Amazon Web Services DVA-C02 Exam With Confidence Using Practice Dumps

The workload is a classic “hot key / hot dataset” pattern: many reads repeatedly target a small subset of items. The best way to improve performance and reduce latency is to add a caching layer so the application does not hit DynamoDB for every read.

Option B is purpose-built for DynamoDB read acceleration: DynamoDB Accelerator (DAX) is an in-memory cache that sits in front of DynamoDB and is API-compatible for many DynamoDB operations. DAX can dramatically reduce read latency (often to microseconds) for frequently accessed items and offload read traffic from the table.

Option A can also help: ElastiCache (Redis/Memcached) can be used as an application-managed cache. This is useful when the application wants more control over caching strategy, TTLs, and non-DynamoDB data caching. For ECS applications, ElastiCache is a common high-performance caching choice.

Why not the others:

C (strongly consistent reads) typically increases latency and capacity consumption; it does not improve performance for repeated reads.

D (increase read capacity) can reduce throttling, but it does not reduce latency as effectively as caching and can be more expensive for hot-read patterns.

E (adaptive capacity) helps DynamoDB handle uneven workloads, but it is not a direct performance boost like caching for repeated reads of a small dataset.

Therefore, adding caching via ElastiCache and/or DAX best improves performance. With “select two,” A and B are correct.

Amazon S3 event notifications can invoke AWS Lambda functions at very high rates during traffic spikes. In this scenario, up to 500 objects per minute can result in hundreds of concurrent Lambda invocations. By default, Lambda functions share the account’s unreserved concurrency pool , which can cause throttling if the pool is exhausted by sudden bursts.

AWS Lambda reserved concurrency guarantees a specific number of concurrent executions for a function. By configuring reserved concurrency, the developer ensures that sufficient execution capacity is always available for this specific function, regardless of other Lambda workloads in the account. This prevents throttling during high-volume S3 event bursts.

Increasing the timeout (Option A) does not address concurrency limits. Adding a layer (Option B) does not solve invocation throttling. Decreasing memory (Option D) can actually worsen performance and increase execution time.

AWS documentation explicitly recommends reserved concurrency when workloads experience unpredictable or bursty invocation patterns and must run reliably. Therefore, reserving concurrency is the correct and AWS-recommended solution.

Amazon S3 automatically scales to handle high request rates, but request parallelism is optimized when objects are distributed across multiple prefixes. AWS documentation explains that S3 internally partitions data by key name, and distributing objects across multiple prefixes allows requests to be spread across multiple partitions.

When many GET requests target objects within the same prefix, they may contend for the same internal partition, limiting throughput. By designing object keys with multiple prefixes—such as including hashed or randomized components—applications can significantly increase parallel request handling.

Options A and D do not address S3 request partitioning. Global Accelerator improves network latency for supported endpoints but does not increase S3 request parallelism. Direct Connect improves network consistency but does not change how S3 scales requests internally. Option B worsens the problem by concentrating traffic into a single prefix.

Therefore, partitioning objects by prefixes is the correct and AWS-recommended solution.