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

Requirement Summary:

DynamoDB Provisioned Mode

ProvisionedThroughputExceededException error occurring

App hosted on a small burstable EC2 instance

Option A: Move to a larger EC2 instance

May improve local performance, but does not solve DynamoDB provisioned throughput limits.

Option B: Increase DynamoDB RCUs

Valid: This directly increases the read throughput capacity of the table.

Helps handle more traffic and reduce throughput exceptions.

Option C: Reduce frequency via exponential backoff

Best practice: Using exponential backoff and jitter reduces request pressure and spreads retries out to avoid spiking.

Option D: Increase frequency of retries by reducing delay

Opposite of best practice. Increases load, worsening the issue.

Option E: Change to on-demand mode

Also valid in general, but question is scoped around provisioned capacity.

Since minimizing cost or workload type isn ' t mentioned, and scaling the existing model is the focus, prefer B and C.

ProvisionedThroughputExceededException: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HandlingErrors.html

Exponential backoff: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Programming.Errors.html

Capacity modes: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadWriteCapacityMode.html

AWS Lambda combined with Amazon S3 event notifications provides the most cost-effective and lowest-latency solution for processing objects uploaded to S3. AWS documentation states that S3 event notifications can invoke Lambda functions immediately after object creation, eliminating delays associated with polling or scheduled execution.

Because the data arrives only about 10 times per day and processing completes quickly, Lambda is ideal due to its pay-per-invocation pricing model . There is no need to maintain always-on infrastructure, which significantly reduces cost compared to running EC2 instances. The developer pays only for execution time and requests.

Using CloudWatch alarms (Option A) or scheduled events (Option C) introduces unnecessary latency and complexity. These approaches either delay processing or require periodic checks rather than reacting instantly to object uploads. Polling from EC2 (Option D) is the most expensive and least efficient option because it requires continuously running compute resources.

AWS best practices for event-driven architectures explicitly recommend S3 event notifications → Lambda for near-real-time processing of uploaded data. This approach minimizes operational overhead, achieves near-zero idle cost, and provides the fastest possible response to new data.

Therefore, invoking a Lambda function directly through an S3 event notification is the optimal solution.

Comprehensive Detailed and Lengthy Step-by-Step Explanation with All AWS Developer References:

1. Understanding the Use Case:

The company wants to gradually release a new feature to 15% of users to perform testing. AWS AppConfig is designed to manage and deploy configurations, including feature flags, allowing controlled rollouts.

2. Key AWS AppConfig Features:

Feature Flags: Enable or disable features dynamically without redeploying code.

Variants: Define different configurations for subsets of users.

Targeting Rules: Specify rules for which users receive a particular variant.

3. Explanation of the Options:

Option A: " Set up a custom script within the application to randomly select 15% of users. Assign a flag for the new feature to the selected users. " While possible, this approach requires significant operational effort to manage user selection and ensure randomness. It does not leverage AWS AppConfig ' s built-in capabilities, which increases overhead.

Option B: " Create separate AWS AppConfig feature flags for both groups of users. Configure the flags to target 15% of users. " Creating multiple feature flags for different user groups complicates configuration management and does not optimize the use of AWS AppConfig.

Option C: " Create an AWS AppConfig feature flag. Define a variant for the new feature, and create a rule to target 15% of users. " This is the correct solution. Using AWS AppConfig feature flags with variants and targeting rules is the most efficient approach. It minimizes operational overhead by leveraging AWS AppConfig ' s built-in targeting and rollout capabilities.

Option D: " Use AWS AppConfig to create a feature flag without variants. Implement a custom traffic splitting mechanism in the application code. " This approach requires custom implementation within the application code, increasing complexity and operational effort.

4. Implementation Steps for Option C:

Set Up AWS AppConfig:

Open the AWS Systems Manager Console.

Navigate to AppConfig.

Create a Feature Flag:

Define a new configuration for the feature flag.

Add variants (e.g., " enabled " for the new feature and " disabled " for no change).

Define a Targeting Rule:

Use percentage-based targeting to define a rule that applies the " enabled " variant to 15% of users.

Targeting rules can use attributes like user IDs or geographic locations.

Deploy the Configuration:

Deploy the configuration using a controlled rollout to ensure gradual exposure.

Amazon S3 supports resource-based policies, which are JSON documents that specify the permissions for accessing S3 resources. A resource-based policy can be used to enforce encryption in transit by denying access to requests that do not use HTTPS. The condition key aws:SecureTransport can be used to check if the request was sent using SSL. If the value of this key is false, the request is denied; otherwise, the request is allowed. Reference: How do I use an S3 bucket policy to require requests to use Secure Socket Layer (SSL)?