Landing a job that needs AWS Lambda skills can be tough. You worry about facing tricky interview questions. You want to feel confident and prepared.
This article helps you with that. It gives you common AWS Lambda interview questions and clear answers.
What is AWS Lambda?
Serverless computing is a cloud computing model. It lets you run code without managing servers. You pay only for the computer time you use. You do not pay for idle time. This model helps you build applications faster and cheaper.
AWS Lambda is a serverless computer service. It runs your code in response to events. You write code and upload it to AWS Lambda. AWS Lambda automatically manages the compute resources for you. This means you do not need to worry about servers, operating systems, or networks.
Functions
AWS Lambda uses functions. A function is a small unit of code. It has a specific task. Functions can use different programming languages like Python, Java, Node.js, and more. You write the function code, package it, and upload it to AWS Lambda.
Triggers
Triggers start functions. They are events that tell AWS Lambda to run a function. There are many types of triggers. For example, an HTTP request, a file upload, or a database change can be a trigger. You can set up triggers to start functions automatically.
Execution Environment
AWS Lambda provides an execution environment for your functions. It includes the computing resources, memory, and network connections. You do not manage these resources. AWS Lambda allocates these resources based on your function’s needs. When your function finishes, AWS Lambda releases the resources.
AWS Lambda is easy to use and can help you save money. Let’s see why it is important.
Why is AWS Lambda important?
AWS Lambda can save you time and money. You don’t need to manage servers, so you can focus on building your application. It also scales automatically, so you only pay for the computer time you use.
Benefits for Developers
- Focus on Code: Developers spend more time writing code and less time managing servers.
- Faster Development: AWS Lambda helps you build and deploy applications quickly.
- Lower Costs: You pay only for the computer time you use.
- Automatic Scaling: Your application can handle a large number of requests without any manual intervention.
- High Availability: AWS Lambda runs your code across multiple availability zones.
Benefits for Businesses
- Reduced Costs: Pay only for the computer resources you use.
- Increased Efficiency: Automate tasks and processes.
- Improved Scalability: Handle sudden increases in traffic without issues.
- Faster Time to Market: Deploy applications quickly.
- Reduced Management Overhead: AWS handles server management.
Use Cases
- Data Processing: Process data from various sources like Amazon S3, Amazon Kinesis, and Amazon DynamoDB.
- Web Applications: Build and deploy web applications without managing servers.
- Mobile Backends: Create APIs for mobile apps.
- IoT Applications: Process data from IoT devices.
- Machine Learning: Deploy machine learning models.
Real-World Examples
- Image Resizing: Automatically resize images when uploaded to Amazon S3.
- Real-Time Data Analysis: Analyse data streams from Amazon Kinesis.
- Serverless Web Applications: Build web applications without managing servers.
- Chatbots: Create chatbots using AWS Lambda and Amazon Lex.
- IoT Data Processing: Process data from IoT devices to monitor equipment health.
AWS Lambda is easy to use and can help you save money. Let’s see why it is important.
Understanding AWS Lambda
AWS Lambda runs your code in response to events. Events can come from many different sources, like Amazon S3, Amazon DynamoDB, or Amazon Kinesis. When an event happens, AWS Lambda runs your code.
Core Components
AWS Lambda has three main parts: functions, triggers, and the execution environment.
A function holds your code. You choose the programming language and set the amount of memory and time your function can use. Triggers start your functions. These can be events from other AWS services like S3 or DynamoDB, or from external systems like web applications. The execution environment gives your function resources like network access and temporary storage while it runs.
Lambda Architecture
Lambda operates as a serverless computing platform. This means you do not manage servers. AWS handles the infrastructure. When a trigger activates a function, Lambda allocates resources for it. After the function finishes, Lambda reclaims these resources.
Source: https://hazelcast.com/glossary/lambda-architecture/
Execution Model
Lambda uses two main execution modes: cold start and warm start. A cold start happens when Lambda creates a new execution environment for a function. This takes more time. A warm start uses an existing environment, so it is faster.
Lambda can run multiple copies of your function at once to handle many requests. This is called concurrent executions. If too many requests arrive, Lambda might throttle your function, meaning it delays or rejects requests. Proper error handling is important to manage unexpected issues.
Deployment and Management
You package your function code into a deployment package. This package uploads to AWS. Lambda lets you create different versions of your function. You can use aliases to point to a specific version. Lambda layers store reusable code libraries for your functions.
Security
Lambda uses IAM roles to control which AWS resources your function can access. You should carefully define these permissions. Protect your function code and data using encryption. Regularly review and update security settings.
Now that you know how AWS Lambda works, let’s see what you can use it for.
AWS Lambda: Common Use Cases
AWS Lambda has many different uses. You can use it to build web applications, mobile backends, and data processing pipelines. You can also use it to automate tasks and create serverless microservices.
API Gateways and RESTful APIs
You can use AWS Lambda to build the backend for your APIs. AWS API Gateway manages API requests and routes them to Lambda functions. This lets you create RESTful APIs quickly and easily. Lambda handles the API logic, and API Gateway handles the traffic.
Real-time Data Processing with Kinesis and DynamoDB
AWS Kinesis is a service for real-time processing of streaming data. Lambda can process data as it arrives in Kinesis. You can use Lambda to transform data, filter data, or load data into other systems. AWS DynamoDB is a NoSQL database that can store and retrieve data with any level of performance. Lambda can access and modify data in DynamoDB.
Scheduled Tasks with EventBridge
AWS EventBridge is a serverless event bus. It helps you build event-driven applications. You can use Lambda to create functions that run on a schedule. EventBridge triggers these functions at the specified times. This is useful for batch jobs, data synchronisation, and other tasks.
Serverless Applications and Microservices
Lambda is ideal for building serverless applications. You can create small, independent functions that handle specific tasks. This makes your applications more scalable, resilient, and cost-effective. You can also use Lambda to build microservices architecture. Microservices are small, independent services that work together to form an application.
Integration with Other AWS Services
Lambda integrates with many other AWS services. You can use Lambda to trigger actions in other services, or you can use other services to trigger Lambda functions. For example, you can use S3 to store files, and Lambda to process these files when they are uploaded. You can also use Lambda to send emails using SES, or to send notifications using SNS.
AWS Lambda is a versatile tool with many possible applications. But to get a job using AWS Lambda, you need to know about the interview questions.
AWS Lambda Interview Questions and Answers
Preparing for an AWS Lambda interview? We have you covered. This section will provide common AWS Lambda interview questions and their answers.
Basic AWS Lambda Interview Questions and Answers
1) What is AWS Lambda? Explain its core components.
AWS Lambda is a serverless computing service provided by Amazon Web Services (AWS) that allows you to run code without provisioning or managing servers. Its core components are:
- Functions: The actual code that performs the task
- Event sources: Triggers that invoke the function (e.g., S3 events, API Gateway requests)
- Execution context: The environment where the function runs, including memory, CPU, and timeout settings
2) How does AWS Lambda differ from EC2?
- EC2 is an Infrastructure-as-a-Service (IaaS) offering that provides virtual machines, while Lambda is a Function-as-a-Service (FaaS) offering that runs code in response to events.
- EC2 requires managing servers, scaling, and provisioning, while Lambda automatically scales and manages infrastructure.
- EC2 charges for the entire time a VM is running, while Lambda charges only for the compute time used by the function.
3) Describe the cold start problem and how to mitigate it.
Cold starts occur when a Lambda function is invoked after a period of inactivity, causing a delay as the runtime environment is initialised. To mitigate cold starts:
- Use provisioned concurrency to keep functions warm
- Optimise function code and dependencies to reduce initialization time
- Use a higher memory setting to get more CPU power and reduce cold starts
4) What are the different ways to invoke a Lambda function?
Lambda functions can be invoked in the following ways:
- Synchronously: The caller waits for the function to complete and returns the result
- Asynchronously: The function is invoked and returns immediately, with results stored for later retrieval
- Event source mapping: AWS services like S3, DynamoDB, and Kinesis trigger the function in response to events
5) Explain the concept of Lambda layers.
Lambda layers are a mechanism to package libraries, custom runtimes, or other dependencies that can be shared across multiple functions. This helps:
- Reduce deployment package sizes
- Promote code reuse
- Easily manage dependencies
Intermediate AWS Lambda Interview Questions and Answers
1) How do you handle errors and exceptions in Lambda functions?
To handle errors in Lambda functions:
- Implement try-catch blocks to catch and handle exceptions
- Return appropriate error messages and status codes
- Use Dead-Letter Queues (DLQs) to capture failed invocations for further processing
- Configure Lambda to automatically retry on transient errors
2) Describe the best practices for writing efficient Lambda functions.
Best practices include:
- Keep functions short and focused on a single task
- Optimise memory settings to balance performance and cost
- Use environment variables for configuration and avoid hard-coding values
- Implement asynchronous patterns like callbacks and promises
- Reuse connections and resources across invocations to improve performance
3) How do you monitor and troubleshoot Lambda functions?
Monitoring and troubleshooting is done using:
- CloudWatch Logs: Captures function logs for debugging and monitoring
- X-Ray: Provides distributed tracing to analyse function performance and dependencies
- CloudWatch Metrics: Provides metrics like invocations, duration, and errors for monitoring function health
4) Explain the concept of concurrent executions and how to manage them.
Concurrent executions refer to the number of instances of a function running at a given time. To manage concurrency:
- Set reserved concurrency limits to allocate a portion of the account’s concurrency to a specific function
- Use provisioned concurrency to keep functions warm and reduce cold starts
- Monitor concurrency metrics in CloudWatch to identify and scale functions as needed
5) What are the security considerations for Lambda functions?
Key security considerations include:
- IAM roles: Assign least-privileged roles to functions to limit access to AWS resources
- VPC: If functions need to access private resources, configure them to run in a VPC
- Environment variables: Store sensitive information like API keys and database credentials as encrypted environment variables
- Logging and monitoring: Use CloudWatch and X-Ray to monitor function activity and detect potential security issues
Advanced AWS Lambda Interview Questions and Answers
1) How would you design a serverless architecture for a high-traffic application?
Key design considerations:
- Use API Gateway for managing and securing API traffic
- Implement asynchronous processing using SQS, SNS, or Step Functions for handling high volumes
- Scale functions proactively using provisioned concurrency and reserved concurrency limits
- Optimise for performance by using higher memory settings and reducing cold starts
- Implement monitoring and alerting to detect and respond to issues
2) Explain the difference between synchronous and asynchronous invocations.
- Synchronous invocations: The caller waits for the function to complete and receives the result. Useful for request-response patterns.
- Asynchronous invocations: The function is invoked and returns immediately. The result is stored for later retrieval. Useful for event-driven architectures.
3) How do you optimise Lambda function performance?
Key optimization techniques include:
- Optimising memory settings to balance performance and cost
- Reducing function initialization time by minimising dependencies and using provisioned concurrency
- Reusing connections and resources across invocations to avoid repeated initialization
- Implementing asynchronous patterns like callbacks and promises to avoid blocking the event loop
- Monitoring and profiling function performance using CloudWatch and X-Ray
4) Discuss the trade-offs between Lambda and other computer services.
Compared to EC2:
- Lambda is more cost-effective for intermittent workloads and event-driven architectures
- EC2 provides more control over the underlying infrastructure and is better suited for long-running workloads
Compared to Fargate:
- Lambda is more serverless and requires less configuration
- Fargate provides more control over the container environment and is better suited for containerized workloads
5) How do you implement a CI/CD pipeline for Lambda functions?
A typical CI/CD pipeline for Lambda functions includes:
- Source control: Store function code and configuration in a version control system like Git
- Build and package: Use tools like AWS SAM or Serverless Framework to package function code and dependencies
- Continuous integration: Automatically build, test, and package function code on each commit using a CI tool like Jenkins or CircleCI
- Continuous deployment: Automatically deploy packaged functions to different environments using AWS CodeDeploy or CloudFormation
The pipeline should also include steps for running automated tests, validating configurations, and managing environment-specific settings.
Knowing the answers to common interview questions is important, but it’s also crucial to follow best practices when using AWS Lambda.
AWS Lambda: Best Practices and Tips
To get the most out of AWS Lambda, it’s important to follow best practices. This section will share tips for optimising your Lambda functions and improving performance.
1) Code Optimization and Performance Tuning
Code optimization makes your software run faster. Performance tuning improves how well your software uses computer resources. Both save money.
To optimise code, write simple and clear code. Use the right data structures. Avoid unnecessary calculations. Test different code versions to find the fastest one.
To tune performance, identify bottlenecks. Use tools to measure how your software uses CPU, memory, and disk. Change code or hardware to fix bottlenecks.
2) Cost Optimization Strategies
Cost optimization saves money on cloud services. Choose the right services for your needs. Use the cheapest service that works. Turn off unused services.
Monitor your cloud usage. Find ways to reduce costs. Use pricing calculators to compare services. Negotiate with cloud providers.
3) Debugging and Troubleshooting Techniques
Debugging finds and fixes code problems. Troubleshooting solves system issues. Both are important skills.
To debug code, use a debugger to step through code. Set breakpoints to stop code at specific points. Inspect variables to find errors.
To troubleshoot systems, check logs for errors. Use monitoring tools. Isolate problems. Test solutions. Document steps.
4) Security Best Practices
Security protects your software and data. Follow security best practices. Prevent attacks. Detect and respond to threats.
Use strong passwords. Keep software updated. Protect data with encryption. Limit user access. Monitor for threats. Respond quickly to incidents.
5) Monitoring and Logging
Monitoring tracks system performance. Logging records events. Both help you understand system behaviour.
Use monitoring tools to track CPU, memory, and disk usage. Set alerts for problems. Analyse metrics to find trends.
Use logging to record important events. Store logs securely. Analyse logs to find problems. Correlate logs with monitoring data.
6) Serverless Architecture Design Principles
Serverless architecture builds applications without managing servers. This saves time and money.
Design small, independent functions. Use triggers to start functions. Let the cloud manage resources. Handle errors gracefully.
Test functions thoroughly. Monitor performance and costs. Use serverless for the right workloads.
Remember, these are general guidelines. Specific situations may require different approaches. Always test changes carefully.
Conclusion
This article covered key AWS Lambda interview questions and answers. You learned about Lambda basics, its uses, and how it works. You also saw questions on Lambda functions, triggers, security, and best practices. Understanding these topics will help you prepare for your AWS Lambda interview. To practise more and boost your confidence, use iScalePro. It offers many practice questions and helps you improve your skills.
AWS Lambda Interview FAQs
1) What are the 3 components of AWS Lambda?
AWS Lambda is a serverless computing service that lets you run code without provisioning or managing servers. It has three main parts:
- Function: This is the code you write to perform a specific task. It can be written in various languages like Node.js, Python, Java, C#, and Go.
- Trigger: This is what tells Lambda when to run your function. It can be an event like a file upload, an API call, or a schedule.
- Execution Environment: This is the environment where your function runs. It includes the operating system, libraries, and runtime.
2) What is Lambda in AWS interview questions?
In AWS interviews, Lambda is a popular topic. Interviewers often ask questions about its components, how it works, and how to use it effectively. They may also ask you to solve coding problems or design Lambda functions for specific scenarios.
3) What AWS Lambda is used for?
AWS Lambda is used for many things. Some common use cases include:
- Data processing: Analyzing data from various sources like IoT devices, databases, and logs.
- Web applications: Building and running serverless web applications.
- Mobile backends: Providing backend services for mobile apps.
- API gateways: Handling API requests and responses.
- IoT applications: Processing data from IoT devices.
- Machine learning: Deploying and running machine learning models.
4) What is Lambda role in AWS?
A Lambda role is an IAM role that gives your Lambda function permission to access other AWS resources. It is important to use a dedicated role for each Lambda function to limit its access to only the resources it needs. The role can be used to grant your function access to things like S3 buckets, DynamoDB tables, and other AWS services.
