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

DynamoDB access control operates at the table and item level , not at individual attribute deny rules in IAM or resource-based policies. Therefore, Option A is not supported. Encrypting the entire table with KMS (Option B) protects data at rest but does not prevent the Lambda function from reading sensitive attributes once access is granted.

To ensure the Lambda function cannot access PII, AWS documentation recommends controlling which attributes are returned from queries and scans. Using a projection expression (Option E) ensures that only non-PII attributes are returned to the function, even if PII exists in the item. This is a standard DynamoDB best practice for limiting data exposure.

For additional protection, client-side or application-level encryption should be used for sensitive fields. Option C uses envelope encryption with AWS KMS to encrypt only PII attributes. Even if the function retrieves the encrypted attribute, it cannot decrypt the data without KMS permissions. This ensures strong isolation of sensitive data.

ABAC (Option D) controls access to resources, not attributes, and therefore does not satisfy the requirement.

Thus, combining attribute-level encryption with projection expressions meets the security requirement.

To reduce the time it takes for new Auto Scaling instances to become ready, the most effective optimization is to bake the time-consuming installation and configuration steps into a custom AMI . By creating a prebuilt AMI that already contains the application binaries, dependencies, and baseline configuration, new instances start from an image that is already near “ready to serve.” This minimizes lengthy bootstrapping and significantly shortens the time required to pass ALB health checks and enter service. Therefore, creating a prebuilt AMI (B) is a primary performance optimization for faster scaling events and lower deployment variability.

After creating the AMI, the Auto Scaling group must actually use it. Because the infrastructure is managed with AWS CloudFormation , the developer should update the launch template resource (or launch configuration equivalent) in the CloudFormation template to reference the new AMI ID. This ensures that all newly launched instances use the optimized image consistently and that the change is tracked and repeatable as part of infrastructure as code. That is option C .

The other options are less optimal for the stated goal. Installing and configuring the app at launch time via Systems Manager Run Command (D) or CloudFormation helper scripts (E) still performs the expensive work during instance initialization, which prolongs launch time and can create variance (for example, dependency download time, repo availability, or transient network slowness). Option A (Marketplace AMI) could help only if a suitable prebuilt image exists for the exact custom application—which is unlikely for a proprietary application—and it also reduces control over the build and hardening process compared to maintaining an internal golden AMI.

Therefore, the best combination is B (bake the app into a custom AMI) and C (update the CloudFormation-managed launch template to use that AMI).

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.