Free Practice Questions for Amazon Web Services DVA-C02 Exam

Visibility Timeout:When an SQS message is processed by a consumer (here, the Lambda function), it's temporarily hidden from other consumers. Visibility timeout controls this duration.

How It Helps:

Increase the visibility timeout beyond the maximum processing time your Lambda might typically take for long videos.

This prevents the message from reappearing in the queue while Lambda is still working, avoiding premature timeouts.

To monitor memory usage in Amazon Elastic Beanstalk environments, it's important to understand that default Elastic Beanstalk monitoring capabilities in Amazon CloudWatch do not track memory usage, as memory metrics are not collected by default. Instead, theAmazon CloudWatch agentmust be configured to collect memory usage metrics.

Why Option C is Correct:

TheAmazon CloudWatch agentcan be installed and configured to monitor system-level metrics such as memory and disk utilization.

To enable memory tracking, developers need to install the CloudWatch agent on the Amazon Elastic Compute Cloud (EC2) instances associated with the Elastic Beanstalk environment.

After installation, the agent can be configured to collect memory metrics, which can then be sent to CloudWatch for further analysis.

How to Implement This Solution:

Install the CloudWatch Agent:Use .ebextensions or AWS Systems Manager to install and configure the CloudWatch agent on the EC2 instances running in the Elastic Beanstalk environment.

Modify CloudWatch Agent Configuration:Create a config.json file that specifies memory usage tracking and other desired metrics.

Enable Metrics Reporting:The CloudWatch agent can push the metrics to CloudWatch, where they can be monitored.

Why Other Options are Incorrect:

Option A:Configuring the agent to push logs is not sufficient to track memory metrics. This option addresses logging but not system-level metrics like memory usage.

Option B:The .ebextensions directory is used to customize Elastic Beanstalk environments but does not directly track memory metrics without additional configuration of the CloudWatch agent.

Option D:Configuring a CloudWatch dashboard will only visualize the metrics that are already being collected. It will not enable memory usage tracking.

AWS Documentation References:

Amazon CloudWatch Agent Overview

Elastic Beanstalk Customization Using .ebextensions

Monitoring Custom Metrics

Step 1: Understanding the Requirements

Gradual Traffic Shift:Test the new version by routing only 10% of production traffic to it.

Lambda Deployment:Use versioning and aliases to manage Lambda function updates.

Step 2: Solution Analysis

Option A:

Publishing a new version creates an immutable version of the Lambda function with the updated code.

This is a prerequisite for deploying changes using weighted aliases.

Correct option.

Option B:

API Gateway stages are not used for weighted routing; they represent environments like "dev" or "prod."

Weighted routing is implemented using Lambda aliases, not API Gateway stages.

Not suitable.

Option C:

Lambda aliases allow traffic to be split between versions using weighted routing.

Assign a 90% weight to the old version and 10% to the new version to implement the gradual rollout.

Correct option.

Option D:

Network Load Balancers are not suitable for managing Lambda function traffic directly.

Not applicable.

Option E:

Route 53 routing policies apply at the DNS level and are not designed for Lambda version management.

Not suitable.

Step 3: Implementation Steps

Publish a New Version:

Publish the updated Lambda function code as a new version.

Create an Alias and Configure Weighted Routing:

Create an alias (e.g., prod) and associate it with both the old and new versions.

Set weights for traffic distribution:

aws lambda update-alias --function-name my-function \

--name prod --routing-config '{"AdditionalVersionWeights": {"2": 0.1}}'

AWS Developer References:

Lambda Function Aliases

Weighted Traffic Routing with Lambda

Why Option A is Correct:Auto-scaling with a target utilization ensures the DynamoDB table dynamically adjusts capacity based on workload, maintaining high performance while optimizing cost. Setting a reasonable target utilization minimizes overprovisioning and throttling risks.

Why Other Options are Incorrect:

Option B:On-demand capacity is costlier than provisioned throughput for predictable workloads.

Option C:Using manual CloudWatch alarms and Lambda for scaling is less efficient and adds operational overhead.

Option D:DAX accelerates read performance but does not improve write performance.

AWS Documentation References:

DynamoDB Auto Scaling

Why Option B is Correct:A customer-managed AWS Key Management Service (KMS) key allows for encryption at rest and provides the ability to rotate the key on demand. This ensures compliance with security requirements for key management and database encryption.

RDS integrates natively with AWS KMS, allowing the use of a customer-managed key for encrypting data at rest.

Key rotation can be managed directly in AWS KMS without needing custom solutions.

Why Other Options are Incorrect:

Option A:AWS KMS managed encryption keys (AWS-owned keys) do not support key rotation on demand.

Option C & D:Storing keys in AWS Secrets Manager with custom rotation is not a recommended approach for database encryption. AWS KMS is designed specifically for secure key management and encryption.

AWS Documentation References:

Encrypting Amazon RDS Resources

AWS Key Management Service (KMS)

The best solution for this requirement is option A. Creating an Amazon EventBridge rule that has a rate expression that will run the rule every 15 minutes and adding the Lambda function as the target of the EventBridge rule is the most efficient way to invoke the Lambda function periodically. This solution does not require any additional resources or development effort, and it leverages the built-in scheduling capabilities of EventBridge1.

Lambda Concurrency: The 'maximum concurrency' setting in event source mappings controls the maximum number of simultaneous invocations Lambda allows for that specific source.

Prioritizing Queues: Setting a lower maximum concurrency for the 'high priority queue' ensures it's processed first while allowing more concurrent invocations from the 'low priority queue'.

Batching: Batch size settings affect the number of messages Lambda retrieves from a queue per invocation, which is less relevant to the prioritization requirement.