Amazon AWS Certified Solutions Architect - Associate SAA-C03 Bundle

Evaluate gp3 or reduced provisioned performance after load testing would help if the DB has a large provisioned IOPS tier but observed IOPS is low. Overprovisioned IOPS increases storage cost. That is not the symptom described here: teams retain full database snapshots far beyond the required restore window. The matching response is Enforce backup-retention policy and lifecycle cleanup, because retention controls prevent unnecessary snapshot charges.

For the alternative failure a low-value internal test database does not need a full-time Multi-AZ standby, the control Use a suitable Single-AZ test instance with tested backups is relevant: relaxed availability requirements permit lower-cost deployment. Here the evidence instead is teams retain full database snapshots far beyond the required restore window. The selected technical mechanism is Enforce backup-retention policy and lifecycle cleanup: retention controls prevent unnecessary snapshot charges.

Add read replicas only if sustained reporting load merits their cost solves a different operational problem where analysts need separate read scaling from the production writer. Replicas increase serving capacity but incur separate resources. It does not directly address teams retain full database snapshots far beyond the required restore window. The case calls for Enforce backup-retention policy and lifecycle cleanup, since retention controls prevent unnecessary snapshot charges.

Scale down reader capacity while preserving resilience requirements would help if an Aurora cluster has many unused small read replicas after traffic drops. Reader count should match real read demand. That is not the symptom described here: dynamoDB items have a one-year expiry but application code never removes them. The matching response is Configure DynamoDB TTL on an appropriate timestamp attribute, because tTL eventually deletes expired items and reduces storage accumulation.

Set a compliant shorter retention after confirming recovery objectives solves a different operational problem where a database's backup retention is 35 days although policy only needs 7. Reducing retained recovery points can reduce backup charges. It does not directly address dynamoDB items have a one-year expiry but application code never removes them. The case calls for Configure DynamoDB TTL on an appropriate timestamp attribute, since tTL eventually deletes expired items and reduces storage accumulation.

The competing option Right-size ElastiCache node count/size and appropriate eviction policy would be useful when a cache is deployed with excessive nodes for infrequently requested data. Cache capacity should be based on measured hot working set. The present issue is dynamoDB items have a one-year expiry but application code never removes them; it is more directly addressed by Configure DynamoDB TTL on an appropriate timestamp attribute. TTL eventually deletes expired items and reduces storage accumulation.

For the alternative failure an Aurora cluster has many unused small read replicas after traffic drops, the control Scale down reader capacity while preserving resilience requirements is relevant: reader count should match real read demand. Here the evidence instead is the application scans DynamoDB daily to find items by secondary attribute. The selected technical mechanism is Add a suitable GSI and use Query instead of Scan: efficient key access reduces read capacity consumption.

Configure DynamoDB TTL on an appropriate timestamp attribute solves a different operational problem where dynamoDB items have a one-year expiry but application code never removes them. TTL eventually deletes expired items and reduces storage accumulation. It does not directly address the application scans DynamoDB daily to find items by secondary attribute. The case calls for Add a suitable GSI and use Query instead of Scan, since efficient key access reduces read capacity consumption.

The option Set a compliant shorter retention after confirming recovery objectives addresses a separate condition: a database's backup retention is 35 days although policy only needs 7. Reducing retained recovery points can reduce backup charges. The actual requirement is the application scans DynamoDB daily to find items by secondary attribute. Recommended corrective action: Add a suitable GSI and use Query instead of Scan. Efficient key access reduces read capacity consumption.

Add a suitable GSI and use Query instead of Scan solves a different operational problem where the application scans DynamoDB daily to find items by secondary attribute. Efficient key access reduces read capacity consumption. It does not directly address an Aurora cluster has many unused small read replicas after traffic drops. The case calls for Scale down reader capacity while preserving resilience requirements, since reader count should match real read demand.

The competing option Right-size ElastiCache node count/size and appropriate eviction policy would be useful when a cache is deployed with excessive nodes for infrequently requested data. Cache capacity should be based on measured hot working set. The present issue is an Aurora cluster has many unused small read replicas after traffic drops; it is more directly addressed by Scale down reader capacity while preserving resilience requirements. Reader count should match real read demand.

The option Set a compliant shorter retention after confirming recovery objectives addresses a separate condition: a database's backup retention is 35 days although policy only needs 7. Reducing retained recovery points can reduce backup charges. The actual requirement is an Aurora cluster has many unused small read replicas after traffic drops. Recommended corrective action: Scale down reader capacity while preserving resilience requirements. Reader count should match real read demand.

The option Scale down reader capacity while preserving resilience requirements addresses a separate condition: an Aurora cluster has many unused small read replicas after traffic drops. Reader count should match real read demand. The actual requirement is a cache is deployed with excessive nodes for infrequently requested data. Recommended corrective action: Right-size ElastiCache node count/size and appropriate eviction policy. Cache capacity should be based on measured hot working set.

Set a compliant shorter retention after confirming recovery objectives would help if a database's backup retention is 35 days although policy only needs 7. Reducing retained recovery points can reduce backup charges. That is not the symptom described here: a cache is deployed with excessive nodes for infrequently requested data. The matching response is Right-size ElastiCache node count/size and appropriate eviction policy, because cache capacity should be based on measured hot working set.

For the alternative failure the application scans DynamoDB daily to find items by secondary attribute, the control Add a suitable GSI and use Query instead of Scan is relevant: efficient key access reduces read capacity consumption. Here the evidence instead is a cache is deployed with excessive nodes for infrequently requested data. The selected technical mechanism is Right-size ElastiCache node count/size and appropriate eviction policy: cache capacity should be based on measured hot working set.

Add a suitable GSI and use Query instead of Scan would help if the application scans DynamoDB daily to find items by secondary attribute. Efficient key access reduces read capacity consumption. That is not the symptom described here: a database's backup retention is 35 days although policy only needs 7. The matching response is Set a compliant shorter retention after confirming recovery objectives, because reducing retained recovery points can reduce backup charges.

For the alternative failure dynamoDB items have a one-year expiry but application code never removes them, the control Configure DynamoDB TTL on an appropriate timestamp attribute is relevant: tTL eventually deletes expired items and reduces storage accumulation. Here the evidence instead is a database's backup retention is 35 days although policy only needs 7. The selected technical mechanism is Set a compliant shorter retention after confirming recovery objectives: reducing retained recovery points can reduce backup charges.

Scale down reader capacity while preserving resilience requirements solves a different operational problem where an Aurora cluster has many unused small read replicas after traffic drops. Reader count should match real read demand. It does not directly address a database's backup retention is 35 days although policy only needs 7. The case calls for Set a compliant shorter retention after confirming recovery objectives, since reducing retained recovery points can reduce backup charges.

Profile actual source workload and right-size the migration target would help if migration testing fails because the target DB is sized much larger than needed. Sizing to evidence avoids permanent overprovisioning. That is not the symptom described here: a managed MySQL database is migrated to RDS MySQL without changing schema. The matching response is Use AWS DMS for compatible homogeneous data movement, because dMS supports database migration with ongoing replication options.

For the alternative failure a one-time small SQL database transfer is planned but teams provision a permanent DMS fleet, the control Compare snapshot/backup restore or native migration tools for small one-off moves is relevant: one-time migrations may not justify ongoing infrastructure. Here the evidence instead is a managed MySQL database is migrated to RDS MySQL without changing schema. The selected technical mechanism is Use AWS DMS for compatible homogeneous data movement: dMS supports database migration with ongoing replication options.

Use AWS Schema Conversion Tool with AWS DMS as appropriate solves a different operational problem where a proprietary database must move to PostgreSQL with schema conversion. Heterogeneous migration requires schema translation and data movement. It does not directly address a managed MySQL database is migrated to RDS MySQL without changing schema. The case calls for Use AWS DMS for compatible homogeneous data movement, since dMS supports database migration with ongoing replication options.

For the alternative failure a managed MySQL database is migrated to RDS MySQL without changing schema, the control Use AWS DMS for compatible homogeneous data movement is relevant: dMS supports database migration with ongoing replication options. Here the evidence instead is a proprietary database must move to PostgreSQL with schema conversion. The selected technical mechanism is Use AWS Schema Conversion Tool with AWS DMS as appropriate: heterogeneous migration requires schema translation and data movement.

The competing option Profile actual source workload and right-size the migration target would be useful when migration testing fails because the target DB is sized much larger than needed. Sizing to evidence avoids permanent overprovisioning. The present issue is a proprietary database must move to PostgreSQL with schema conversion; it is more directly addressed by Use AWS Schema Conversion Tool with AWS DMS as appropriate. Heterogeneous migration requires schema translation and data movement.

The option Scope replication tasks and stop resources after cutover addresses a separate condition: large ongoing DMS replication traffic unexpectedly costs more than planned. Continuous replication should end when no longer required. The actual requirement is a proprietary database must move to PostgreSQL with schema conversion. Recommended corrective action: Use AWS Schema Conversion Tool with AWS DMS as appropriate. Heterogeneous migration requires schema translation and data movement.

Use AWS Schema Conversion Tool with AWS DMS as appropriate solves a different operational problem where a proprietary database must move to PostgreSQL with schema conversion. Heterogeneous migration requires schema translation and data movement. It does not directly address a one-time small SQL database transfer is planned but teams provision a permanent DMS fleet. The case calls for Compare snapshot/backup restore or native migration tools for small one-off moves, since one-time migrations may not justify ongoing infrastructure.

The competing option Use AWS DMS for compatible homogeneous data movement would be useful when a managed MySQL database is migrated to RDS MySQL without changing schema. DMS supports database migration with ongoing replication options. The present issue is a one-time small SQL database transfer is planned but teams provision a permanent DMS fleet; it is more directly addressed by Compare snapshot/backup restore or native migration tools for small one-off moves. One-time migrations may not justify ongoing infrastructure.

Scope replication tasks and stop resources after cutover would help if large ongoing DMS replication traffic unexpectedly costs more than planned. Continuous replication should end when no longer required. That is not the symptom described here: a one-time small SQL database transfer is planned but teams provision a permanent DMS fleet. The matching response is Compare snapshot/backup restore or native migration tools for small one-off moves, because one-time migrations may not justify ongoing infrastructure.

The competing option Compare snapshot/backup restore or native migration tools for small one-off moves would be useful when a one-time small SQL database transfer is planned but teams provision a permanent DMS fleet. One-time migrations may not justify ongoing infrastructure. The present issue is migration testing fails because the target DB is sized much larger than needed; it is more directly addressed by Profile actual source workload and right-size the migration target. Sizing to evidence avoids permanent overprovisioning.

The option Scope replication tasks and stop resources after cutover addresses a separate condition: large ongoing DMS replication traffic unexpectedly costs more than planned. Continuous replication should end when no longer required. The actual requirement is migration testing fails because the target DB is sized much larger than needed. Recommended corrective action: Profile actual source workload and right-size the migration target. Sizing to evidence avoids permanent overprovisioning.

For the alternative failure a managed MySQL database is migrated to RDS MySQL without changing schema, the control Use AWS DMS for compatible homogeneous data movement is relevant: dMS supports database migration with ongoing replication options. Here the evidence instead is migration testing fails because the target DB is sized much larger than needed. The selected technical mechanism is Profile actual source workload and right-size the migration target: sizing to evidence avoids permanent overprovisioning.

The option Use AWS Schema Conversion Tool with AWS DMS as appropriate addresses a separate condition: a proprietary database must move to PostgreSQL with schema conversion. Heterogeneous migration requires schema translation and data movement. The actual requirement is large ongoing DMS replication traffic unexpectedly costs more than planned. Recommended corrective action: Scope replication tasks and stop resources after cutover. Continuous replication should end when no longer required.

Use AWS DMS for compatible homogeneous data movement would help if a managed MySQL database is migrated to RDS MySQL without changing schema. DMS supports database migration with ongoing replication options. That is not the symptom described here: large ongoing DMS replication traffic unexpectedly costs more than planned. The matching response is Scope replication tasks and stop resources after cutover, because continuous replication should end when no longer required.

For the alternative failure a one-time small SQL database transfer is planned but teams provision a permanent DMS fleet, the control Compare snapshot/backup restore or native migration tools for small one-off moves is relevant: one-time migrations may not justify ongoing infrastructure. Here the evidence instead is large ongoing DMS replication traffic unexpectedly costs more than planned. The selected technical mechanism is Scope replication tasks and stop resources after cutover: continuous replication should end when no longer required.