Amazon AWS Certified Solutions Architect - Associate SAA-C03 Bundle

Choose Amazon ElastiCache for Redis-compatible engines or MemoryDB as durability requires would help if application code requires an in-memory Redis-compatible data structure. Managed Redis-compatible engines support structured in-memory access. That is not the symptom described here: a surge of cache misses overwhelms Aurora. The matching response is Add request coalescing and cache warming with bounded origin calls, because thundering herd mitigation protects underlying databases.

For the alternative failure a product catalog repeatedly runs the same read queries with no near-term updates, the control Cache query results in ElastiCache with a bounded TTL is relevant: hot cache hits avoid repeated database work. Here the evidence instead is a surge of cache misses overwhelms Aurora. The selected technical mechanism is Add request coalescing and cache warming with bounded origin calls: thundering herd mitigation protects underlying databases.

Invalidate or update cache entries on price mutations solves a different operational problem where product prices change and cached responses stay stale for hours. Cache consistency depends on update policy. It does not directly address a surge of cache misses overwhelms Aurora. The case calls for Add request coalescing and cache warming with bounded origin calls, since thundering herd mitigation protects underlying databases.

Evaluate Amazon Neptune for graph relationships would help if a graph recommendation engine traverses many related entities. Graph databases optimize connected-entity traversal. That is not the symptom described here: a financial ledger needs ACID multirow SQL transactions and joins. The matching response is Use an RDS/Aurora relational database with an appropriate SQL engine, because relational engines support transactional joins.

Use Amazon Redshift instead of a transactional RDS engine solves a different operational problem where analysts need a data warehouse for large columnar aggregations. Redshift is optimized for analytical workloads. It does not directly address a financial ledger needs ACID multirow SQL transactions and joins. The case calls for Use an RDS/Aurora relational database with an appropriate SQL engine, since relational engines support transactional joins.

The competing option Use Amazon OpenSearch Service for search-oriented workloads would be useful when an application needs managed search with full-text scoring and filtering. Search indexes serve full-text query patterns. The present issue is a financial ledger needs ACID multirow SQL transactions and joins; it is more directly addressed by Use an RDS/Aurora relational database with an appropriate SQL engine. Relational engines support transactional joins.

For the alternative failure an application needs managed search with full-text scoring and filtering, the control Use Amazon OpenSearch Service for search-oriented workloads is relevant: search indexes serve full-text query patterns. Here the evidence instead is a serverless API stores key-value session records with extremely variable throughput. The selected technical mechanism is Use DynamoDB on-demand capacity with appropriate key design: on-demand DynamoDB scales request capacity without manual provisioning.

Use Amazon Redshift instead of a transactional RDS engine solves a different operational problem where analysts need a data warehouse for large columnar aggregations. Redshift is optimized for analytical workloads. It does not directly address a serverless API stores key-value session records with extremely variable throughput. The case calls for Use DynamoDB on-demand capacity with appropriate key design, since on-demand DynamoDB scales request capacity without manual provisioning.

The competing option Use an RDS/Aurora relational database with an appropriate SQL engine would be useful when a financial ledger needs ACID multirow SQL transactions and joins. Relational engines support transactional joins. The present issue is a serverless API stores key-value session records with extremely variable throughput; it is more directly addressed by Use DynamoDB on-demand capacity with appropriate key design. On-demand DynamoDB scales request capacity without manual provisioning.

Use Amazon Redshift instead of a transactional RDS engine solves a different operational problem where analysts need a data warehouse for large columnar aggregations. Redshift is optimized for analytical workloads. It does not directly address a graph recommendation engine traverses many related entities. The case calls for Evaluate Amazon Neptune for graph relationships, since graph databases optimize connected-entity traversal.

The option Use Amazon OpenSearch Service for search-oriented workloads addresses a separate condition: an application needs managed search with full-text scoring and filtering. Search indexes serve full-text query patterns. The actual requirement is a graph recommendation engine traverses many related entities. Recommended corrective action: Evaluate Amazon Neptune for graph relationships. Graph databases optimize connected-entity traversal.

Use an RDS/Aurora relational database with an appropriate SQL engine would help if a financial ledger needs ACID multirow SQL transactions and joins. Relational engines support transactional joins. That is not the symptom described here: a graph recommendation engine traverses many related entities. The matching response is Evaluate Amazon Neptune for graph relationships, because graph databases optimize connected-entity traversal.

The competing option Use Amazon Redshift instead of a transactional RDS engine would be useful when analysts need a data warehouse for large columnar aggregations. Redshift is optimized for analytical workloads. The present issue is an application needs managed search with full-text scoring and filtering; it is more directly addressed by Use Amazon OpenSearch Service for search-oriented workloads. Search indexes serve full-text query patterns.

The option Use an RDS/Aurora relational database with an appropriate SQL engine addresses a separate condition: a financial ledger needs ACID multirow SQL transactions and joins. Relational engines support transactional joins. The actual requirement is an application needs managed search with full-text scoring and filtering. Recommended corrective action: Use Amazon OpenSearch Service for search-oriented workloads. Search indexes serve full-text query patterns.

Evaluate Amazon Neptune for graph relationships would help if a graph recommendation engine traverses many related entities. Graph databases optimize connected-entity traversal. That is not the symptom described here: an application needs managed search with full-text scoring and filtering. The matching response is Use Amazon OpenSearch Service for search-oriented workloads, because search indexes serve full-text query patterns.

Use Amazon OpenSearch Service for search-oriented workloads would help if an application needs managed search with full-text scoring and filtering. Search indexes serve full-text query patterns. That is not the symptom described here: analysts need a data warehouse for large columnar aggregations. The matching response is Use Amazon Redshift instead of a transactional RDS engine, because redshift is optimized for analytical workloads.

For the alternative failure a serverless API stores key-value session records with extremely variable throughput, the control Use DynamoDB on-demand capacity with appropriate key design is relevant: on-demand DynamoDB scales request capacity without manual provisioning. Here the evidence instead is analysts need a data warehouse for large columnar aggregations. The selected technical mechanism is Use Amazon Redshift instead of a transactional RDS engine: redshift is optimized for analytical workloads.

Use an RDS/Aurora relational database with an appropriate SQL engine solves a different operational problem where a financial ledger needs ACID multirow SQL transactions and joins. Relational engines support transactional joins. It does not directly address analysts need a data warehouse for large columnar aggregations. The case calls for Use Amazon Redshift instead of a transactional RDS engine, since redshift is optimized for analytical workloads.

For the alternative failure cPU usage spikes during queries that ignore selective indexes, the control Inspect execution plans and create/test appropriate indexes is relevant: query plan tuning reduces unnecessary scanned work. Here the evidence instead is rDS storage response latency rises because provisioned IOPS is insufficient. The selected technical mechanism is Increase eligible provisioned IOPS or select suitable SSD storage class: storage IOPS can bottleneck database response time.

Use RDS Proxy and application connection pools solves a different operational problem where thousands of client connections consume memory before query processing. Pooling caps backend connection proliferation. It does not directly address rDS storage response latency rises because provisioned IOPS is insufficient. The case calls for Increase eligible provisioned IOPS or select suitable SSD storage class, since storage IOPS can bottleneck database response time.

The competing option Right-size instance memory or scale suitable database tier would be useful when a dataset no longer fits the chosen database memory footprint. Cache hit rate and memory pressure affect database latency. The present issue is rDS storage response latency rises because provisioned IOPS is insufficient; it is more directly addressed by Increase eligible provisioned IOPS or select suitable SSD storage class. Storage IOPS can bottleneck database response time.

For the alternative failure a dataset no longer fits the chosen database memory footprint, the control Right-size instance memory or scale suitable database tier is relevant: cache hit rate and memory pressure affect database latency. Here the evidence instead is cPU usage spikes during queries that ignore selective indexes. The selected technical mechanism is Inspect execution plans and create/test appropriate indexes: query plan tuning reduces unnecessary scanned work.

The competing option Send suitable reads to read replicas and monitor replica lag would be useful when read queries block writer throughput during reporting windows. Replicas separate reporting reads from write traffic. The present issue is cPU usage spikes during queries that ignore selective indexes; it is more directly addressed by Inspect execution plans and create/test appropriate indexes. Query plan tuning reduces unnecessary scanned work.

The option Use RDS Proxy and application connection pools addresses a separate condition: thousands of client connections consume memory before query processing. Pooling caps backend connection proliferation. The actual requirement is cPU usage spikes during queries that ignore selective indexes. Recommended corrective action: Inspect execution plans and create/test appropriate indexes. Query plan tuning reduces unnecessary scanned work.

The competing option Inspect execution plans and create/test appropriate indexes would be useful when cPU usage spikes during queries that ignore selective indexes. Query plan tuning reduces unnecessary scanned work. The present issue is thousands of client connections consume memory before query processing; it is more directly addressed by Use RDS Proxy and application connection pools. Pooling caps backend connection proliferation.

The option Right-size instance memory or scale suitable database tier addresses a separate condition: a dataset no longer fits the chosen database memory footprint. Cache hit rate and memory pressure affect database latency. The actual requirement is thousands of client connections consume memory before query processing. Recommended corrective action: Use RDS Proxy and application connection pools. Pooling caps backend connection proliferation.

Send suitable reads to read replicas and monitor replica lag would help if read queries block writer throughput during reporting windows. Replicas separate reporting reads from write traffic. That is not the symptom described here: thousands of client connections consume memory before query processing. The matching response is Use RDS Proxy and application connection pools, because pooling caps backend connection proliferation.

The competing option Increase eligible provisioned IOPS or select suitable SSD storage class would be useful when rDS storage response latency rises because provisioned IOPS is insufficient. Storage IOPS can bottleneck database response time. The present issue is read queries block writer throughput during reporting windows; it is more directly addressed by Send suitable reads to read replicas and monitor replica lag. Replicas separate reporting reads from write traffic.

The option Right-size instance memory or scale suitable database tier addresses a separate condition: a dataset no longer fits the chosen database memory footprint. Cache hit rate and memory pressure affect database latency. The actual requirement is read queries block writer throughput during reporting windows. Recommended corrective action: Send suitable reads to read replicas and monitor replica lag. Replicas separate reporting reads from write traffic.

For the alternative failure thousands of client connections consume memory before query processing, the control Use RDS Proxy and application connection pools is relevant: pooling caps backend connection proliferation. Here the evidence instead is read queries block writer throughput during reporting windows. The selected technical mechanism is Send suitable reads to read replicas and monitor replica lag: replicas separate reporting reads from write traffic.

The option Send suitable reads to read replicas and monitor replica lag addresses a separate condition: read queries block writer throughput during reporting windows. Replicas separate reporting reads from write traffic. The actual requirement is a dataset no longer fits the chosen database memory footprint. Recommended corrective action: Right-size instance memory or scale suitable database tier. Cache hit rate and memory pressure affect database latency.

Increase eligible provisioned IOPS or select suitable SSD storage class would help if rDS storage response latency rises because provisioned IOPS is insufficient. Storage IOPS can bottleneck database response time. That is not the symptom described here: a dataset no longer fits the chosen database memory footprint. The matching response is Right-size instance memory or scale suitable database tier, because cache hit rate and memory pressure affect database latency.

Use RDS Proxy and application connection pools solves a different operational problem where thousands of client connections consume memory before query processing. Pooling caps backend connection proliferation. It does not directly address a dataset no longer fits the chosen database memory footprint. The case calls for Right-size instance memory or scale suitable database tier, since cache hit rate and memory pressure affect database latency.