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A developer is deploying an AWS Lambda function The developer wants the ability to return to older versions of the function quickly and seamlessly.
How can the developer achieve this goal with the LEAST operational overhead?
A company is building an application to accept data from customers. The data must be encrypted at rest and in transit.
The application uses an Amazon API Gateway API that resolves to AWS Lambda functions. The Lambda functions store the data in an Amazon Aurora MySQL DB cluster. The application worked properly during testing.
A developer configured an Amazon CloudFront distribution with field-level encryption that uses an AWS Key Management Service (AWS KMS) key. After the configuration of the distribution, the application behaved unexpectedly. All the data in the database changed from plaintext to ciphertext.
The developer must ensure that the data is not stored in the database as the ciphertext from the CloudFront field-level encryption.
Which solution will meet this requirement?
A developer has a legacy application that is hosted on-premises. Other applications hosted on AWS depend on the on-premises application for proper functioning. In case of any application errors, the developer wants to be able to use Amazon CloudWatch to monitor and troubleshoot all applications from one place.
How can the developer accomplish this?
Amazon CloudWatch is a service that monitors AWS resources and applications. The developer can use CloudWatch to monitor and troubleshoot all applications from one place. To do so, the developer needs to download the CloudWatch agent to the on-premises server and configure the agent to use IAM user credentials with permissions for CloudWatch. The agent will collect logs and metrics from the on-premises server and send them to CloudWatch.
[What Is Amazon CloudWatch? - Amazon CloudWatch]
[Installing and Configuring the CloudWatch Agent - Amazon CloudWatch]
A company wants to use AWS AppConfig to gradually deploy a new feature to 15% of users to test the feature before a full deployment.
Which solution will meet this requirement with the LEAST operational overhead?
Comprehensive Detailed and Lengthy Step-by-Step Explanation with All AWS Developer Reference:
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.
A developer is building a microservices-based application by using Python on AWS and several AWS services The developer must use AWS X-Ray The developer views the service map by using the console to view the service dependencies. During testing, the developer notices that some services are missing from the service map
What can the developer do to ensure that all services appear in the X-Ray service map?
AWS X-Ray SDK:The primary way to enable X-Ray tracing within applications. The SDK sends data about requests and subsegments to the X-Ray daemon for service map generation.
Instrumenting All Services:To visualize a complete microservice architecture on the service map, each relevant service must include the X-Ray SDK.
AWS X-Ray Documentation:https://docs.aws.amazon.com/xray/
X-Ray SDK for Python:https://docs.aws.amazon.com/xray/latest/devguide/xray-sdk-python.html
