Amazon Web Services Updated DVA-C02 Exam Questions and Answers by archibald

SQS Delivery Behavior: Standard SQS queues guarantee at-least-once delivery, meaning messages may be processed more than once. This can lead to duplicate emails in this scenario.

Visibility Timeout: If the visibility timeout on the SQS queue is too short, a message might become visible for another consumer before the first Lambda function finishes processing it. This can also lead to duplicates.

Comprehensive and Detailed 250 to 300 words of Explanation (AWS-doc-aligned):

To minimize widget latency for ranking and sorting, the best fit is Amazon ElastiCache for Redis OSS because Redis provides built-in in-memory data structures specifically suited for ranking use cases, such as Sorted Sets (ZSETs) . Sorted sets maintain members in order by score, which enables fast retrieval of “top N” items and efficient re-ranking without repeatedly querying the primary database and sorting at the application tier. Memcached, by contrast, is a simpler key-value cache and does not provide comparable native ranking/sorting data structures.

To ensure data remains up to date , a write-through caching strategy is preferred. With write-through, updates are written to the cache at the same time as the system of record, so the cache reflects the latest values immediately after writes. This avoids stale rankings that can occur with lazy-loading (cache-aside), where the cache is only populated or refreshed on reads and can serve older data until expiration or explicit invalidation.

Combining Redis’s native sorted/ranking structures with write-through updates yields the least latency because the page widgets can fetch pre-ranked/pre-sorted results directly from Redis memory with minimal computation, while still maintaining freshness as new plays, likes, or album changes occur. Operationally, this pattern reduces database load, avoids repeated sorting work in the application, and provides consistently fast reads for a high-traffic page.

To add a caching layer for frequently requested data (such as “most viewed products”), the AWS-native choice is Amazon ElastiCache, commonly using Redis for low-latency key/value caching, counters, sorted sets, and leaderboard-like patterns. Redis is particularly well-suited for ecommerce “top N” or popularity queries because it supports atomic increments and sorted sets, enabling efficient ranking updates.

Option B correctly introduces an ElastiCache (Redis OSS) cluster and updates the application to read from Redis first (cache-aside or write-through patterns) instead of hitting the RDS MySQL cluster for every request. This offloads read pressure from the database, reduces latency, and improves overall throughput.

Option A is not a valid RDS feature: you do not add a “cache node” to an RDS MySQL cluster as part of RDS itself.

Option C is incorrect because DAX is a caching layer specifically for DynamoDB, not for RDS MySQL.

Option D improves availability via Multi-AZ standby, but the standby is not for read scaling in standard RDS Multi-AZ (it’s primarily for failover). It does not provide a cache and does not solve the performance needs for hot reads.

The issue is write throttling during peak login surges. In DynamoDB provisioned capacity mode, throttling happens when the workload exceeds the table’s configured write capacity units (WCU) (or a partition becomes “hot”). To resolve throttling and reduce latency, the table must have sufficient write capacity available when the surge occurs.

Option B directly addresses this by increasing the table’s provisioned throughput (WCU). With more write capacity, DynamoDB can accept more writes per second without returning throttling errors, which reduces retries and improves end-user latency during peak usage.

Option E is also effective: switching the table to on-demand capacity mode automatically accommodates traffic spikes without the team needing to forecast and pre-provision capacity. On-demand is designed for unpredictable workloads and can scale to handle sudden surges, reducing the risk of throttling caused by under-provisioning. For many teams, this is the fastest operational fix because it removes manual capacity planning and reactive scaling steps.

Why the other options are not the best fit:

A (DAX) improves read performance and reduces read latency, but it does not fix write throttling .

C (retries and exponential backoff) is a best practice for handling throttling gracefully, but it does not “resolve” throttling; it reduces contention and improves success rates at the cost of higher latency, and it does not increase available capacity.

D focuses on control plane operations (schema/index/table management) which are unrelated to data plane write throttling during login surges.

Therefore, the two solutions that meet the requirements to resolve write throttling and reduce latency are B (increase provisioned throughput) and E (switch to on-demand capacity mode for automatic scaling).