Amazon AWS Certified Solutions Architect - Associate SAA-C03 Bundle

The option Apply reserved or provisioned concurrency and upstream retry/backpressure strategy as appropriate addresses a separate condition: lambda concurrency limits cause 429 and throttling during promotions. Lambda concurrency controls bound simultaneous executions. The actual requirement is the public API needs request validation and authorization before invoking business code. Recommended corrective action: Configure API Gateway authentication and request validation for supported routes. API management filters unauthorized or malformed calls early.

For the alternative failure long-running video conversions exceed Lambda's maximum execution duration, the control Use AWS Step Functions plus suitable ECS/Batch tasks for longer jobs is relevant: orchestrated workloads avoid Lambda runtime-duration limits. Here the evidence instead is the public API needs request validation and authorization before invoking business code. The selected technical mechanism is Configure API Gateway authentication and request validation for supported routes: API management filters unauthorized or malformed calls early.

Use Amazon API Gateway with Lambda integration solves a different operational problem where web clients require a managed HTTPS entry for Lambda operations. API Gateway fronts serverless APIs and routing. It does not directly address the public API needs request validation and authorization before invoking business code. The case calls for Configure API Gateway authentication and request validation for supported routes, since API management filters unauthorized or malformed calls early.

Adjust partition-key distribution and stream throughput capacity would help if a Kinesis consumer falls behind after a shard hot spot emerges. A hot shard can throttle even when total stream traffic appears moderate. That is not the symptom described here: thousands of producers need ordered records per logical partition. The matching response is Use Amazon Kinesis Data Streams partition keys and sufficient shard/on-demand capacity, because partition keys provide per-shard ordering within stream limits.

Apply Amazon EventBridge event-bus rules with pattern matching solves a different operational problem where events must be routed to different targets according to their content. EventBridge rules select event targets by structured event fields. It does not directly address thousands of producers need ordered records per logical partition. The case calls for Use Amazon Kinesis Data Streams partition keys and sufficient shard/on-demand capacity, since partition keys provide per-shard ordering within stream limits.

The competing option Retain events in Kinesis Data Streams for bounded replay would be useful when a sensor app cannot lose events when an analytics processor pauses. Stream retention permits consumers to resume from checkpoints. The present issue is thousands of producers need ordered records per logical partition; it is more directly addressed by Use Amazon Kinesis Data Streams partition keys and sufficient shard/on-demand capacity. Partition keys provide per-shard ordering within stream limits.

Use S3 event notifications through EventBridge or SQS to invoke a worker solves a different operational problem where batch files arrive in S3 and must trigger lightweight processing. Event notifications decouple new object arrivals from processing. It does not directly address a sensor app cannot lose events when an analytics processor pauses. The case calls for Retain events in Kinesis Data Streams for bounded replay, since stream retention permits consumers to resume from checkpoints.

The competing option Use Amazon Kinesis Data Streams partition keys and sufficient shard/on-demand capacity would be useful when thousands of producers need ordered records per logical partition. Partition keys provide per-shard ordering within stream limits. The present issue is a sensor app cannot lose events when an analytics processor pauses; it is more directly addressed by Retain events in Kinesis Data Streams for bounded replay. Stream retention permits consumers to resume from checkpoints.

The option Adjust partition-key distribution and stream throughput capacity addresses a separate condition: a Kinesis consumer falls behind after a shard hot spot emerges. A hot shard can throttle even when total stream traffic appears moderate. The actual requirement is a sensor app cannot lose events when an analytics processor pauses. Recommended corrective action: Retain events in Kinesis Data Streams for bounded replay. Stream retention permits consumers to resume from checkpoints.

Adjust partition-key distribution and stream throughput capacity solves a different operational problem where a Kinesis consumer falls behind after a shard hot spot emerges. A hot shard can throttle even when total stream traffic appears moderate. It does not directly address batch files arrive in S3 and must trigger lightweight processing. The case calls for Use S3 event notifications through EventBridge or SQS to invoke a worker, since event notifications decouple new object arrivals from processing.

The competing option Retain events in Kinesis Data Streams for bounded replay would be useful when a sensor app cannot lose events when an analytics processor pauses. Stream retention permits consumers to resume from checkpoints. The present issue is batch files arrive in S3 and must trigger lightweight processing; it is more directly addressed by Use S3 event notifications through EventBridge or SQS to invoke a worker. Event notifications decouple new object arrivals from processing.

The option Use Amazon Kinesis Data Streams partition keys and sufficient shard/on-demand capacity addresses a separate condition: thousands of producers need ordered records per logical partition. Partition keys provide per-shard ordering within stream limits. The actual requirement is batch files arrive in S3 and must trigger lightweight processing. Recommended corrective action: Use S3 event notifications through EventBridge or SQS to invoke a worker. Event notifications decouple new object arrivals from processing.

The option Retain events in Kinesis Data Streams for bounded replay addresses a separate condition: a sensor app cannot lose events when an analytics processor pauses. Stream retention permits consumers to resume from checkpoints. The actual requirement is events must be routed to different targets according to their content. Recommended corrective action: Apply Amazon EventBridge event-bus rules with pattern matching. EventBridge rules select event targets by structured event fields.

Use S3 event notifications through EventBridge or SQS to invoke a worker would help if batch files arrive in S3 and must trigger lightweight processing. Event notifications decouple new object arrivals from processing. That is not the symptom described here: events must be routed to different targets according to their content. The matching response is Apply Amazon EventBridge event-bus rules with pattern matching, because eventBridge rules select event targets by structured event fields.

For the alternative failure a Kinesis consumer falls behind after a shard hot spot emerges, the control Adjust partition-key distribution and stream throughput capacity is relevant: a hot shard can throttle even when total stream traffic appears moderate. Here the evidence instead is events must be routed to different targets according to their content. The selected technical mechanism is Apply Amazon EventBridge event-bus rules with pattern matching: eventBridge rules select event targets by structured event fields.

The option Apply Amazon EventBridge event-bus rules with pattern matching addresses a separate condition: events must be routed to different targets according to their content. EventBridge rules select event targets by structured event fields. The actual requirement is a Kinesis consumer falls behind after a shard hot spot emerges. Recommended corrective action: Adjust partition-key distribution and stream throughput capacity. A hot shard can throttle even when total stream traffic appears moderate.

Retain events in Kinesis Data Streams for bounded replay would help if a sensor app cannot lose events when an analytics processor pauses. Stream retention permits consumers to resume from checkpoints. That is not the symptom described here: a Kinesis consumer falls behind after a shard hot spot emerges. The matching response is Adjust partition-key distribution and stream throughput capacity, because a hot shard can throttle even when total stream traffic appears moderate.

Use S3 event notifications through EventBridge or SQS to invoke a worker solves a different operational problem where batch files arrive in S3 and must trigger lightweight processing. Event notifications decouple new object arrivals from processing. It does not directly address a Kinesis consumer falls behind after a shard hot spot emerges. The case calls for Adjust partition-key distribution and stream throughput capacity, since a hot shard can throttle even when total stream traffic appears moderate.

For the alternative failure user sessions disappear when an application instance is replaced, the control Externalize session state to DynamoDB or ElastiCache as appropriate is relevant: stateless instances can be safely replaced if session state is external. Here the evidence instead is traffic bursts overwhelm one oversized instance while other capacity is unused. The selected technical mechanism is Place stateless app instances in an EC2 Auto Scaling group behind an ALB: horizontal scaling distributes requests across instances.

Use target tracking scaling on supported ALB request or utilization metrics solves a different operational problem where request rate spikes unpredictably during sales. Target tracking adjusts group size from measured load. It does not directly address traffic bursts overwhelm one oversized instance while other capacity is unused. The case calls for Place stateless app instances in an EC2 Auto Scaling group behind an ALB, since horizontal scaling distributes requests across instances.

The competing option Enable and correctly configure ELB health checks and grace periods would be useful when an Auto Scaling group keeps launching instances that fail its load-balancer health checks. Health evaluation determines whether instances are replaced. The present issue is traffic bursts overwhelm one oversized instance while other capacity is unused; it is more directly addressed by Place stateless app instances in an EC2 Auto Scaling group behind an ALB. Horizontal scaling distributes requests across instances.

Apply scheduled scaling based on observed traffic patterns solves a different operational problem where traffic rises predictably every weekday morning. Scheduled scaling provisions capacity ahead of known peaks. It does not directly address user sessions disappear when an application instance is replaced. The case calls for Externalize session state to DynamoDB or ElastiCache as appropriate, since stateless instances can be safely replaced if session state is external.

The competing option Enable and correctly configure ELB health checks and grace periods would be useful when an Auto Scaling group keeps launching instances that fail its load-balancer health checks. Health evaluation determines whether instances are replaced. The present issue is user sessions disappear when an application instance is replaced; it is more directly addressed by Externalize session state to DynamoDB or ElastiCache as appropriate. Stateless instances can be safely replaced if session state is external.

The option Use target tracking scaling on supported ALB request or utilization metrics addresses a separate condition: request rate spikes unpredictably during sales. Target tracking adjusts group size from measured load. The actual requirement is user sessions disappear when an application instance is replaced. Recommended corrective action: Externalize session state to DynamoDB or ElastiCache as appropriate. Stateless instances can be safely replaced if session state is external.

Externalize session state to DynamoDB or ElastiCache as appropriate solves a different operational problem where user sessions disappear when an application instance is replaced. Stateless instances can be safely replaced if session state is external. It does not directly address traffic rises predictably every weekday morning. The case calls for Apply scheduled scaling based on observed traffic patterns, since scheduled scaling provisions capacity ahead of known peaks.

The competing option Place stateless app instances in an EC2 Auto Scaling group behind an ALB would be useful when traffic bursts overwhelm one oversized instance while other capacity is unused. Horizontal scaling distributes requests across instances. The present issue is traffic rises predictably every weekday morning; it is more directly addressed by Apply scheduled scaling based on observed traffic patterns. Scheduled scaling provisions capacity ahead of known peaks.

Enable and correctly configure ELB health checks and grace periods would help if an Auto Scaling group keeps launching instances that fail its load-balancer health checks. Health evaluation determines whether instances are replaced. That is not the symptom described here: traffic rises predictably every weekday morning. The matching response is Apply scheduled scaling based on observed traffic patterns, because scheduled scaling provisions capacity ahead of known peaks.

The competing option Enable and correctly configure ELB health checks and grace periods would be useful when an Auto Scaling group keeps launching instances that fail its load-balancer health checks. Health evaluation determines whether instances are replaced. The present issue is request rate spikes unpredictably during sales; it is more directly addressed by Use target tracking scaling on supported ALB request or utilization metrics. Target tracking adjusts group size from measured load.

Apply scheduled scaling based on observed traffic patterns would help if traffic rises predictably every weekday morning. Scheduled scaling provisions capacity ahead of known peaks. That is not the symptom described here: request rate spikes unpredictably during sales. The matching response is Use target tracking scaling on supported ALB request or utilization metrics, because target tracking adjusts group size from measured load.

For the alternative failure user sessions disappear when an application instance is replaced, the control Externalize session state to DynamoDB or ElastiCache as appropriate is relevant: stateless instances can be safely replaced if session state is external. Here the evidence instead is request rate spikes unpredictably during sales. The selected technical mechanism is Use target tracking scaling on supported ALB request or utilization metrics: target tracking adjusts group size from measured load.

The option Place stateless app instances in an EC2 Auto Scaling group behind an ALB addresses a separate condition: traffic bursts overwhelm one oversized instance while other capacity is unused. Horizontal scaling distributes requests across instances. The actual requirement is an Auto Scaling group keeps launching instances that fail its load-balancer health checks. Recommended corrective action: Enable and correctly configure ELB health checks and grace periods. Health evaluation determines whether instances are replaced.

Externalize session state to DynamoDB or ElastiCache as appropriate would help if user sessions disappear when an application instance is replaced. Stateless instances can be safely replaced if session state is external. That is not the symptom described here: an Auto Scaling group keeps launching instances that fail its load-balancer health checks. The matching response is Enable and correctly configure ELB health checks and grace periods, because health evaluation determines whether instances are replaced.

For the alternative failure request rate spikes unpredictably during sales, the control Use target tracking scaling on supported ALB request or utilization metrics is relevant: target tracking adjusts group size from measured load. Here the evidence instead is an Auto Scaling group keeps launching instances that fail its load-balancer health checks. The selected technical mechanism is Enable and correctly configure ELB health checks and grace periods: health evaluation determines whether instances are replaced.