Incident and Event Response practice questions

Check the SNS topic that the scaling policy publishes to for notifications.

Why wrong: Checking the SNS topic that the scaling policy publishes to will only deliver scaling lifecycle notifications or activity notifications indicating that a scaling action occurred. These notifications contain the policy name and the new/old desired capacity, but they never include the underlying application metric data or the reason the metric spiked. Without application-level telemetry, such as error rates, request latencies, or deployment markers, you cannot determine the root cause from SNS alone.

Use AWS CloudTrail to review API calls that modified the scaling policy.

Why wrong: AWS CloudTrail records API calls made by users, roles, or services, including calls that modify the scaling policy such as PutScalingPolicy or UpdateAutoScalingGroup. However, it does not capture application-level performance metrics or errors that would explain a sudden spike in load, and it is irrelevant if the policy definition has not been changed. Unless someone deliberately altered the policy thresholds or targets, CloudTrail will show no relevant events to investigate.

Temporarily disable the scaling policy and manually increase the desired capacity to handle the load.

Why wrong: Temporarily disabling the scaling policy and manually increasing desired capacity is a reactive mitigation that addresses the symptom, not the root cause. This action does not generate any diagnostic data, and it may even mask the issue by absorbing the load without revealing what caused the spike. It also risks leaving manual capacity in place after the spike subsides, leading to overprovisioning or conflicting with the dynamic scaling policy's target values.

Increase the task definition CPU to 1024 units and memory to 2048 MiB.

Why wrong: Raising the task from 512 units to 1024 units of CPU while also doubling memory will increase cost and may change the valid Fargate CPU/memory combination, but it does nothing to relieve the memory ceiling that caused the OutOfMemoryError. The container was already killed because its working set exceeded the configured memory limit; additional vCPUs cannot expand the task's cgroup memory cap. Even though 2048 MiB is added in this option, it needlessly pairs that memory increase with extra CPU, making this a more expensive over-provisioned fix than necessary.

Configure the ECS service to use a rolling update with a longer health check grace period.

Why wrong: Switching to a rolling update with a longer health check grace period only adjusts how ECS handles the transition between old and new tasks during a deployment, specifically how long it waits before deciding a replacement task is healthy. It does not affect steady-state memory consumption and cannot prevent the ECS agent or kernel from killing a task that exceeds its memory hard limit during regular operation. The OutOfMemoryError occurs because the runtime memory ceiling is too low for the workload, not because health checks are evaluating too soon.

Decrease the task definition memory to 512 MiB to force garbage collection more frequently.

Why wrong: Lowering the task memory to 512 MiB shrinks the kernel's cgroup limit even further, so the application will hit the maximum resident set size sooner and the OOM killer will act more aggressively. Forcing garbage collection more frequently does not help because the real problem is insufficient total heap capacity, not a rarely triggered GC in an adequately sized heap. This change would turn occasional OOM errors into guaranteed, immediate failures, making the application even less stable.

Review the CloudWatch metrics for CPU utilization and network throughput.

Why wrong: Reviewing CloudWatch CPU utilization and network throughput won't reveal the cause of unreachability if the instance is otherwise healthy and simply being blocked from receiving inbound traffic. High CPU or network saturation can cause latency or packet loss, but security group and network ACL denials operate at the packet filter level, not the resource utilization level, so these metrics may appear normal even when all inbound packets are silently dropped.

Reboot the instance to reset the network interface.

Why wrong: Rebooting the instance resets the guest OS and device drivers but does not change the security group, network ACL, route table, or VPC subnet configuration that governs whether inbound traffic is permitted. The ENI attachment and its associated firewall rules persist across a reboot, so if packets are being filtered by security group or NACL rules, a reboot will not resolve the connectivity issue and only adds avoidable downtime.

Stop and start the instance to move it to new underlying hardware.

Why wrong: Stopping and starting the instance does migrate it to new underlying hardware, but it retains the same security groups, network ACLs, and VPC networking configuration. If an inbound deny rule exists in the security group or NACL, the new host will still enforce the same deny at the network layer, so this action cannot fix an authorization-based reachability problem and is unnecessarily disruptive for a running workload.

Modify the S3 bucket event notification configuration to use a prefix filter that excludes duplicate objects.

Why wrong: S3 event notifications use at-least-once delivery, so the same object key can trigger multiple events because of client retries, network retries, or Lambda's asynchronous retry policy. A prefix filter in the bucket notification configuration only restricts which object keys generate events based on the key's prefix; it cannot detect or skip duplicate object keys. Therefore, configuring a prefix filter will not exclude duplicate objects and will not eliminate duplicate invocations.

Set the Lambda function's ReservedConcurrency to 1 to prevent concurrent executions.

Why wrong: Setting ReservedConcurrency to 1 enforces a maximum of one concurrent execution of the Lambda function, but duplicate S3 events will still invoke the function sequentially, each running the full processing logic. The function would therefore process the same object multiple times, just not at the same time, so it does not achieve idempotency. Additionally, capping concurrency at 1 creates a throughput bottleneck and can cause throttling errors under normal load.

Use an Amazon SQS FIFO queue as the event source and enable content-based deduplication.

Why wrong: S3 event notifications can be sent to Lambda directly or to SQS only if the bucket notification is explicitly configured for that queue. Content-based deduplication in SQS FIFO suppresses messages only when the message body is identical and within a 5-minute deduplication interval; S3 event payloads typically differ in request IDs or timestamps, so they are not recognized as duplicates. Even with FIFO, Lambda's event source mapping processes messages at least once, so duplicate processing remains possible.

Create a new stack from the same template and migrate resources.

Why wrong: Creating a new stack from the same template and migrating resources is an unnecessarily complex and risky alternative. It requires manually replicating or re-creating all resources, which may involve data migration, downtime, and reconfiguration of dependent systems. CloudFormation's `CreateStack` does not automatically capture the existing stack's state, and migrating an existing stack's live resources is not a trivial operation. Moreover, this approach does not resolve the original stack's failed rollback state, leaving the old stack in a broken condition unless it is also cleaned up.

Manually correct the resource configuration that caused the failure, then perform a stack update.

Why wrong: Manually correcting the resource configuration that caused the failure, then performing a stack update, is against CloudFormation's management model. Any manual changes outside CloudFormation create stack drift, meaning the actual resource state no longer matches the template-defined desired state. A subsequent stack update would likely detect mismatches, attempt to 'fix' the resource based on the template, and could cause an unintended replacement or further failure. CloudFormation does not recognize manual repairs as part of its state machine, so the stack remains in `UPDATE_ROLLBACK_FAILED` until you use an API operation like `ContinueUpdateRollback`.

Delete the stack and then recreate it from the same template.

Why wrong: Deleting the stack and then recreating it from the same template is an extreme and potentially destructive action. In an `UPDATE_ROLLBACK_FAILED` state, the stack may not delete cleanly because the same resource that prevented rollback could also block stack deletion. Furthermore, if the stack contains stateful resources (e.g., an RDS database) or data-bearing volumes, deleting the stack risks irreversible data loss unless deletion protection or a snapshot strategy is in place. Recreating also loses any existing resource IDs, endpoints, or configurations that dependencies may rely on. CloudFormation's `ContinueUpdateRollback` is specifically designed to recover from such failures without resorting to this disruptive approach.

Check the ALB target group health check settings and ensure instances are healthy.

Why wrong: Checking ALB target group health is a red herring here: if targets were unhealthy, the ALB would itself return 503 Service Unavailable or 502 Bad Gateway, and CloudFront would receive that error status, not time out. Since the ALB access logs show HTTP 200 responses, the registered instances are healthy and are successfully completing requests from CloudFront's perspective. Therefore, health check settings cannot be the cause of the 504, and the issue lies in timing between the two services.

Examine the request headers in CloudFront logs to identify unusual patterns.

Why wrong: Examining request headers for unusual patterns would only help identify application-level exceptions or WAF-related blocks, but a 504 from CloudFront is a gateway timeout meaning the origin never completed a response within CloudFront’s wait window. Headers themselves do not cause CloudFront to stop waiting; the response arrives too late (or not at all). While adversarial header patterns might induce slow backend processing, the actionable fix is still to compare timeout configurations, not to look for header anomalies.

Review the CloudFront cache hit ratio and optimize caching strategies.

Why wrong: Reviewing cache hit ratio and optimizing caching strategies will not resolve a 504 because the error occurs even on the first miss, when CloudFront must forward the request to the ALB and wait for the response. A high cache hit ratio would reduce the number of origin requests, but it cannot change the fact that the ALB is closing the connection or taking too long for the uncached requests. The 504 is a network/origin timeout issue, not a cache efficiency metric, so this action addresses a different performance problem.

Create a CloudWatch alarm on the SecurityGroupEvent metric to notify the security team.

Why wrong: A CloudWatch alarm on a SecurityGroupEvent metric is not a native CloudWatch metric; security group changes are recorded as API events through CloudTrail or EventBridge. Even if you created a custom metric or rule to trigger an alarm, the alarm can only notify the security team—it cannot automatically revoke or roll back the non-compliant security group rule, leaving a window of exposure until a human manually intervenes.

Apply a Service Control Policy (SCP) that denies changes to security groups in the production account.

Why wrong: An SCP is an organizational policy that denies or allows entire API actions, such as ec2:AuthorizeSecurityGroupIngress, for all principals in the account. It cannot inspect the content of the security group rule (for example, whether port 22 is open to 0.0.0.0/0) to block only non-compliant changes, and it does not remediate existing non-compliant rules, meaning legitimate changes would also be blocked while the offending rule remains active.

Set up a CloudTrail trail that logs security group modifications and use Amazon Detective to analyze the changes.

Why wrong: A CloudTrail trail captures all API activity, including security group modifications, and Amazon Detective can help investigate the source and scope of those changes after the fact. However, this approach is purely detective: it produces logs and analysis but has no built-in mechanism to automatically apply a corrective action or restore the security group to a compliant state, so the security exposure persists until a human runs a fix.

Enable AWS Shield Advanced on the ALB for additional DDoS protection.

Why wrong: AWS Shield Advanced is a paid service that requires a 12-month commitment and engagement with the AWS DDoS Response Team for proactive mitigations; enabling it on an ALB after an attack has started is not possible instantly because provisioning, subscribing, and associating resources takes time. While it provides enhanced volumetric attack detection and mitigation, it is not an immediate operational action you can take mid-incident. For a rapid L7 response, WAF rate-based rules are the right first step; Shield Advanced is a longer-term protection investment.

Scale up the EC2 instances by increasing their instance size.

Why wrong: Scaling up EC2 instances by moving to a larger size increases compute and memory capacity, but a DDoS attack typically overwhelms network bandwidth, connection state, or application resources, and a larger instance simply gives the attack a larger target. It does not filter out malicious requests, so legitimate traffic and attack traffic compete for the same resources, and the cost increase is immediate while the attack continues. This approach is neither a mitigation nor an immediate response; capacity scaling cannot absorb or stop a large-scale volumetric attack without other protections.

Place an Amazon CloudFront distribution in front of the ALB to cache content.

Why wrong: Placing a CloudFront distribution in front of the ALB can offer edge-level caching and absorb some spoofed or volumetric traffic, but it requires creating a distribution, configuring origins, attaching SSL certificates, and updating DNS records to point to CloudFront—none of which can be completed quickly during an active attack. For dynamic content that is not cacheable, CloudFront will still forward requests to the ALB, so it does not reduce the load on the origin unless you also configure edge behaviors and rate controls. It is a useful architectural improvement for future resilience, not a tactical immediate mitigation.

Increase the read and write capacity units manually before the sale.

Why wrong: Increasing read and write capacity units manually before the sale is reactive admin overhead: you must predict peak traffic, and DynamoDB capacity changes take time to propagate. If the sale traffic exceeds the manually set values, you still get throttled until another manual change completes, and if you set capacity too high you waste money after the surge ends. For a flash sale, use on-demand capacity or auto scaling with conservative targets instead of a one-time manual adjustment.

Switch from on-demand to provisioned capacity with auto scaling.

Why wrong: Switching from on-demand to provisioned capacity with auto scaling is counterproductive for a sale with unpredictable spikes. Auto scaling adjusts capacity only after CloudWatch utilization metrics are observed, so a sudden burst can exhaust the table's burst capacity and return ProvisionedThroughputExceededException before the scaling action takes effect. On-demand mode is designed to absorb instantaneous traffic spikes without capacity planning, making it a better fit for flash-sale load.

Enable DynamoDB Streams and replicate data to a read replica.

Why wrong: Enabling DynamoDB Streams and replicating to a read replica is invalid because DynamoDB does not offer native read replicas. Streams is a change-data-capture mechanism that records item-level INSERT, MODIFY, and DELETE events, commonly used for event-driven workflows or to feed external stores, but it does not serve point reads or reduce load on the primary table. Offloading reads requires DAX, ElastiCache, or Global Tables for cross-region use, as appropriate for your consistency and latency requirements.

Create a read replica and promote it to primary.

Why wrong: Creating a read replica and promoting it does not increase the storage capacity of the original DB instance. A read replica is a separate instance that replicates data for read scaling, and promotion simply makes that replica the new primary with the same allocated storage size. The underlying storage-full issue on the original volume remains unresolved, and the promotion process introduces downtime without adding any space to the existing instance.

Delete old automated snapshots to free up storage.

Why wrong: Deleting old automated snapshots does not free up space on the DB instance's allocated storage because automated backups are stored in Amazon S3, not on the instance's EBS volume. The storage-full error is caused by the growth of database files, transaction logs, or temporary files on the primary volume. Additionally, snapshot retention is independent of the instance's storage capacity, so removing them has no impact on available space.

Scale up the DB instance to a larger instance class.

Why wrong: Scaling the DB instance to a larger instance class changes the compute and memory resources (CPU and RAM) but does not alter the size of the allocated storage volume. The instance class and the storage allocation are separate dimensions in Amazon RDS, and increasing the former does not expand the latter. Therefore, this action does nothing to resolve the 'insufficient storage' condition and may incur unnecessary costs without addressing the root cause.

The EC2 instances are running out of memory and the application is crashing.

Why wrong: If EC2 instances were running out of memory and the application were crashing, the application process would either terminate or become unresponsive, causing health check requests to time out or return connection-refused errors, not HTTP 200. Since the health checks explicitly return 200, the application is alive and able to process inbound requests on the health path. Additionally, a crash-level OOM would prevent the ALB from getting any HTTP response for /api/orders, not a well-formed 500 error generated by the application's exception handler. The 500s indicate the application is executing code and failing on a specific operation, which is characteristic of a dependency failure rather than resource exhaustion.

The ALB is misconfigured and routing requests to the wrong target group.

Why wrong: An ALB misconfiguration that routes requests to the wrong target group would cause all traffic for that listener (including health checks and /api/orders) to hit the incorrect backing instances. However, the health checks are tied to the target group's registered instances, and if the ALB sent health checks to the wrong target group, those health checks would reflect the actual health of that unintended group; they still return 200, meaning the configured target group is healthy and correctly targeted. Furthermore, if the ALB were sending /api/orders to a wrong target group that doesn't host the service, the response would be a 404/503 from the ALB itself or a connection error, not an HTTP 500 generated by the application. The application-level 500 proves the request reached the correct application code, ruling out listener-level misrouting.

The EC2 instances are not passing health checks and are being deregistered from the target group.

Why wrong: Instances failing health checks are automatically deregistered from the target group, and the ALB stops sending them new requests, so they cannot produce HTTP 500 responses to live traffic. Since the log shows repeated HTTP 500 errors from /api/orders, traffic is still being forwarded to the backend instances, which means they are passing health checks and are in-service. The stated health check result of 200 directly contradicts this option, as the ALB would mark the instances unhealthy and remove them from rotation long before they could emit a 500. The 500s are coming from the application code after routing, not from an unhealthy/deregistered instance being unreachable.

The scaling policy is not properly configured; recreate it with a lower target value.

Why wrong: The scaling policy itself is functioning as intended because the metric is above the target and the policy is actively trying to scale out. Recreating it with a lower target value would only increase the desired count further, but the hard cap of 10 tasks prevents any change from taking effect. The root cause is the maximum capacity limit, not the policy's target setpoint.

The CloudWatch metric is not being emitted correctly; check the metric namespace.

Why wrong: The CloudWatch metric is being emitted correctly because it is visible and showing the expected pattern of high CPU utilization that exceeds the target value. The namespace (for example, ECS/ContainerInsights or a custom namespace) is correctly configured since the metric is present. This eliminates a missing-metric problem; the scaling failure stems from the task capacity limit, not from metric emission.

The ECS service is using Fargate, which does not support target tracking scaling policies.

Why wrong: Amazon ECS on Fargate fully supports target tracking scaling policies through Application Auto Scaling, with no limitation preventing their use. Fargate services can use the same scaling policies as EC2 launch type, including target tracking for CPU, memory, and other custom metrics. Therefore the Fargate launch type is not a barrier to scaling; the capacity cap is what is blocking the scale-out.

Use the AWS Management Console to view memory metrics from the EC2 monitoring tab.

Why wrong: The EC2 monitoring tab in the AWS Management Console displays only the metrics that CloudWatch collects automatically from the hypervisor — CPU, network, disk I/O, and status checks — none of which represent the OS-level memory usage. Since memory metrics are not sent by the hypervisor, they are simply absent from the console's graphs. Even enabling detailed monitoring changes only the collection frequency of these existing metrics, not their scope, so the console will still lack memory utilization data.

Use EC2Rescue to generate a memory dump and analyze it.

Why wrong: EC2Rescue is an offline diagnostic tool used for one-time troubleshooting of specific issues such as boot failures, disk errors, or OS-level crashes; it is not designed to run as a persistent agent and does not publish streaming metrics to CloudWatch. Running EC2Rescue to generate a memory dump would provide a point-in-time snapshot for forensic analysis, not ongoing memory utilization monitoring. Therefore, it cannot serve as a solution for continuously collecting and alarming on memory metrics.

Enable CloudWatch detailed monitoring on the instance.

Why wrong: CloudWatch detailed monitoring increases the granularity of the existing default EC2 metrics — for example, changing the period for CPU utilization from 5 minutes to 1 minute — but it does not introduce new metric dimensions. The hypervisor still does not report memory utilization because memory is a guest-OS resource that the hypervisor cannot directly measure. Thus, enabling detailed monitoring would only give you more frequent CPU or network data points, leaving memory metrics completely absent.

The S3 event notification is configured to send to an SNS topic that is not subscribed to the Lambda function.

Why wrong: If S3 is configured to publish to an SNS topic that has no Lambda subscription, the notification is undeliverable. SNS will log a delivery failure and may retry, but Lambda is never called, so there are no Lambda CloudWatch Logs. Rather than a silent absence of logs, you would typically see SNS delivery status failures, possibly in CloudTrail, which makes this inconsistent with the observed symptom.

The S3 bucket policy does not allow the Lambda function to be invoked.

Why wrong: An S3 bucket policy that lacks lambda:InvokeFunction permission would cause the invocation request to fail during authorization. Lambda's API would return AccessDeniedException before the function is executed, so no function logs appear. However, this failure is recorded in AWS CloudTrail as an API call from S3 to Lambda, giving a clear audit trail — the absence of any CloudTrail error would rule this out.

The Lambda function has a timeout that is too short.

Why wrong: A function timeout only occurs after the handler has been invoked and is running past its time limit. Lambda stops the execution and writes an execution report to CloudWatch Logs containing 'Task timed out after X seconds' plus the billed duration. That means you would see log events, not an absence of logs, so this does not explain a completely silent CloudWatch Logs group.

The RDS CNAME record was not updated by AWS after the failover.

Why wrong: This is incorrect because AWS RDS Multi-AZ failover automatically updates the DNS CNAME record for the primary DB instance to point to the new primary (the former standby) within seconds. The CNAME is managed by RDS and is updated programmatically during the failover, so a stale CNAME due to AWS not updating it is not a plausible cause. The issue is more likely that the application itself has cached the resolved IP from before the failover, not that the canonical record is outdated.

The application is using the read replica endpoint instead of the primary endpoint.

Why wrong: This is incorrect because read replica endpoints are distinct endpoints that point to the replica instance and are not used for primary read/write traffic. In a Multi-AZ deployment, failover affects only the primary endpoint; the read replica endpoint remains unchanged, so if the application were using a replica endpoint it would continue to connect to the replica (read-only) and would not experience a failover-related outage. The symptom described—connection failures or stale data after failover—indicates the application is using the primary endpoint but has not re-resolved its DNS.

The application is using a Route 53 health check that failed and redirected traffic away from the endpoint.

Why wrong: This is incorrect because Route 53 health checks are not automatically configured for RDS endpoints as part of the RDS service. While you can manually create a Route 53 health check that monitors an RDS endpoint (e.g., via a custom resource), the failover itself triggers a DNS change in the RDS-managed CNAME, not a Route 53 record change. Route 53 health checks do not redirect traffic away from the primary RDS endpoint during a Multi-AZ failover; they only affect records that are explicitly tied to a health check, which is not the default behavior here.

The ECS service is configured with a minimum healthy percent that prevents scaling out.

Why wrong: The minimum healthy percent is a deployment configuration parameter used by ECS to control how many tasks must remain available during a rolling update, not a scaling constraint. Automatic scaling adds or removes tasks by adjusting the desired count independently of the deployment lifecycle, so a high minimum healthy percent would only slow or block new task versions during deployments, not prevent scale-out actions.

The ECS service does not have an IAM role that allows it to call CloudWatch.

Why wrong: ECS service auto scaling does not rely on the ECS service's IAM role to call CloudWatch. Instead, Application Auto Scaling uses the service-linked role AWSServiceRoleForApplicationAutoScaling_ECSService, which grants permissions to read CloudWatch alarms and adjust the desired count. The task execution role or task role is used for pulling container images and accessing AWS services from within the container, but neither is involved in the scaling decision path.

The CloudWatch alarm is configured with a period that is too long.

Why wrong: A long CloudWatch alarm period affects how frequently the alarm evaluates metrics, but if the alarm has already entered the ALARM state, the scaling action should be triggered on the next evaluation. The period does not prevent an alarm from firing once the threshold is breached; it only delays detection. In this scenario, an alarm that is already breached should invoke the scaling policy unless another mechanism, such as a cooldown, is suppressing the action.

Enable CloudTrail Insights to detect unusual activity in the account.

Why wrong: CloudTrail Insights is designed to detect anomalous behavior by analyzing baseline patterns of write management events, not to match a specific, deterministic API call such as CreateAccessKey. It requires sufficient historical data to establish a baseline, and its output is an Insight event that indicates unusual activity, not a guaranteed notification for a fixed event type. Consequently, while Insights can alert broadly on abnormal access-key creation patterns, it cannot be configured to fire reliably on every CreateAccessKey call, making it inappropriate for this requirement.

Create an AWS Config rule that checks for access key creation and sends an SNS notification.

Why wrong: An AWS Config rule is intended to evaluate the configuration state of AWS resources and detect drift from desired settings; it does not monitor or react to API call events in real-time. For example, you could create a rule to check whether IAM users have access keys older than a defined max age, but that only reflects resource state at periodic evaluations or on configuration changes. When such a rule is non-compliant, it can send a notification via SNS, but this is not equivalent to detecting the moment CreateAccessKey is called. Therefore, using Config here would either miss immediate occurrences or introduce significant delay, and it does not align with the event-based alerting requirement.

Use CloudWatch Logs Insights to run a query on the CloudTrail logs and set an alarm.

Why wrong: CloudWatch Logs Insights is an interactive query engine for exploring and analyzing log data stored in CloudWatch Logs; it does not directly support setting alarms or sending real-time notifications. To alert on a specific event using logs, you would first need to create a CloudWatch Logs metric filter, such as filtering CloudTrail log events for CreateAccessKey, and then define a CloudWatch alarm based on that metric. The alarm, not Logs Insights, would ultimately trigger an SNS notification, and even this approach has higher latency and complexity compared to an EventBridge rule. The option as stated is incorrect because it relies on Logs Insights alone, which lacks both real-time monitoring and alarm capabilities.

Disable cross-zone load balancing on the ALB to limit the number of instances receiving traffic.

Why wrong: Disabling cross-zone load balancing confines traffic distribution to each Availability Zone's own targets, but the Application Load Balancer still accepts the full volume of malicious requests and the overall capacity is not reduced. This setting only shifts where requests are forwarded, and in fact can lead to underutilized instances across other AZs, making the application more vulnerable to overload and not addressing the DDoS source.

Configure the Auto Scaling group to scale based on the NetworkIn metric to handle the increased traffic.

Why wrong: Scaling the Auto Scaling group based on NetworkIn tries to add capacity to absorb malicious traffic, but a DDoS flood will simply consume those additional resources and drive up instance costs without relieving the overload. Because the attack traffic is not legitimate workload, the scale-out policy may chase the attack metrics, repeatedly launching instances and potentially surpassing account limits. High-level mitigation at the edge or WAF is required instead of reactive scaling.

Configure CloudTrail to send logs to CloudWatch Logs and create a metric filter that alerts on AuthorizeSecurityGroupIngress events with 0.0.0.0/0.

Why wrong: This approach is detective only. CloudTrail logs the AuthorizeSecurityGroupIngress API call, and a CloudWatch Logs metric filter can raise an alarm when the request contains an IPv4 CIDR of 0.0.0.0/0, but the filter cannot automatically undo the rule. Without a separate remediation workflow (such as an AWS Config rule invoking Systems Manager Automation), the insecure rule remains in effect until an engineer manually intervenes. Thus it fails to satisfy the requirement for automatic remediation.

Create an IAM policy that denies the ec2:AuthorizeSecurityGroupIngress action if the source IP is 0.0.0.0/0.

Why wrong: IAM policies evaluate the identity of the API caller and the requested action, not the contents of the security group rule being created. You cannot condition on the destination CIDR block inside the ec2:AuthorizeSecurityGroupIngress request, because no request parameter key exposes the rule's IP range to IAM. The aws:SourceIp condition key reflects the caller's network IP, not the security group rule's source, so this policy would incorrectly deny legitimate users based on their location and still allow all-IP ingress. Therefore it cannot prevent or undo the permissive rule.

Enable Amazon GuardDuty to detect and block such changes in real time.

Why wrong: GuardDuty is an intelligent threat detection service that reviews VPC flow logs, DNS query logs, and CloudTrail management events to produce security findings. It does not modify security groups or block configuration changes, and there is no GuardDuty feature that revokes an ingress rule. While GuardDuty might flag a compromised instance attempting unusual traffic, it would not act on a permissive security group rule added by an identity. This misunderstands the service's scope.

The ECS service's target group had an unhealthy instance that prevented the deregistration.

Why wrong: An unhealthy instance in the target group would affect the deployment's ability to maintain traffic health, but it does not prevent the ECS service from being updated; the service would simply replace the unhealthy task or trigger a deployment failure. The deregistration process is managed by the target group, not tied to the CloudFormation update cancellation. Moreover, the cancellation occurs before any task is actually deployed, and unhealthy instances would only matter during the health check phase, not during the desired count adjustment.

The ECS service deployment circuit breaker was triggered due to a timeout.

Why wrong: The deployment circuit breaker is an ECS feature that automatically triggers a rollback when a new deployment fails to stabilize, typically due to tasks failing health checks or reaching the maximum percent. It does not apply to a CloudFormation update cancellation, which is a client-side operation that occurs when the service scheduler rejects a change like a desired count adjustment. The circuit breaker is activated during an active deployment, not when the update request is initially rejected.

The ECS service did not have the required IAM role to call ecs:UpdateService.

Why wrong: If the IAM role lacked permission to call ecs:UpdateService, the CloudFormation stack update would fail with an AccessDenied error, and CloudFormation would mark the stack as UPDATE_FAILED. That is not the same as the update being 'cancelled'; a cancellation indicates the request was received but rejected by the ECS service logic. Another subtle point: CloudFormation uses the credentials of the caller, not the service's IAM role, to perform API calls, so the service's role itself is irrelevant to this failure.