DVA-C02 Exam Dumps - AWS Certified Developer - Associate

Amazon SNS supports server-side encryption (SSE) using AWS KMS to protect message content at rest . Enabling SSE on the SNS topic ensures that all messages published to the topic are encrypted using a customer-managed or AWS-managed KMS key. AWS documentation explicitly states that SSE is the mechanism for encrypting SNS messages stored within the service.

For encryption in transit , AWS services rely on HTTPS/TLS. To enforce this at the IAM policy level, AWS recommends using the global condition key aws:SecureTransport . By adding an explicit Deny statement when " aws:SecureTransport " : " false " , the policy ensures that any request not using HTTPS is rejected. This is a common AWS security best practice and is documented across multiple AWS services.

Option E correctly enforces encryption in transit. Option B is incorrect because denying when SecureTransport is true would block valid HTTPS requests. Option D is invalid because sns:Protocol applies to subscription protocols (such as email or HTTP endpoints), not to Lambda publishing actions. Option C (VPC endpoint) is not required because Lambda-to-SNS traffic is already encrypted over TLS by default.

Therefore, enabling SNS server-side encryption and enforcing HTTPS via an IAM deny condition fully satisfies the encryption-at-rest and encryption-in-transit requirements.

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).

The correct answer is B because Amazon CloudWatch Logs Insights is the AWS-native tool for interactively searching, filtering, and analyzing log data that is already stored in CloudWatch Logs. In this case, the developer needs to investigate intermittent Lambda failures within a specific 1-hour period , identify logs related to failures, and capture the timestamps of those failures. Logs Insights is purpose-built for exactly this kind of operational troubleshooting.

AWS documentation describes Logs Insights as a fully integrated log analytics capability that can query log groups by time range and pattern. The developer can run a query that filters for error-related log messages, extracts timestamp fields, and sorts the results to find exactly when failures occurred. This is highly efficient because it does not require exporting logs, writing custom parsing scripts, or manually browsing through streams.

Option A is possible but not the most operationally efficient. Browsing logs through the CLI is slower and more manual, especially for intermittent failures. Option C is inefficient because downloading logs to a local machine adds unnecessary steps and operational overhead. Option D can work for large-scale analytics, but exporting to S3 and querying with Athena is more complex than needed for logs that are already in CloudWatch Logs.

Therefore, the best solution is to use CloudWatch Logs Insights to query the Lambda log group for failure patterns and timestamps. This provides fast, targeted, and operationally efficient troubleshooting directly within AWS.

AWS AppConfig Feature Flags are designed to release features safely with minimal code changes. The lowest operational overhead approach is to use a single feature flag and configure it to deliver the feature to a percentage of users through built-in targeting rules and variants.

With option C, the developer creates one AppConfig feature flag and defines a variant that represents the new feature behavior (for example, enabled=true or a “newExperience” variant). Then the developer creates a rule in AppConfig to target 15% of users. AppConfig feature flags support rules that can evaluate attributes (such as user IDs or other contextual attributes supplied by the application) and apply percentage-based rollout. This avoids building and maintaining custom rollout logic in the application.

Option A and D both require implementing and maintaining custom traffic splitting in code, which increases complexity and operational burden, and risks inconsistent behavior across clients/services.

Option B adds extra configuration overhead and complexity by managing multiple feature flags for “groups” instead of using a single flag with a variant and rule. A single flag with variants is the clean, scalable pattern.

Therefore, create one AppConfig feature flag, define a variant, and configure a rule to target 15% of users.

Amazon API Gateway is a service that enables developers to create, publish, maintain, monitor, and secure APIs at any scale. The developer can enable API caching in API Gateway to cache responses from the backend integration point for a specified time-to-live (TTL) period. This can improve the responsiveness of the APIs by reducing the number of calls made to the backend service.

When multiple Lambda functions must execute in a defined sequence as part of a workflow (order processing → payment → inventory → fulfillment, etc.), the AWS service designed to coordinate and orchestrate serverless workflows is AWS Step Functions.

Option C is the least operational overhead because Step Functions provides a managed state machine that invokes Lambda functions in an explicit order with built-in support for retries, timeouts, error handling, branching, and state passing between steps. The developer defines the workflow declaratively (Amazon States Language) and Step Functions ensures the sequence is enforced consistently.

Option A (SQS) is not a workflow orchestrator. Ensuring strict sequencing would require custom coordination logic, state tracking, and careful handling of retries and ordering—more code and complexity (and standard SQS does not guarantee strict order).

Option B (SNS) fans out events and is not designed for sequential orchestration.

Option D (EventBridge Scheduler) can schedule invocations at times, but it does not coordinate multi-step workflows with dependencies and conditional transitions.

EventBridge (formerly CloudWatch Events) is the ideal service for real-time event monitoring.

CloudTrail logs IAM role creation.

EventBridge rules can filter CloudTrail events and trigger SNS notifications instantly.

When imported key material is deleted from a KMS key, the KMS key becomes unusable for cryptographic operations until the same key material is reimported. The encrypted data can only be decrypted with the same KMS key and the same imported key material. Creating a new AWS managed key will not decrypt data that was encrypted with the original customer managed key. AWS Support cannot recover deleted imported key material because AWS never owned that external key material. Option A is close but less precise because the developer should reimport the same original key material, not create new key material. AWS KMS documentation states that deleting imported key material is reversible by reimporting the same key material. ( AWS Documentation )

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