Amazon AWS Certified Solutions Architect - Associate SAA-C03 Bundle

The option Enforce cost allocation tags and enable reporting in billing tools addresses a separate condition: an engineering team has no ownership tags for chargeback on EC2 fleets. Tags attribute resource charges to responsible teams. The actual requirement is a short-term migration spawns large temporary compute that remains allocated afterward. Recommended corrective action: Add time-to-live cleanup and budget alerts to migration environments. Expiry enforcement limits abandoned spend.

For the alternative failure a GPU training fleet remains active when the training queue is empty, the control Scale GPUs to zero and provision by job queue demand is relevant: queue-driven provisioning reduces unused acceleration cost. Here the evidence instead is a short-term migration spawns large temporary compute that remains allocated afterward. The selected technical mechanism is Add time-to-live cleanup and budget alerts to migration environments: expiry enforcement limits abandoned spend.

Purchase appropriately scoped Savings Plans based on usage evidence solves a different operational problem where a cluster uses On-Demand for long predictable baseline jobs without commitments. Reservations/commitments can discount recurring use. It does not directly address a short-term migration spawns large temporary compute that remains allocated afterward. The case calls for Add time-to-live cleanup and budget alerts to migration environments, since expiry enforcement limits abandoned spend.

Right-size its instance class and storage performance after benchmarking would help if a fixed RDS instance is oversized relative to CPU/memory measurements. Paying for unused DB resources increases cost. That is not the symptom described here: a DynamoDB table has sporadic unpredictable traffic and provisioned RCU/WCU sit idle. The matching response is Use DynamoDB on-demand capacity where workload fits, because on-demand capacity follows requests without fixed provisioned units.

For the alternative failure the application needs no relational queries for simple session key lookups, the control Use DynamoDB for compatible key-value access instead of overprovisioned SQL is relevant: purpose-built key-value storage can reduce administration and cost. Here the evidence instead is a DynamoDB table has sporadic unpredictable traffic and provisioned RCU/WCU sit idle. The selected technical mechanism is Use DynamoDB on-demand capacity where workload fits: on-demand capacity follows requests without fixed provisioned units.

The competing option Evaluate Aurora Serverless v2 and its minimum capacity costs would be useful when an Aurora database handles short sporadic activity with long idle periods. Serverless v2 adjusts database capacity within configured bounds. The present issue is a DynamoDB table has sporadic unpredictable traffic and provisioned RCU/WCU sit idle; it is more directly addressed by Use DynamoDB on-demand capacity where workload fits. On-demand capacity follows requests without fixed provisioned units.

For the alternative failure a fixed RDS instance is oversized relative to CPU/memory measurements, the control Right-size its instance class and storage performance after benchmarking is relevant: paying for unused DB resources increases cost. Here the evidence instead is a steady DynamoDB workload has known throughput 24/7 and predictable utilization. The selected technical mechanism is Evaluate provisioned capacity with reserved capacity or auto scaling: provisioned throughput may be economical for steady usage.

Use DynamoDB for compatible key-value access instead of overprovisioned SQL solves a different operational problem where the application needs no relational queries for simple session key lookups. Purpose-built key-value storage can reduce administration and cost. It does not directly address a steady DynamoDB workload has known throughput 24/7 and predictable utilization. The case calls for Evaluate provisioned capacity with reserved capacity or auto scaling, since provisioned throughput may be economical for steady usage.

The competing option Evaluate Aurora Serverless v2 and its minimum capacity costs would be useful when an Aurora database handles short sporadic activity with long idle periods. Serverless v2 adjusts database capacity within configured bounds. The present issue is a steady DynamoDB workload has known throughput 24/7 and predictable utilization; it is more directly addressed by Evaluate provisioned capacity with reserved capacity or auto scaling. Provisioned throughput may be economical for steady usage.

Use DynamoDB on-demand capacity where workload fits solves a different operational problem where a DynamoDB table has sporadic unpredictable traffic and provisioned RCU/WCU sit idle. On-demand capacity follows requests without fixed provisioned units. It does not directly address an Aurora database handles short sporadic activity with long idle periods. The case calls for Evaluate Aurora Serverless v2 and its minimum capacity costs, since serverless v2 adjusts database capacity within configured bounds.

The competing option Evaluate provisioned capacity with reserved capacity or auto scaling would be useful when a steady DynamoDB workload has known throughput 24/7 and predictable utilization. Provisioned throughput may be economical for steady usage. The present issue is an Aurora database handles short sporadic activity with long idle periods; it is more directly addressed by Evaluate Aurora Serverless v2 and its minimum capacity costs. Serverless v2 adjusts database capacity within configured bounds.

The option Use DynamoDB for compatible key-value access instead of overprovisioned SQL addresses a separate condition: the application needs no relational queries for simple session key lookups. Purpose-built key-value storage can reduce administration and cost. The actual requirement is an Aurora database handles short sporadic activity with long idle periods. Recommended corrective action: Evaluate Aurora Serverless v2 and its minimum capacity costs. Serverless v2 adjusts database capacity within configured bounds.

The competing option Use DynamoDB for compatible key-value access instead of overprovisioned SQL would be useful when the application needs no relational queries for simple session key lookups. Purpose-built key-value storage can reduce administration and cost. The present issue is a fixed RDS instance is oversized relative to CPU/memory measurements; it is more directly addressed by Right-size its instance class and storage performance after benchmarking. Paying for unused DB resources increases cost.

Use DynamoDB on-demand capacity where workload fits would help if a DynamoDB table has sporadic unpredictable traffic and provisioned RCU/WCU sit idle. On-demand capacity follows requests without fixed provisioned units. That is not the symptom described here: a fixed RDS instance is oversized relative to CPU/memory measurements. The matching response is Right-size its instance class and storage performance after benchmarking, because paying for unused DB resources increases cost.

For the alternative failure a steady DynamoDB workload has known throughput 24/7 and predictable utilization, the control Evaluate provisioned capacity with reserved capacity or auto scaling is relevant: provisioned throughput may be economical for steady usage. Here the evidence instead is a fixed RDS instance is oversized relative to CPU/memory measurements. The selected technical mechanism is Right-size its instance class and storage performance after benchmarking: paying for unused DB resources increases cost.

The option Evaluate Aurora Serverless v2 and its minimum capacity costs addresses a separate condition: an Aurora database handles short sporadic activity with long idle periods. Serverless v2 adjusts database capacity within configured bounds. The actual requirement is the application needs no relational queries for simple session key lookups. Recommended corrective action: Use DynamoDB for compatible key-value access instead of overprovisioned SQL. Purpose-built key-value storage can reduce administration and cost.

Evaluate provisioned capacity with reserved capacity or auto scaling would help if a steady DynamoDB workload has known throughput 24/7 and predictable utilization. Provisioned throughput may be economical for steady usage. That is not the symptom described here: the application needs no relational queries for simple session key lookups. The matching response is Use DynamoDB for compatible key-value access instead of overprovisioned SQL, because purpose-built key-value storage can reduce administration and cost.

For the alternative failure a fixed RDS instance is oversized relative to CPU/memory measurements, the control Right-size its instance class and storage performance after benchmarking is relevant: paying for unused DB resources increases cost. Here the evidence instead is the application needs no relational queries for simple session key lookups. The selected technical mechanism is Use DynamoDB for compatible key-value access instead of overprovisioned SQL: purpose-built key-value storage can reduce administration and cost.

Measure query demand and migrate to a smaller suitable instance would help if a large legacy RDS instance has more storage and CPU than the dataset needs. Database right sizing aligns resources with workload. That is not the symptom described here: analytics scans repeatedly slow the OLTP RDS primary during peak hours. The matching response is Move analytical queries to suitable replicas or warehouse as warranted, because read workload separation reduces transactional contention.

For the alternative failure each microservice opens thousands of short-lived direct RDS connections, the control Add RDS Proxy and reusable connection pools is relevant: connection pooling reduces memory/CPU cost of connection churn. Here the evidence instead is analytics scans repeatedly slow the OLTP RDS primary during peak hours. The selected technical mechanism is Move analytical queries to suitable replicas or warehouse as warranted: read workload separation reduces transactional contention.

The competing option Apply snapshot retention governance and delete approved obsolete copies would be useful when retired snapshots from an old RDS database remain indefinitely. Unneeded snapshots keep incurring storage cost. The present issue is analytics scans repeatedly slow the OLTP RDS primary during peak hours; it is more directly addressed by Move analytical queries to suitable replicas or warehouse as warranted. Read workload separation reduces transactional contention.

For the alternative failure retired snapshots from an old RDS database remain indefinitely, the control Apply snapshot retention governance and delete approved obsolete copies is relevant: unneeded snapshots keep incurring storage cost. Here the evidence instead is a read replica exists solely for a report run once monthly. The selected technical mechanism is Start/stop or choose a cost-appropriate analytic extract instead where supported: infrequent reports need not require continuous reader capacity.

Add RDS Proxy and reusable connection pools solves a different operational problem where each microservice opens thousands of short-lived direct RDS connections. Connection pooling reduces memory/CPU cost of connection churn. It does not directly address a read replica exists solely for a report run once monthly. The case calls for Start/stop or choose a cost-appropriate analytic extract instead where supported, since infrequent reports need not require continuous reader capacity.

The option Move analytical queries to suitable replicas or warehouse as warranted addresses a separate condition: analytics scans repeatedly slow the OLTP RDS primary during peak hours. Read workload separation reduces transactional contention. The actual requirement is a read replica exists solely for a report run once monthly. Recommended corrective action: Start/stop or choose a cost-appropriate analytic extract instead where supported. Infrequent reports need not require continuous reader capacity.

Move analytical queries to suitable replicas or warehouse as warranted solves a different operational problem where analytics scans repeatedly slow the OLTP RDS primary during peak hours. Read workload separation reduces transactional contention. It does not directly address retired snapshots from an old RDS database remain indefinitely. The case calls for Apply snapshot retention governance and delete approved obsolete copies, since unneeded snapshots keep incurring storage cost.

The competing option Measure query demand and migrate to a smaller suitable instance would be useful when a large legacy RDS instance has more storage and CPU than the dataset needs. Database right sizing aligns resources with workload. The present issue is retired snapshots from an old RDS database remain indefinitely; it is more directly addressed by Apply snapshot retention governance and delete approved obsolete copies. Unneeded snapshots keep incurring storage cost.

The option Add RDS Proxy and reusable connection pools addresses a separate condition: each microservice opens thousands of short-lived direct RDS connections. Connection pooling reduces memory/CPU cost of connection churn. The actual requirement is retired snapshots from an old RDS database remain indefinitely. Recommended corrective action: Apply snapshot retention governance and delete approved obsolete copies. Unneeded snapshots keep incurring storage cost.

The competing option Start/stop or choose a cost-appropriate analytic extract instead where supported would be useful when a read replica exists solely for a report run once monthly. Infrequent reports need not require continuous reader capacity. The present issue is each microservice opens thousands of short-lived direct RDS connections; it is more directly addressed by Add RDS Proxy and reusable connection pools. Connection pooling reduces memory/CPU cost of connection churn.

The option Measure query demand and migrate to a smaller suitable instance addresses a separate condition: a large legacy RDS instance has more storage and CPU than the dataset needs. Database right sizing aligns resources with workload. The actual requirement is each microservice opens thousands of short-lived direct RDS connections. Recommended corrective action: Add RDS Proxy and reusable connection pools. Connection pooling reduces memory/CPU cost of connection churn.

For the alternative failure retired snapshots from an old RDS database remain indefinitely, the control Apply snapshot retention governance and delete approved obsolete copies is relevant: unneeded snapshots keep incurring storage cost. Here the evidence instead is each microservice opens thousands of short-lived direct RDS connections. The selected technical mechanism is Add RDS Proxy and reusable connection pools: connection pooling reduces memory/CPU cost of connection churn.

The option Move analytical queries to suitable replicas or warehouse as warranted addresses a separate condition: analytics scans repeatedly slow the OLTP RDS primary during peak hours. Read workload separation reduces transactional contention. The actual requirement is a large legacy RDS instance has more storage and CPU than the dataset needs. Recommended corrective action: Measure query demand and migrate to a smaller suitable instance. Database right sizing aligns resources with workload.

Apply snapshot retention governance and delete approved obsolete copies would help if retired snapshots from an old RDS database remain indefinitely. Unneeded snapshots keep incurring storage cost. That is not the symptom described here: a large legacy RDS instance has more storage and CPU than the dataset needs. The matching response is Measure query demand and migrate to a smaller suitable instance, because database right sizing aligns resources with workload.

For the alternative failure a read replica exists solely for a report run once monthly, the control Start/stop or choose a cost-appropriate analytic extract instead where supported is relevant: infrequent reports need not require continuous reader capacity. Here the evidence instead is a large legacy RDS instance has more storage and CPU than the dataset needs. The selected technical mechanism is Measure query demand and migrate to a smaller suitable instance: database right sizing aligns resources with workload.