Amazon AWS Certified Solutions Architect - Associate SAA-C03 Bundle

The option Migrate supported short handlers to Lambda with API Gateway addresses a separate condition: a low-traffic webhook service runs on an always-on EC2 instance. Serverless per-invocation billing can suit sporadic traffic. The actual requirement is a workload has stable continuous high CPU load and Lambda invocation charges exceed EC2. Recommended corrective action: Benchmark EC2/ECS continuous compute economics against Lambda. Long steady compute may favor provisioned capacity.

For the alternative failure a high-volume Lambda function has too much memory allocated for its actual requirements, the control Benchmark and tune Lambda memory-duration cost tradeoffs is relevant: lambda price depends on memory allocation and execution duration. Here the evidence instead is a workload has stable continuous high CPU load and Lambda invocation charges exceed EC2. The selected technical mechanism is Benchmark EC2/ECS continuous compute economics against Lambda: long steady compute may favor provisioned capacity.

Schedule ECS service desired-count reductions with safeguards solves a different operational problem where an ECS service's tasks run 24/7 at full size although demand is only daytime. Scaling down idle containers reduces compute charges. It does not directly address a workload has stable continuous high CPU load and Lambda invocation charges exceed EC2. The case calls for Benchmark EC2/ECS continuous compute economics against Lambda, since long steady compute may favor provisioned capacity.

Evaluate EC2 Instance Savings Plans for eligible regional family coverage would help if a stable same-region instance-family fleet has predictable usage. Instance Savings Plans trade flexibility for discounts. That is not the symptom described here: a job must remain continuously available with no interruption. The matching response is Use appropriate On-Demand or committed capacity rather than relying only on Spot, because spot can be interrupted and is not a sole hard-availability guarantee.

Evaluate Compute Savings Plans for flexibility under committed usage solves a different operational problem where a stable baseline runs across multiple instance families and regions. Compute Savings Plans apply across eligible compute configurations. It does not directly address a job must remain continuously available with no interruption. The case calls for Use appropriate On-Demand or committed capacity rather than relying only on Spot, since spot can be interrupted and is not a sole hard-availability guarantee.

The competing option Use Spot pools with diversified instance types and checkpoints would be useful when an occasional overnight reprocessing job retries safely. Diversification and replay safety reduce interruption impact. The present issue is a job must remain continuously available with no interruption; it is more directly addressed by Use appropriate On-Demand or committed capacity rather than relying only on Spot. Spot can be interrupted and is not a sole hard-availability guarantee.

For the alternative failure a stable same-region instance-family fleet has predictable usage, the control Evaluate EC2 Instance Savings Plans for eligible regional family coverage is relevant: instance Savings Plans trade flexibility for discounts. Here the evidence instead is a stable baseline runs across multiple instance families and regions. The selected technical mechanism is Evaluate Compute Savings Plans for flexibility under committed usage: compute Savings Plans apply across eligible compute configurations.

Use Spot pools with diversified instance types and checkpoints solves a different operational problem where an occasional overnight reprocessing job retries safely. Diversification and replay safety reduce interruption impact. It does not directly address a stable baseline runs across multiple instance families and regions. The case calls for Evaluate Compute Savings Plans for flexibility under committed usage, since compute Savings Plans apply across eligible compute configurations.

The option Use On-Demand Capacity Reservations where supported and justified addresses a separate condition: a workload needs capacity reservation, not merely a billing discount. Discount constructs do not automatically reserve instance capacity. The actual requirement is a stable baseline runs across multiple instance families and regions. Recommended corrective action: Evaluate Compute Savings Plans for flexibility under committed usage. Compute Savings Plans apply across eligible compute configurations.

Use appropriate On-Demand or committed capacity rather than relying only on Spot solves a different operational problem where a job must remain continuously available with no interruption. Spot can be interrupted and is not a sole hard-availability guarantee. It does not directly address a stable same-region instance-family fleet has predictable usage. The case calls for Evaluate EC2 Instance Savings Plans for eligible regional family coverage, since instance Savings Plans trade flexibility for discounts.

The competing option Use Spot pools with diversified instance types and checkpoints would be useful when an occasional overnight reprocessing job retries safely. Diversification and replay safety reduce interruption impact. The present issue is a stable same-region instance-family fleet has predictable usage; it is more directly addressed by Evaluate EC2 Instance Savings Plans for eligible regional family coverage. Instance Savings Plans trade flexibility for discounts.

The option Use On-Demand Capacity Reservations where supported and justified addresses a separate condition: a workload needs capacity reservation, not merely a billing discount. Discount constructs do not automatically reserve instance capacity. The actual requirement is a stable same-region instance-family fleet has predictable usage. Recommended corrective action: Evaluate EC2 Instance Savings Plans for eligible regional family coverage. Instance Savings Plans trade flexibility for discounts.

The competing option Evaluate Compute Savings Plans for flexibility under committed usage would be useful when a stable baseline runs across multiple instance families and regions. Compute Savings Plans apply across eligible compute configurations. The present issue is an occasional overnight reprocessing job retries safely; it is more directly addressed by Use Spot pools with diversified instance types and checkpoints. Diversification and replay safety reduce interruption impact.

Use On-Demand Capacity Reservations where supported and justified would help if a workload needs capacity reservation, not merely a billing discount. Discount constructs do not automatically reserve instance capacity. That is not the symptom described here: an occasional overnight reprocessing job retries safely. The matching response is Use Spot pools with diversified instance types and checkpoints, because diversification and replay safety reduce interruption impact.

For the alternative failure a job must remain continuously available with no interruption, the control Use appropriate On-Demand or committed capacity rather than relying only on Spot is relevant: spot can be interrupted and is not a sole hard-availability guarantee. Here the evidence instead is an occasional overnight reprocessing job retries safely. The selected technical mechanism is Use Spot pools with diversified instance types and checkpoints: diversification and replay safety reduce interruption impact.

Evaluate Compute Savings Plans for flexibility under committed usage would help if a stable baseline runs across multiple instance families and regions. Compute Savings Plans apply across eligible compute configurations. That is not the symptom described here: a workload needs capacity reservation, not merely a billing discount. The matching response is Use On-Demand Capacity Reservations where supported and justified, because discount constructs do not automatically reserve instance capacity.

For the alternative failure a stable same-region instance-family fleet has predictable usage, the control Evaluate EC2 Instance Savings Plans for eligible regional family coverage is relevant: instance Savings Plans trade flexibility for discounts. Here the evidence instead is a workload needs capacity reservation, not merely a billing discount. The selected technical mechanism is Use On-Demand Capacity Reservations where supported and justified: discount constructs do not automatically reserve instance capacity.

Use appropriate On-Demand or committed capacity rather than relying only on Spot solves a different operational problem where a job must remain continuously available with no interruption. Spot can be interrupted and is not a sole hard-availability guarantee. It does not directly address a workload needs capacity reservation, not merely a billing discount. The case calls for Use On-Demand Capacity Reservations where supported and justified, since discount constructs do not automatically reserve instance capacity.

Configure scheduled scale-out ahead of the spike would help if the workload spikes at 9 a.m. daily and scaling arrives late. Predictable capacity can be provisioned before demand. That is not the symptom described here: an EC2 Auto Scaling group keeps minimum ten hosts at night when one would suffice. The matching response is Adjust minimum/scheduled capacity using monitored demand, because capacity floors drive idle instance hours.

For the alternative failure a Lambda API has strict low latency but provisioned concurrency is enabled for every function 24/7, the control Scope provisioned concurrency to critical aliases and active hours is relevant: prepared concurrency has ongoing costs even without invocations. Here the evidence instead is an EC2 Auto Scaling group keeps minimum ten hosts at night when one would suffice. The selected technical mechanism is Adjust minimum/scheduled capacity using monitored demand: capacity floors drive idle instance hours.

Configure target tracking and safe scale-in policies solves a different operational problem where containers are held at maximum task count after a weekend burst. Target tracking removes capacity as demand falls. It does not directly address an EC2 Auto Scaling group keeps minimum ten hosts at night when one would suffice. The case calls for Adjust minimum/scheduled capacity using monitored demand, since capacity floors drive idle instance hours.

For the alternative failure tests create large EC2 instances and never terminate them, the control Automate resource expiration with tags and lifecycle rules is relevant: ephemeral environment cleanup reduces orphaned compute cost. Here the evidence instead is the workload spikes at 9 a.m. daily and scaling arrives late. The selected technical mechanism is Configure scheduled scale-out ahead of the spike: predictable capacity can be provisioned before demand.

Configure target tracking and safe scale-in policies solves a different operational problem where containers are held at maximum task count after a weekend burst. Target tracking removes capacity as demand falls. It does not directly address the workload spikes at 9 a.m. daily and scaling arrives late. The case calls for Configure scheduled scale-out ahead of the spike, since predictable capacity can be provisioned before demand.

The option Scope provisioned concurrency to critical aliases and active hours addresses a separate condition: a Lambda API has strict low latency but provisioned concurrency is enabled for every function 24/7. Prepared concurrency has ongoing costs even without invocations. The actual requirement is the workload spikes at 9 a.m. daily and scaling arrives late. Recommended corrective action: Configure scheduled scale-out ahead of the spike. Predictable capacity can be provisioned before demand.

The competing option Configure scheduled scale-out ahead of the spike would be useful when the workload spikes at 9 a.m. daily and scaling arrives late. Predictable capacity can be provisioned before demand. The present issue is a Lambda API has strict low latency but provisioned concurrency is enabled for every function 24/7; it is more directly addressed by Scope provisioned concurrency to critical aliases and active hours. Prepared concurrency has ongoing costs even without invocations.

The option Configure target tracking and safe scale-in policies addresses a separate condition: containers are held at maximum task count after a weekend burst. Target tracking removes capacity as demand falls. The actual requirement is a Lambda API has strict low latency but provisioned concurrency is enabled for every function 24/7. Recommended corrective action: Scope provisioned concurrency to critical aliases and active hours. Prepared concurrency has ongoing costs even without invocations.

Automate resource expiration with tags and lifecycle rules would help if tests create large EC2 instances and never terminate them. Ephemeral environment cleanup reduces orphaned compute cost. That is not the symptom described here: a Lambda API has strict low latency but provisioned concurrency is enabled for every function 24/7. The matching response is Scope provisioned concurrency to critical aliases and active hours, because prepared concurrency has ongoing costs even without invocations.

The option Automate resource expiration with tags and lifecycle rules addresses a separate condition: tests create large EC2 instances and never terminate them. Ephemeral environment cleanup reduces orphaned compute cost. The actual requirement is containers are held at maximum task count after a weekend burst. Recommended corrective action: Configure target tracking and safe scale-in policies. Target tracking removes capacity as demand falls.

Configure scheduled scale-out ahead of the spike would help if the workload spikes at 9 a.m. daily and scaling arrives late. Predictable capacity can be provisioned before demand. That is not the symptom described here: containers are held at maximum task count after a weekend burst. The matching response is Configure target tracking and safe scale-in policies, because target tracking removes capacity as demand falls.

For the alternative failure a Lambda API has strict low latency but provisioned concurrency is enabled for every function 24/7, the control Scope provisioned concurrency to critical aliases and active hours is relevant: prepared concurrency has ongoing costs even without invocations. Here the evidence instead is containers are held at maximum task count after a weekend burst. The selected technical mechanism is Configure target tracking and safe scale-in policies: target tracking removes capacity as demand falls.

Configure target tracking and safe scale-in policies would help if containers are held at maximum task count after a weekend burst. Target tracking removes capacity as demand falls. That is not the symptom described here: tests create large EC2 instances and never terminate them. The matching response is Automate resource expiration with tags and lifecycle rules, because ephemeral environment cleanup reduces orphaned compute cost.

For the alternative failure the workload spikes at 9 a.m. daily and scaling arrives late, the control Configure scheduled scale-out ahead of the spike is relevant: predictable capacity can be provisioned before demand. Here the evidence instead is tests create large EC2 instances and never terminate them. The selected technical mechanism is Automate resource expiration with tags and lifecycle rules: ephemeral environment cleanup reduces orphaned compute cost.

Scope provisioned concurrency to critical aliases and active hours solves a different operational problem where a Lambda API has strict low latency but provisioned concurrency is enabled for every function 24/7. Prepared concurrency has ongoing costs even without invocations. It does not directly address tests create large EC2 instances and never terminate them. The case calls for Automate resource expiration with tags and lifecycle rules, since ephemeral environment cleanup reduces orphaned compute cost.