Scenario: You are migrating workloads from one AWS account to another. How would you ensure seamless access to resources during the migration process using Cross-Account Access?
- Create IAM users in the destination account and manually copy permissions from the source account.
- Grant cross-account access to all users in both accounts to facilitate resource access.
- Set up IAM roles in the destination account with permissions to access resources in the source account and establish trust relationships between the two accounts.
- Temporarily disable IAM policies in the source account to allow unrestricted access during the migration.
By configuring IAM roles in the destination account with the necessary permissions to access resources in the source account and establishing trust relationships between the two accounts, you can ensure seamless access to resources during the migration process.
What is a custom runtime in AWS Lambda?
- A custom runtime allows you to use programming languages not officially supported by AWS Lambda, such as Rust or COBOL.
- A custom runtime is a built-in feature of AWS Lambda for optimizing performance and resource allocation.
- A custom runtime is a tool provided by AWS Lambda for debugging and monitoring functions in production environments.
- A custom runtime refers to manually configuring the execution environment of a Lambda function to tailor it to specific requirements.
A custom runtime allows you to use programming languages not officially supported by AWS Lambda, such as Rust or COBOL.
How does a custom runtime differ from standard runtimes in AWS Lambda?
- Custom runtimes are only available for enterprise-tier AWS Lambda plans, while standard runtimes are included in all tiers.
- Custom runtimes enable the use of programming languages not officially supported by AWS Lambda, while standard runtimes are limited to a predefined set of languages.
- Custom runtimes offer greater control over the execution environment and configuration options compared to standard runtimes.
- Custom runtimes provide better performance and scalability compared to standard runtimes in AWS Lambda.
Custom runtimes enable the use of programming languages not officially supported by AWS Lambda, expanding the range of languages developers can use.
AWS Lambda enforces a __________ for custom runtimes to ensure security and stability.
- Execution policy
- Hardware specification
- Runtime API
- Scripting language
AWS Lambda enforces a Runtime API for custom runtimes to ensure security and stability.
Scenario: Your team is developing a new programming language optimized for specific computational tasks. How would you integrate this language as a custom runtime in AWS Lambda?
- Create a Lambda layer with the language runtime
- Deploy the language runtime as a standalone service
- Embed the language runtime directly into the function code
- Use an existing natively supported runtime and adapt it
Creating a Lambda layer with the language runtime allows for reusability across multiple functions and simplifies maintenance.
Scenario: You need to ensure compliance with strict security requirements for your AWS Lambda functions, including custom runtime environments. How would you implement security controls and best practices?
- Enable AWS CloudTrail logging for Lambda function activity
- Encrypt environment variables containing sensitive data
- Implement least privilege IAM roles for Lambda functions
- Utilize AWS WAF to filter incoming requests
Implementing least privilege IAM roles for Lambda functions helps restrict access and reduce the attack surface, contributing to compliance with strict security requirements.
What is Lambda@Edge?
- AWS service for running code closer to end-users
- Container service
- Database service
- Machine learning service
Lambda@Edge is an AWS service that allows you to run code closer to end-users, enabling you to customize content delivery and enhance user experience.
What is the primary purpose of Lambda@Edge?
- Analyzing data
- Customizing content delivery and enhancing user experience
- Managing databases
- Securing network traffic
The primary purpose of Lambda@Edge is to enable customization of content delivery and enhance user experience by running code closer to end-users.
What are some common use cases for Lambda@Edge?
- Batch processing
- Database management
- IoT device management
- Website personalization
Lambda@Edge allows for dynamic content customization based on viewer location, device type, or other factors, enhancing user experience.
How does Lambda@Edge help improve content delivery performance?
- Executing code closer to viewers
- Increasing server capacity
- Managing networking hardware
- Optimizing database queries
Lambda@Edge allows code execution at CloudFront edge locations, reducing latency by executing code closer to viewers, thus improving content delivery performance.