Amazon AWS Certified Solutions Architect - Associate SAA-C03 Bundle

The option Link FSx for Lustre to S3 data repositories where supported addresses a separate condition: a parallel analytics workload reads objects from S3 but needs a filesystem cache. S3 data-repository integration stages objects for high-performance filesystem access. The actual requirement is a ZFS workload depends on file snapshots and high-throughput low-latency access. Recommended corrective action: Evaluate Amazon FSx for OpenZFS with appropriate provisioning. Managed OpenZFS supports ZFS-oriented file workloads.

For the alternative failure windows clients must access centralized file shares using native SMB, the control Use FSx for Windows File Server and AD integration as appropriate is relevant: fSx for Windows supports SMB-compatible clients. Here the evidence instead is a ZFS workload depends on file snapshots and high-throughput low-latency access. The selected technical mechanism is Evaluate Amazon FSx for OpenZFS with appropriate provisioning: managed OpenZFS supports ZFS-oriented file workloads.

Use Amazon FSx for NetApp ONTAP when its features match requirements solves a different operational problem where an ONTAP enterprise workload needs NFS/SMB with snapshots and migration compatibility. FSx ONTAP provides managed ONTAP file capabilities. It does not directly address a ZFS workload depends on file snapshots and high-throughput low-latency access. The case calls for Evaluate Amazon FSx for OpenZFS with appropriate provisioning, since managed OpenZFS supports ZFS-oriented file workloads.

Use mount targets in the same AZ as each client where possible would help if compute instances in another AZ mount across zones and show extra path latency. AZ-local mount paths reduce cross-zone networking. That is not the symptom described here: an EFS filesystem has unpredictable bursts and is hitting the configured throughput ceiling. The matching response is Evaluate Elastic throughput or appropriate provisioned throughput mode for the usage, because eFS throughput modes govern elastic file throughput.

Choose the supported EFS performance mode based on latency and scale needs solves a different operational problem where a single file operation must minimize per-operation latency rather than aggregate throughput. File-system performance mode affects latency-versus-concurrency tradeoffs. It does not directly address an EFS filesystem has unpredictable bursts and is hitting the configured throughput ceiling. The case calls for Evaluate Elastic throughput or appropriate provisioned throughput mode for the usage, since eFS throughput modes govern elastic file throughput.

The competing option Optimize application directory operations and evaluate purpose-built file storage would be useful when millions of small metadata operations overwhelm the shared file tree. NFS metadata behavior can be a bottleneck. The present issue is an EFS filesystem has unpredictable bursts and is hitting the configured throughput ceiling; it is more directly addressed by Evaluate Elastic throughput or appropriate provisioned throughput mode for the usage. EFS throughput modes govern elastic file throughput.

For the alternative failure compute instances in another AZ mount across zones and show extra path latency, the control Use mount targets in the same AZ as each client where possible is relevant: aZ-local mount paths reduce cross-zone networking. Here the evidence instead is a single file operation must minimize per-operation latency rather than aggregate throughput. The selected technical mechanism is Choose the supported EFS performance mode based on latency and scale needs: file-system performance mode affects latency-versus-concurrency tradeoffs.

The competing option Use elastic Amazon EFS capacity rather than manually managed fixed EBS volumes would be useful when storage grows unpredictably and servers have no operational capacity for resizing disks. EFS scales capacity with file usage. The present issue is a single file operation must minimize per-operation latency rather than aggregate throughput; it is more directly addressed by Choose the supported EFS performance mode based on latency and scale needs. File-system performance mode affects latency-versus-concurrency tradeoffs.

The option Evaluate Elastic throughput or appropriate provisioned throughput mode for the usage addresses a separate condition: an EFS filesystem has unpredictable bursts and is hitting the configured throughput ceiling. EFS throughput modes govern elastic file throughput. The actual requirement is a single file operation must minimize per-operation latency rather than aggregate throughput. Choose the supported EFS performance mode based on latency and scale needs instead: File-system performance mode affects latency-versus-concurrency tradeoffs.

Optimize application directory operations and evaluate purpose-built file storage solves a different operational problem where millions of small metadata operations overwhelm the shared file tree. NFS metadata behavior can be a bottleneck. It does not directly address compute instances in another AZ mount across zones and show extra path latency. The case calls for Use mount targets in the same AZ as each client where possible, since aZ-local mount paths reduce cross-zone networking.

The option Choose the supported EFS performance mode based on latency and scale needs addresses a separate condition: a single file operation must minimize per-operation latency rather than aggregate throughput. File-system performance mode affects latency-versus-concurrency tradeoffs. The actual requirement is compute instances in another AZ mount across zones and show extra path latency. Recommended corrective action: Use mount targets in the same AZ as each client where possible. AZ-local mount paths reduce cross-zone networking.

Use elastic Amazon EFS capacity rather than manually managed fixed EBS volumes would help if storage grows unpredictably and servers have no operational capacity for resizing disks. EFS scales capacity with file usage. That is not the symptom described here: compute instances in another AZ mount across zones and show extra path latency. The matching response is Use mount targets in the same AZ as each client where possible, because aZ-local mount paths reduce cross-zone networking.

The competing option Evaluate Elastic throughput or appropriate provisioned throughput mode for the usage would be useful when an EFS filesystem has unpredictable bursts and is hitting the configured throughput ceiling. EFS throughput modes govern elastic file throughput. The present issue is millions of small metadata operations overwhelm the shared file tree; it is more directly addressed by Optimize application directory operations and evaluate purpose-built file storage. NFS metadata behavior can be a bottleneck.

Use elastic Amazon EFS capacity rather than manually managed fixed EBS volumes would help if storage grows unpredictably and servers have no operational capacity for resizing disks. EFS scales capacity with file usage. That is not the symptom described here: millions of small metadata operations overwhelm the shared file tree. The matching response is Optimize application directory operations and evaluate purpose-built file storage, because nFS metadata behavior can be a bottleneck.

For the alternative failure compute instances in another AZ mount across zones and show extra path latency, the control Use mount targets in the same AZ as each client where possible is relevant: aZ-local mount paths reduce cross-zone networking. Here the evidence instead is millions of small metadata operations overwhelm the shared file tree. The selected technical mechanism is Optimize application directory operations and evaluate purpose-built file storage: nFS metadata behavior can be a bottleneck.

The option Evaluate Elastic throughput or appropriate provisioned throughput mode for the usage addresses a separate condition: an EFS filesystem has unpredictable bursts and is hitting the configured throughput ceiling. EFS throughput modes govern elastic file throughput. The actual requirement is storage grows unpredictably and servers have no operational capacity for resizing disks. Recommended corrective action: Use elastic Amazon EFS capacity rather than manually managed fixed EBS volumes. EFS scales capacity with file usage.

Use mount targets in the same AZ as each client where possible would help if compute instances in another AZ mount across zones and show extra path latency. AZ-local mount paths reduce cross-zone networking. That is not the symptom described here: storage grows unpredictably and servers have no operational capacity for resizing disks. The matching response is Use elastic Amazon EFS capacity rather than manually managed fixed EBS volumes, because eFS scales capacity with file usage.

Optimize application directory operations and evaluate purpose-built file storage solves a different operational problem where millions of small metadata operations overwhelm the shared file tree. NFS metadata behavior can be a bottleneck. It does not directly address storage grows unpredictably and servers have no operational capacity for resizing disks. The case calls for Use elastic Amazon EFS capacity rather than manually managed fixed EBS volumes, since eFS scales capacity with file usage.

For the alternative failure a gp3 volume saturates throughput on large streaming reads, the control Increase gp3 throughput provisioning and validate instance limits is relevant: eBS throughput depends on volume and instance network limits. Here the evidence instead is a gp3 volume has adequate capacity but hits its provisioned IOPS ceiling. The selected technical mechanism is Increase gp3 provisioned IOPS within supported limits: gp3 performance settings can scale independently from volume size.

Initialize EBS volumes or use Fast Snapshot Restore where justified solves a different operational problem where after snapshot restoration the first read of cold blocks has high latency. Snapshot lazy loading can create first-read latency. It does not directly address a gp3 volume has adequate capacity but hits its provisioned IOPS ceiling. The case calls for Increase gp3 provisioned IOPS within supported limits, since gp3 performance settings can scale independently from volume size.

The competing option Choose an instance with sufficient EBS-optimized bandwidth would be useful when an instance's EBS bandwidth is lower than its attached volume aggregate. Instance-level EBS limits can bottleneck attached volume performance. The present issue is a gp3 volume has adequate capacity but hits its provisioned IOPS ceiling; it is more directly addressed by Increase gp3 provisioned IOPS within supported limits. gp3 performance settings can scale independently from volume size.

Choose an instance with sufficient EBS-optimized bandwidth solves a different operational problem where an instance's EBS bandwidth is lower than its attached volume aggregate. Instance-level EBS limits can bottleneck attached volume performance. It does not directly address a gp3 volume saturates throughput on large streaming reads. The case calls for Increase gp3 throughput provisioning and validate instance limits, since eBS throughput depends on volume and instance network limits.

The competing option Increase gp3 provisioned IOPS within supported limits would be useful when a gp3 volume has adequate capacity but hits its provisioned IOPS ceiling. gp3 performance settings can scale independently from volume size. The present issue is a gp3 volume saturates throughput on large streaming reads; it is more directly addressed by Increase gp3 throughput provisioning and validate instance limits. EBS throughput depends on volume and instance network limits.

The option Measure IOPS queue depth and choose suitable SSD volume class addresses a separate condition: a very large EBS volume is underutilized while I/O latency stays high. Size alone does not guarantee I/O performance. The actual requirement is a gp3 volume saturates throughput on large streaming reads. Recommended corrective action: Increase gp3 throughput provisioning and validate instance limits. EBS throughput depends on volume and instance network limits.

Increase gp3 throughput provisioning and validate instance limits solves a different operational problem where a gp3 volume saturates throughput on large streaming reads. EBS throughput depends on volume and instance network limits. It does not directly address a very large EBS volume is underutilized while I/O latency stays high. The case calls for Measure IOPS queue depth and choose suitable SSD volume class, since size alone does not guarantee I/O performance.

The option Choose an instance with sufficient EBS-optimized bandwidth addresses a separate condition: an instance's EBS bandwidth is lower than its attached volume aggregate. Instance-level EBS limits can bottleneck attached volume performance. The actual requirement is a very large EBS volume is underutilized while I/O latency stays high. Recommended corrective action: Measure IOPS queue depth and choose suitable SSD volume class. Size alone does not guarantee I/O performance.

Increase gp3 provisioned IOPS within supported limits would help if a gp3 volume has adequate capacity but hits its provisioned IOPS ceiling. gp3 performance settings can scale independently from volume size. That is not the symptom described here: a very large EBS volume is underutilized while I/O latency stays high. The matching response is Measure IOPS queue depth and choose suitable SSD volume class, because size alone does not guarantee I/O performance.

The competing option Increase gp3 throughput provisioning and validate instance limits would be useful when a gp3 volume saturates throughput on large streaming reads. EBS throughput depends on volume and instance network limits. The present issue is after snapshot restoration the first read of cold blocks has high latency; it is more directly addressed by Initialize EBS volumes or use Fast Snapshot Restore where justified. Snapshot lazy loading can create first-read latency.

Measure IOPS queue depth and choose suitable SSD volume class would help if a very large EBS volume is underutilized while I/O latency stays high. Size alone does not guarantee I/O performance. That is not the symptom described here: after snapshot restoration the first read of cold blocks has high latency. The matching response is Initialize EBS volumes or use Fast Snapshot Restore where justified, because snapshot lazy loading can create first-read latency.

For the alternative failure a gp3 volume has adequate capacity but hits its provisioned IOPS ceiling, the control Increase gp3 provisioned IOPS within supported limits is relevant: gp3 performance settings can scale independently from volume size. Here the evidence instead is after snapshot restoration the first read of cold blocks has high latency. The selected technical mechanism is Initialize EBS volumes or use Fast Snapshot Restore where justified: snapshot lazy loading can create first-read latency.

The option Measure IOPS queue depth and choose suitable SSD volume class addresses a separate condition: a very large EBS volume is underutilized while I/O latency stays high. Size alone does not guarantee I/O performance. The actual requirement is an instance's EBS bandwidth is lower than its attached volume aggregate. Choose an instance with sufficient EBS-optimized bandwidth instead: Instance-level EBS limits can bottleneck attached volume performance.

Increase gp3 throughput provisioning and validate instance limits would help if a gp3 volume saturates throughput on large streaming reads. EBS throughput depends on volume and instance network limits. That is not the symptom described here: an instance's EBS bandwidth is lower than its attached volume aggregate. The matching response is Choose an instance with sufficient EBS-optimized bandwidth, because instance-level EBS limits can bottleneck attached volume performance.

For the alternative failure a gp3 volume has adequate capacity but hits its provisioned IOPS ceiling, the control Increase gp3 provisioned IOPS within supported limits is relevant: gp3 performance settings can scale independently from volume size. Here the evidence instead is an instance's EBS bandwidth is lower than its attached volume aggregate. The selected technical mechanism is Choose an instance with sufficient EBS-optimized bandwidth: instance-level EBS limits can bottleneck attached volume performance.