the CodeBuild API, whereas EventBridge provides a native, serverless integration without code. Option D is wrong because Amazon SNS is a pub/sub messaging service that can receive events from EventBridge and fan out to subscribers, but it cannot directly trigger CodeBuild; you would still need an additional service (like Lambda or a webhook) to invoke the build.

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Option B is wrong because CodePipeline does not require a webhook for CodeCommit; it relies on polling by default, and the question states the pipeline polls the repository. Option C is wrong because if the IAM role lacked read permissions, the pipeline would fail consistently, not intermittently. Option D is wrong because CloudWatch Events is not required for CodePipeline to detect changes; the pipeline uses polling, not event-driven triggers, unless explicitly configured with a webhook or CloudWatch Events rule.

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Option B is wrong because pre-installing npm packages in a custom Docker image does not address the root cause of network timeouts during the build; it only avoids downloading packages for the first build, but subsequent updates or missing dependencies would still require internet access. Option C is wrong because local dependency caching reduces download time for repeated dependencies but does not resolve network timeouts caused by lack of outbound internet connectivity; the timeout would still occur on the first cache miss or cache refresh. Option D is wrong because increasing the build timeout does not fix the underlying network connectivity issue; it merely allows the build to wait longer for a timeout that will still occur if the npm registry is unreachable.

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Option A is wrong because increasing the stack creation timeout does not address the root cause of a failure; it only extends the wait period, and RDS instance creation typically completes within the default timeout unless there is a resource constraint or misconfiguration. Option C is wrong because RDS instances in a VPC require at least two subnets (public or private) in different Availability Zones only if you are creating a Multi-AZ deployment or a DB subnet group; the error message does not indicate a subnet issue, and this is not the first troubleshooting step. Option D is wrong while checking for a duplicate DB instance identifier is a valid possibility, the CloudFormation stack events will explicitly report a 'DBInstanceAlreadyExists' error if that is the cause, making it redundant to check the RDS console first; the events provide the definitive diagnosis.

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Option B is wrong because creating a separate CloudTrail trail for CodePipeline API calls is unnecessary; CloudTrail is already enabled by default for all AWS services, including CodePipeline, and logs all management events, including approval actions. Option C is wrong because CloudTrail Insights is designed to detect unusual API activity patterns (e.g., anomalous call volumes), not to log or verify individual approval events or identities. Option D is wrong because sending a notification to an SNS topic and logging delivery to CloudWatch Logs does not capture the approver's IAM identity in CloudTrail; it only logs the SNS delivery event, not the approval action itself.

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Option B is wrong because if the application were not registered with an Elastic Load Balancer, health checks would not be performed at all, and the deployment would not fail due to health check failures—it would either succeed or fail for another reason. Option C is wrong because the deployment group must be configured to use an Auto Scaling group for a blue/green deployment to work; if it were not, the deployment would fail at the start, not during health checks. Option D is wrong because if the CodeDeploy agent were not installed on the new instances, the deployment would fail during the Install phase, not during health checks, and the error would be about agent connectivity, not health check failures.

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Option A is wrong because using a custom build environment with pre-installed Java does not address the primary bottleneck of dependency downloads; it only saves a few seconds on environment setup. Option C is wrong because storing source code in CodeCommit instead of S3 does not reduce build time; both are source storage options with similar retrieval speeds, and the build time issue is not related to source code retrieval. Option D is wrong because increasing the compute type to a larger instance may improve CPU-bound tasks but does not solve the dependency download overhead, which is I/O-bound and network-bound; it would be a less effective and more costly solution.

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Option A is wrong because the CodeBuild project's VPC configuration does not need to include the subnet IDs where the database resides; it only needs to specify the VPC ID and the subnets where the build environment runs, and the database can be in a different subnet within the same VPC. Option B is wrong because security groups are stateful — if the CodeBuild project initiates outbound traffic, the return traffic is automatically allowed, so an explicit outbound rule is not required. Option D is wrong because the RDS database is in a private subnet and does not require internet access; CodeBuild can reach it via private IP within the VPC without an internet gateway.

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Option B is wrong because AWS::ServiceCatalog::CloudFormationProduct is used to create a product in AWS Service Catalog, which is a service for creating and managing a catalog of approved IT services, not for creating a CodeStar project. Option C is wrong because AWS::CodePipeline::Pipeline creates a CodePipeline pipeline, which is a CI/CD pipeline resource, but it does not create the overarching CodeStar project that orchestrates multiple services. Option D is wrong because AWS::CodeBuild::Project creates a CodeBuild build project, which is a single build step, not the full CodeStar project that includes source, build, deploy, and pipeline orchestration.

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Option A is wrong because setting 'On failure' to 'ABORT' causes the pipeline to stop immediately when an action fails, which is the opposite of the desired behavior. Option C is wrong because setting the stage's 'On failure' to 'ROLLBACK' triggers a rollback of the entire stage upon any failure, not allowing other actions to continue. Option D is wrong because while a custom Lambda function could be used to handle failures, it is not a native feature of CodePipeline for this specific requirement; the 'CONTINUE' setting is the straightforward, built-in solution.

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Option A is wrong because AWS CodeCommit does not support pre-push hooks or blocking pushes via Lambda; Lambda can only react to events asynchronously, not intercept or block a push in real time. Option B is wrong because AWS CodeCommit does not have a built-in 'branch protection rules' feature; that concept exists in GitHub and GitLab, not in CodeCommit. Option D is wrong because CloudWatch Events can notify when a push occurs but cannot enforce a review requirement before the merge; it is purely a notification mechanism.

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Option B is wrong because the buildspec version (0.2) is valid and widely used; an incorrect version would cause a parsing error, not a missing directory. Option C is wrong because Python 3.8 is a standard runtime available in CodeBuild; if it were unavailable, the error would be a runtime selection failure, not a missing directory. Option D is wrong because a missing requirements.txt would cause a pip install failure during the install phase, not a missing 'dist' directory after the build phase.

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Option A is wrong because simply creating a CloudWatch alarm that monitors the health check endpoint does not cause an automatic rollback; the alarm must be associated with the deployment group's rollback configuration. Option C is wrong because the Auto Scaling group health check grace period (default 300 seconds) only delays when EC2 health checks begin, but does not trigger a CodeDeploy rollback based on application-level health check failures. Option D is wrong because the deployment configuration's 'timeout' setting controls how long CodeDeploy waits for the deployment to complete before marking it as failed, not a health-check-based rollback trigger.

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Option A is wrong because AWS CodePipeline with source polling periodically checks the repository for changes, but it cannot directly react to a pull request creation event; it is designed for continuous delivery pipelines, not event-driven triggers for specific pull request actions. Option B is wrong because AWS CodeBuild with webhooks from CodeCommit supports triggers on push events to branches, not on pull request creation events; webhooks in CodeBuild are configured for branch or tag changes, not for pull request state changes. Option C is wrong because AWS CloudWatch Logs subscription filter for repository logs would require CodeCommit to emit logs for pull request events (which it does not by default) and is not designed to trigger actions based on repository events; it is meant for real-time log processing.

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Option A is wrong because the AWS CodeDeploy API is used for deploying applications to EC2, on-premises, or Lambda, not for pushing Docker images to ECR; pushing images is a registry operation, not a deployment action. Option B is wrong because the ECS RunTask API is used to run a standalone task in ECS, not to push images to ECR; pushing images must happen before any ECS task can reference them. Option C is wrong because CodeBuild does not have a built-in 'ecr-push' action or phase; the buildspec phases are 'install', 'pre_build', 'build', 'post_build', and custom commands must be written to perform Docker operations.

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Option A is wrong because while a VPC restriction could cause network issues, it would typically result in build failures due to dependency download errors, not test failures that pass locally. Option B is wrong because a pipeline timeout would terminate the entire build process, not cause specific test failures; the error would be 'BUILD_TIMEOUT' or similar, not 'BUILD_FAILED' with test failures. Option C is wrong because the question states the pipeline triggers on pushes to the 'develop' branch, so a branch filter for 'main' would prevent the pipeline from triggering at all, not cause intermittent failures.

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Option A is wrong because Amazon EventBridge scheduled rules run on a fixed interval (e.g., every minute) and cannot natively receive webhooks from external services like Jira; they are designed for internal AWS events or scheduled cron jobs, not real-time HTTP callbacks. Option C is wrong because AWS CodePipeline does not have a built-in capability to poll for Jira issues; it relies on source actions (e.g., CodeCommit, S3) or webhooks for specific services (e.g., GitHub), not arbitrary issue trackers. Option D is wrong because AWS CodeBuild is a build service that runs when triggered by a webhook, but it cannot directly create a branch in CodeCommit; it is designed to execute build commands, not perform repository management actions like branch creation.

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Option B is wrong because the error occurs during the 'build' phase, not the 'post_build' phase; failing unit tests in post_build would not produce a 'FileNotFoundError: setup.py' error. Option C is wrong because Python 3.8 is fully supported by CodeBuild; the error is about a missing file, not an unsupported runtime version. Option D is wrong because the artifacts configuration with `discard-paths` only affects how artifacts are stored after a successful build; it does not cause a missing file error during the build phase.

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Option A is wrong because an RDS maintenance window would affect all instances, not selectively block only the newly launched ones, and the error pattern points to connectivity from new instances. Option B is wrong because switching to Rolling would not fix a security group connectivity problem and Rolling updates instances in place, which is unrelated to the root cause. Option D is wrong because an application code bug would typically fail health checks regardless of deployment policy, and the logs specifically show a database connection failure, pointing to network/security configuration rather than code.

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Option A is wrong because using Lambda to deploy stacks across accounts lacks the orchestration, rollback, and approval capabilities of CodePipeline, and it does not natively support cross-account IAM role assumption for deployment. Option C is wrong because CodeDeploy is designed for deploying applications (e.g., EC2, Lambda, ECS) and does not have native actions to deploy CloudFormation stacks; CloudFormation deployment actions in CodePipeline are separate. Option D is wrong because a single CodeBuild project that both validates and deploys to all accounts violates the principle of least privilege and separation of concerns, and CodeBuild cannot natively assume IAM roles in multiple target accounts without complex scripting, whereas CodePipeline actions can directly assume roles.

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Option A is wrong because AWS CodeDeploy is a deployment service that automates code deployments to compute services like EC2, Lambda, or ECS, but it does not have built-in capabilities to run integration tests or execute test scripts as part of a CI/CD pipeline. Option C is wrong because Amazon CloudWatch is a monitoring and observability service for logs, metrics, and alarms; it cannot run integration tests or execute code, making it unsuitable for automated testing in a pipeline. Option D is wrong because AWS CloudFormation is an Infrastructure as Code (IaC) service used to provision and manage AWS resources; while it can deploy the serverless application, it lacks the ability to execute integration tests or validate application behavior after deployment.

Why this answer

How to eliminate wrong answers

Option A is wrong because insufficient IAM permissions would produce an 'access denied' or 'unauthorized' error, not a 'code is too large' error. Option B is wrong because CloudFormation template size limits (1 MB for templates, 51,200 bytes for parameters) are unrelated to the Lambda function code size; the error is about the function's code, not the template. Option C is wrong because CodePipeline artifact size limits (default 2 GB per artifact) are much larger than Lambda's code size limit, and the error message specifically mentions the Lambda function's code, not the pipeline artifact.