Amazon AWS Certified Solutions Architect - Associate SAA-C03 Bundle

The option Evaluate EBS sc1 for eligible cold sequential workloads addresses a separate condition: a cold HDD archival scan workload only needs infrequent sequential throughput. Cold HDD tiers have lower cost with limited IOPS. The actual requirement is a snapshot collection references deleted EC2 instances but remains retained. Recommended corrective action: Audit and expire orphaned EBS snapshots according to recovery policy. Snapshots incur ongoing storage charges until removed.

For the alternative failure a general-purpose SSD volume is oversized merely to obtain more performance, the control Migrate to gp3 and provision IOPS/throughput separately within limits is relevant: gp3 performance can be specified independently of GB. Here the evidence instead is a snapshot collection references deleted EC2 instances but remains retained. The selected technical mechanism is Audit and expire orphaned EBS snapshots according to recovery policy: snapshots incur ongoing storage charges until removed.

Use io2 only where latency/IOPS demands justify its price solves a different operational problem where a database requires consistent very high random IOPS. High-performance SSD costs more than general purpose. It does not directly address a snapshot collection references deleted EC2 instances but remains retained. The case calls for Audit and expire orphaned EBS snapshots according to recovery policy, since snapshots incur ongoing storage charges until removed.

For the alternative failure an existing File Gateway is repeatedly fetching large cold objects and the on-prem cache is undersized, the control Right-size File Gateway local cache against the observed hot working set is relevant: a correctly sized local cache reduces repeated remote fetches. Here the evidence instead is a data center must move 100 TB over an available high-throughput link. The selected technical mechanism is Use AWS DataSync rather than manual per-file uploads: dataSync automates efficient managed transfers.

Evaluate AWS Data Transfer Terminal, online DataSync or approved partner transfer against the migration bandwidth and location solves a different operational problem where the organization is budgeting a new petabyte-scale migration based on ordering Snowball devices despite the new-customer service restriction. New Snow Family device orders are unavailable to new customers; alternatives must match transfer constraints. It does not directly address a data center must move 100 TB over an available high-throughput link. The case calls for Use AWS DataSync rather than manual per-file uploads, since dataSync automates efficient managed transfers.

The competing option Enable incremental change detection in DataSync or an appropriate sync process would be useful when a copy job repeatedly moves unchanged files between file stores every night. Incremental transfers avoid recopying identical content. The present issue is a data center must move 100 TB over an available high-throughput link; it is more directly addressed by Use AWS DataSync rather than manual per-file uploads. DataSync automates efficient managed transfers.

For the alternative failure a data center must move 100 TB over an available high-throughput link, the control Use AWS DataSync rather than manual per-file uploads is relevant: dataSync automates efficient managed transfers. Here the evidence instead is the organization is budgeting a new petabyte-scale migration based on ordering Snowball devices despite the new-customer service restriction. The selected technical mechanism is Evaluate AWS Data Transfer Terminal, online DataSync or approved partner transfer against the migration bandwidth and location: new Snow Family device orders are unavailable to new customers; alternatives must match transfer constraints.

The competing option Right-size File Gateway local cache against the observed hot working set would be useful when an existing File Gateway is repeatedly fetching large cold objects and the on-prem cache is undersized. A correctly sized local cache reduces repeated remote fetches. The present issue is the organization is budgeting a new petabyte-scale migration based on ordering Snowball devices despite the new-customer service restriction; it is more directly addressed by Evaluate AWS Data Transfer Terminal, online DataSync or approved partner transfer against the migration bandwidth and location. New Snow Family device orders are unavailable to new customers; alternatives must match transfer constraints.

The option Enable incremental change detection in DataSync or an appropriate sync process addresses a separate condition: a copy job repeatedly moves unchanged files between file stores every night. Incremental transfers avoid recopying identical content. The actual requirement is the organization is budgeting a new petabyte-scale migration based on ordering Snowball devices despite the new-customer service restriction. Recommended corrective action: Evaluate AWS Data Transfer Terminal, online DataSync or approved partner transfer against the migration bandwidth and location. New Snow Family device orders are unavailable to new customers; alternatives must match transfer constraints.

Use AWS DataSync rather than manual per-file uploads solves a different operational problem where a data center must move 100 TB over an available high-throughput link. DataSync automates efficient managed transfers. It does not directly address an existing File Gateway is repeatedly fetching large cold objects and the on-prem cache is undersized. The case calls for Right-size File Gateway local cache against the observed hot working set, since a correctly sized local cache reduces repeated remote fetches.

The option Use managed FSx for Windows rather than rebuilding SMB file servers addresses a separate condition: most applications must continue using SMB to access file data after AWS migration. Managed file service removes custom SMB host management. The actual requirement is an existing File Gateway is repeatedly fetching large cold objects and the on-prem cache is undersized. Recommended corrective action: Right-size File Gateway local cache against the observed hot working set. A correctly sized local cache reduces repeated remote fetches.

Enable incremental change detection in DataSync or an appropriate sync process would help if a copy job repeatedly moves unchanged files between file stores every night. Incremental transfers avoid recopying identical content. That is not the symptom described here: an existing File Gateway is repeatedly fetching large cold objects and the on-prem cache is undersized. The matching response is Right-size File Gateway local cache against the observed hot working set, because a correctly sized local cache reduces repeated remote fetches.

The competing option Right-size File Gateway local cache against the observed hot working set would be useful when an existing File Gateway is repeatedly fetching large cold objects and the on-prem cache is undersized. A correctly sized local cache reduces repeated remote fetches. The present issue is most applications must continue using SMB to access file data after AWS migration; it is more directly addressed by Use managed FSx for Windows rather than rebuilding SMB file servers. Managed file service removes custom SMB host management.

The option Enable incremental change detection in DataSync or an appropriate sync process addresses a separate condition: a copy job repeatedly moves unchanged files between file stores every night. Incremental transfers avoid recopying identical content. The actual requirement is most applications must continue using SMB to access file data after AWS migration. Recommended corrective action: Use managed FSx for Windows rather than rebuilding SMB file servers. Managed file service removes custom SMB host management.

Use AWS DataSync rather than manual per-file uploads would help if a data center must move 100 TB over an available high-throughput link. DataSync automates efficient managed transfers. That is not the symptom described here: most applications must continue using SMB to access file data after AWS migration. The matching response is Use managed FSx for Windows rather than rebuilding SMB file servers, because managed file service removes custom SMB host management.

The option Right-size File Gateway local cache against the observed hot working set addresses a separate condition: an existing File Gateway is repeatedly fetching large cold objects and the on-prem cache is undersized. A correctly sized local cache reduces repeated remote fetches. The actual requirement is a copy job repeatedly moves unchanged files between file stores every night. Recommended corrective action: Enable incremental change detection in DataSync or an appropriate sync process. Incremental transfers avoid recopying identical content.

Use AWS DataSync rather than manual per-file uploads would help if a data center must move 100 TB over an available high-throughput link. DataSync automates efficient managed transfers. That is not the symptom described here: a copy job repeatedly moves unchanged files between file stores every night. The matching response is Enable incremental change detection in DataSync or an appropriate sync process, because incremental transfers avoid recopying identical content.

Use managed FSx for Windows rather than rebuilding SMB file servers solves a different operational problem where most applications must continue using SMB to access file data after AWS migration. Managed file service removes custom SMB host management. It does not directly address a copy job repeatedly moves unchanged files between file stores every night. The case calls for Enable incremental change detection in DataSync or an appropriate sync process, since incremental transfers avoid recopying identical content.

Keep active query datasets in immediately accessible storage classes would help if frequently scanned Parquet tables have been moved to deep archive and analysts wait hours. Archive retrieval latency defeats interactive analytics. That is not the symptom described here: rarely queried raw logs are kept indefinitely in S3 Standard. The matching response is Apply lifecycle movement to suitable IA/Glacier classes after a defined age, because cold tiers reduce storage cost when retrieval is rare.

Use S3 lifecycle expiration with Object Lock/hold validation solves a different operational problem where archive data must be deleted after retention but current process uses manual reminders. Automated expiration controls storage growth after retention. It does not directly address rarely queried raw logs are kept indefinitely in S3 Standard. The case calls for Apply lifecycle movement to suitable IA/Glacier classes after a defined age, since cold tiers reduce storage cost when retrieval is rare.

The competing option Evaluate S3 for object-oriented access instead of managed file storage would be useful when customers require instant read access but file storage was selected without POSIX need. Object storage may be cheaper when file semantics are unnecessary. The present issue is rarely queried raw logs are kept indefinitely in S3 Standard; it is more directly addressed by Apply lifecycle movement to suitable IA/Glacier classes after a defined age. Cold tiers reduce storage cost when retrieval is rare.

For the alternative failure rarely queried raw logs are kept indefinitely in S3 Standard, the control Apply lifecycle movement to suitable IA/Glacier classes after a defined age is relevant: cold tiers reduce storage cost when retrieval is rare. Here the evidence instead is frequently scanned Parquet tables have been moved to deep archive and analysts wait hours. The selected technical mechanism is Keep active query datasets in immediately accessible storage classes: archive retrieval latency defeats interactive analytics.

Use S3 lifecycle expiration with Object Lock/hold validation solves a different operational problem where archive data must be deleted after retention but current process uses manual reminders. Automated expiration controls storage growth after retention. It does not directly address frequently scanned Parquet tables have been moved to deep archive and analysts wait hours. The case calls for Keep active query datasets in immediately accessible storage classes, since archive retrieval latency defeats interactive analytics.

The competing option Use scoped S3 access or OneLake-style references where supported rather than copying would be useful when a large dataset must be shared among consumers who are duplicating it daily. Shared object access prevents unnecessary duplicates. The present issue is frequently scanned Parquet tables have been moved to deep archive and analysts wait hours; it is more directly addressed by Keep active query datasets in immediately accessible storage classes. Archive retrieval latency defeats interactive analytics.

Keep active query datasets in immediately accessible storage classes solves a different operational problem where frequently scanned Parquet tables have been moved to deep archive and analysts wait hours. Archive retrieval latency defeats interactive analytics. It does not directly address a large dataset must be shared among consumers who are duplicating it daily. The case calls for Use scoped S3 access or OneLake-style references where supported rather than copying, since shared object access prevents unnecessary duplicates.

The competing option Use S3 lifecycle expiration with Object Lock/hold validation would be useful when archive data must be deleted after retention but current process uses manual reminders. Automated expiration controls storage growth after retention. The present issue is a large dataset must be shared among consumers who are duplicating it daily; it is more directly addressed by Use scoped S3 access or OneLake-style references where supported rather than copying. Shared object access prevents unnecessary duplicates.

Evaluate S3 for object-oriented access instead of managed file storage would help if customers require instant read access but file storage was selected without POSIX need. Object storage may be cheaper when file semantics are unnecessary. That is not the symptom described here: a large dataset must be shared among consumers who are duplicating it daily. The matching response is Use scoped S3 access or OneLake-style references where supported rather than copying, because shared object access prevents unnecessary duplicates.

The competing option Use scoped S3 access or OneLake-style references where supported rather than copying would be useful when a large dataset must be shared among consumers who are duplicating it daily. Shared object access prevents unnecessary duplicates. The present issue is customers require instant read access but file storage was selected without POSIX need; it is more directly addressed by Evaluate S3 for object-oriented access instead of managed file storage. Object storage may be cheaper when file semantics are unnecessary.

The option Use S3 lifecycle expiration with Object Lock/hold validation addresses a separate condition: archive data must be deleted after retention but current process uses manual reminders. Automated expiration controls storage growth after retention. The actual requirement is customers require instant read access but file storage was selected without POSIX need. Recommended corrective action: Evaluate S3 for object-oriented access instead of managed file storage. Object storage may be cheaper when file semantics are unnecessary.

For the alternative failure frequently scanned Parquet tables have been moved to deep archive and analysts wait hours, the control Keep active query datasets in immediately accessible storage classes is relevant: archive retrieval latency defeats interactive analytics. Here the evidence instead is customers require instant read access but file storage was selected without POSIX need. The selected technical mechanism is Evaluate S3 for object-oriented access instead of managed file storage: object storage may be cheaper when file semantics are unnecessary.

Apply lifecycle movement to suitable IA/Glacier classes after a defined age would help if rarely queried raw logs are kept indefinitely in S3 Standard. Cold tiers reduce storage cost when retrieval is rare. That is not the symptom described here: archive data must be deleted after retention but current process uses manual reminders. The matching response is Use S3 lifecycle expiration with Object Lock/hold validation, because automated expiration controls storage growth after retention.

For the alternative failure frequently scanned Parquet tables have been moved to deep archive and analysts wait hours, the control Keep active query datasets in immediately accessible storage classes is relevant: archive retrieval latency defeats interactive analytics. Here the evidence instead is archive data must be deleted after retention but current process uses manual reminders. The selected technical mechanism is Use S3 lifecycle expiration with Object Lock/hold validation: automated expiration controls storage growth after retention.

Use scoped S3 access or OneLake-style references where supported rather than copying solves a different operational problem where a large dataset must be shared among consumers who are duplicating it daily. Shared object access prevents unnecessary duplicates. It does not directly address archive data must be deleted after retention but current process uses manual reminders. The case calls for Use S3 lifecycle expiration with Object Lock/hold validation, since automated expiration controls storage growth after retention.