Amazon AWS Certified Solutions Architect - Professional SAP-C02 Bundle

Although Choose Amazon FSx for Lustre with an appropriate deployment and S3 integration can be valid when A distributed HPC application needs very high throughput parallel file access and optional S3 data repository integration, it targets a different technical condition. Lustre is designed for parallel high-performance file workloads.

Select EBS Provisioned IOPS SSD with performance sizing validated against workload needs would address a different requirement: A relational database on EC2 needs consistently provisioned high random IOPS on attached block storage. Provisioned IOPS EBS supplies independently configured block IOPS and throughput.

This alternative, Increase FSx for Windows throughput capacity after measuring client I/O and workload limits, is appropriate when An existing FSx for Windows File Server share has adequate storage but SMB clients saturate file throughput during daily processing. File-system throughput capacity is provisioned separately from total stored file capacity.

This alternative, Redesign the partition key to distribute writes across shards, is appropriate when A streaming service hotspots one Kinesis shard because all event keys are identical. Kinesis throughput is limited per shard so hot keys throttle regardless of total capacity.

For the separate situation An event pipeline must deliver records to S3 with buffering and transformation but without managing consumer servers, Use Amazon Data Firehose with destination buffering and supported transformation could be useful. Firehose provides managed delivery from streams into analytics storage.

Although Use Amazon MSK with appropriately sized broker and storage architecture can be valid when A team migrates a Kafka ecosystem that depends on Kafka APIs and broker compatibility, it targets a different technical condition. Managed Kafka retains Kafka client compatibility while AWS manages cluster operations.

For the separate situation Millions of event records per minute require ordered processing within partitions and multiple independent consumers, Use Amazon Kinesis Data Streams with designed partition-key cardinality could be useful. Kinesis streams preserve per-shard ordering and retain events for consumer replay.

Use S3 landing plus scheduled batch jobs rather than maintaining always-on stream consumers solves the related scenario An hourly log pipeline can tolerate deferred batch ingestion rather than real-time streaming; batch ingest lowers continuous processing overhead when latency constraints allow.

Redesign the partition key to distribute writes across shards would address a different requirement: A streaming service hotspots one Kinesis shard because all event keys are identical. Kinesis throughput is limited per shard so hot keys throttle regardless of total capacity.

Use S3 landing plus scheduled batch jobs rather than maintaining always-on stream consumers solves the related scenario An hourly log pipeline can tolerate deferred batch ingestion rather than real-time streaming; batch ingest lowers continuous processing overhead when latency constraints allow.

Redesign the partition key to distribute writes across shards would address a different requirement: A streaming service hotspots one Kinesis shard because all event keys are identical. Kinesis throughput is limited per shard so hot keys throttle regardless of total capacity.

This alternative, Use Amazon Data Firehose with destination buffering and supported transformation, is appropriate when An event pipeline must deliver records to S3 with buffering and transformation but without managing consumer servers. Firehose provides managed delivery from streams into analytics storage.

Use Amazon MSK with appropriately sized broker and storage architecture would address a different requirement: A team migrates a Kafka ecosystem that depends on Kafka APIs and broker compatibility. Managed Kafka retains Kafka client compatibility while AWS manages cluster operations.

This alternative, Use Amazon Kinesis Data Streams with designed partition-key cardinality, is appropriate when Millions of event records per minute require ordered processing within partitions and multiple independent consumers. Kinesis streams preserve per-shard ordering and retain events for consumer replay.

For the separate situation An event pipeline must deliver records to S3 with buffering and transformation but without managing consumer servers, Use Amazon Data Firehose with destination buffering and supported transformation could be useful. Firehose provides managed delivery from streams into analytics storage.

Use Amazon Kinesis Data Streams with designed partition-key cardinality would address a different requirement: Millions of event records per minute require ordered processing within partitions and multiple independent consumers. Kinesis streams preserve per-shard ordering and retain events for consumer replay.

This alternative, Use Amazon Data Firehose with destination buffering and supported transformation, is appropriate when An event pipeline must deliver records to S3 with buffering and transformation but without managing consumer servers. Firehose provides managed delivery from streams into analytics storage.

Use S3 landing plus scheduled batch jobs rather than maintaining always-on stream consumers solves the related scenario An hourly log pipeline can tolerate deferred batch ingestion rather than real-time streaming; batch ingest lowers continuous processing overhead when latency constraints allow.

Use Spot capacity with resilient interruption processing and mixed-instance policies solves the related scenario A batch rendering fleet can checkpoint work every few minutes and has flexible deadlines; interruptible discounts fit recoverable work that can resume on replacement nodes.

Although Reduce instance size after load testing and monitor new headroom can be valid when An underused application instance is twice as large as its measured peak requirements, it targets a different technical condition. Rightsizing removes paid idle CPU and memory before buying discounts.

Use Lambda or an appropriate scale-to-zero managed platform would address a different requirement: An infrequent event-driven API stays idle most of the day. Per-request compute avoids continuous provisioned idle capacity.

Reduce instance size after load testing and monitor new headroom solves the related scenario An underused application instance is twice as large as its measured peak requirements; rightsizing removes paid idle CPU and memory before buying discounts.

Although Reevaluate coverage and eligible architecture before renewing commitments can be valid when A baseline compute commitment is near expiration after a migration to Graviton, it targets a different technical condition. Compute purchasing should reflect the future instance mix, not historic usage.

Commit only the stable baseline to Savings Plans and keep bursts on elastic on-demand capacity would address a different requirement: A new service has stable compute usage at night but highly volatile daytime traffic. Splitting baseline and burst avoids overcommitting to uncertain peak usage.

Although Reevaluate coverage and eligible architecture before renewing commitments can be valid when A baseline compute commitment is near expiration after a migration to Graviton, it targets a different technical condition. Compute purchasing should reflect the future instance mix, not historic usage.

Commit only the stable baseline to Savings Plans and keep bursts on elastic on-demand capacity would address a different requirement: A new service has stable compute usage at night but highly volatile daytime traffic. Splitting baseline and burst avoids overcommitting to uncertain peak usage.

For the separate situation An infrequent event-driven API stays idle most of the day, Use Lambda or an appropriate scale-to-zero managed platform could be useful. Per-request compute avoids continuous provisioned idle capacity.

Use Spot capacity with resilient interruption processing and mixed-instance policies would address a different requirement: A batch rendering fleet can checkpoint work every few minutes and has flexible deadlines. Interruptible discounts fit recoverable work that can resume on replacement nodes.

This alternative, Reduce instance size after load testing and monitor new headroom, is appropriate when An underused application instance is twice as large as its measured peak requirements. Rightsizing removes paid idle CPU and memory before buying discounts.

Reevaluate coverage and eligible architecture before renewing commitments solves the related scenario A baseline compute commitment is near expiration after a migration to Graviton; compute purchasing should reflect the future instance mix, not historic usage.

This alternative, Use Spot capacity with resilient interruption processing and mixed-instance policies, is appropriate when A batch rendering fleet can checkpoint work every few minutes and has flexible deadlines. Interruptible discounts fit recoverable work that can resume on replacement nodes.

For the separate situation An underused application instance is twice as large as its measured peak requirements, Reduce instance size after load testing and monitor new headroom could be useful. Rightsizing removes paid idle CPU and memory before buying discounts.

Commit only the stable baseline to Savings Plans and keep bursts on elastic on-demand capacity solves the related scenario A new service has stable compute usage at night but highly volatile daytime traffic; splitting baseline and burst avoids overcommitting to uncertain peak usage.