AWS Lambda

  • A serverless compute service.
  • Lambda executes your code only when needed and scales automatically.
  • Lambda functions are stateless – no affinity to the underlying infrastructure.
  • You choose the amount of memory you want to allocate to your functions and AWS Lambda allocates proportional CPU power, network bandwidth, and disk I/O.
  • AWS Lambda is SOC, HIPAA, PCI, ISO compliant.
  • Natively supports the following languages:
    • Node.js
    • Java
    • C#
    • Go
    • Python
    • Ruby
    • PowerShell
  • You can also provide your own custom runtime.

Components of a Lambda Application

  • Function – a script or program that runs in Lambda. Lambda passes invocation events to your function. The function processes an event and returns a response.
  • Runtimes – Lambda runtimes allow functions in different languages to run in the same base execution environment. The runtime sits in-between the Lambda service and your function code, relaying invocation events, context information, and responses between the two.
  • Layers – Lambda layers are a distribution mechanism for libraries, custom runtimes, and other function dependencies. Layers let you manage your in-development function code independently from the unchanging code and resources that it uses.
  • Event source – an AWS service or a custom service that triggers your function and executes its logic.
  • Downstream resources – an AWS service that your Lambda function calls once it is triggered.
  • Log streams – While Lambda automatically monitors your function invocations and reports metrics to CloudWatch, you can annotate your function code with custom logging statements that allow you to analyze the execution flow and performance of your Lambda function.
  • AWS Serverless Application Model

Introduction to AWS Lambda & Serverless Applications

Lambda Functions

  • You upload your application code in the form of one or more Lambda functions. Lambda stores code in Amazon S3 and encrypts it at rest.
  • To create a Lambda function, you first package your code and dependencies in a deployment package. Then, you upload the deployment package to create your Lambda function.
  • After your Lambda function is in production, Lambda automatically monitors functions on your behalf, reporting metrics through Amazon CloudWatch.
  • IT Certification Category (English)728x90
  • Configure basic function settings including the description, memory usage, execution timeout, and role that the function will use to execute your code.
  • Environment variables are always encrypted at rest, and can be encrypted in transit as well.
  • Versions and aliases are secondary resources that you can create to manage function deployment and invocation.
  • A layer is a ZIP archive that contains libraries, a custom runtime, or other dependencies. Use layers to manage your function’s dependencies independently and keep your deployment package small.
  • You can configure a function to mount an Amazon EFS file system to a local directory. With Amazon EFS, your function code can access and modify shared resources securely and at high concurrency.

Invoking Functions

  • Lambda supports synchronous and asynchronous invocation of a Lambda function. You can control the invocation type only when you invoke a Lambda function (referred to as on-demand invocation).
  • An event source is the entity that publishes events, and a Lambda function is the custom code that processes the events.
  • Event source mapping maps an event source to a Lambda function. It enables automatic invocation of your Lambda function when events occur. 
  • Lambda provides event source mappings for the following services.
    • Amazon Kinesis
    • Amazon DynamoDB
    • Amazon Simple Queue Service
  • Your functions’ concurrency is the number of instances that serve requests at a given time. When your function is invoked, Lambda allocates an instance of it to process the event. When the function code finishes running, it can handle another request. If the function is invoked again while a request is still being processed, another instance is allocated, which increases the function’s concurrency.
  • To ensure that a function can always reach a certain level of concurrency, you can configure the function with reserved concurrency. When a function has reserved concurrency, no other function can use that concurrency. Reserved concurrency also limits the maximum concurrency for the function.
  • To enable your function to scale without fluctuations in latency, use provisioned concurrency. By allocating provisioned concurrency before an increase in invocations, you can ensure that all requests are served by initialized instances with very low latency.

Configuring a Lambda Function to Access Resources in a VPC

In AWS Lambda, you can set up your function to establish a connection to your virtual private cloud (VPC). With this connection, your function can access the private resources of your VPC during execution like EC2, RDS and many others.

By default, AWS executes your Lambda function code securely within a VPC. Alternatively, you can enable your Lambda function to access resources inside your private VPC by providing additional VPC-specific configuration information such as VPC subnet IDs and security group IDs. It uses this information to set up elastic network interfaces which enable your Lambda function to connect securely to other resources within your VPC.



  • Lets you run Lambda functions to customize content that CloudFront delivers, executing the functions in AWS locations closer to the viewer. The functions run in response to CloudFront events, without provisioning or managing servers.
  • You can use Lambda functions to change CloudFront requests and responses at the following points:
    • After CloudFront receives a request from a viewer (viewer request)
    • Before CloudFront forwards the request to the origin (origin request)
    • After CloudFront receives the response from the origin (origin response)
    • Before CloudFront forwards the response to the viewer (viewer response)

AWS Training Lambda

  • You can automate your serverless application’s release process using AWS CodePipeline and AWS CodeDeploy.
  • Lambda will automatically track the behavior of your Lambda function invocations and provide feedback that you can monitor. In addition, it provides metrics that allows you to analyze the full function invocation spectrum, including event source integration and whether downstream resources perform as expected.


  • You are charged based on the total number of requests for your functions and the duration, the time it takes for your code to execute.

Additional AWS Lambda-related Cheat Sheets:


Validate Your Knowledge

Question 1

Your company has recently deployed a new web application which uses a serverless-based architecture in AWS. Your manager instructed you to implement CloudWatch metrics to monitor your systems more effectively. You know that Lambda automatically monitors functions on your behalf and reports metrics through Amazon CloudWatch.

In this scenario, what types of data do these metrics monitor? (Choose 2)

  1. ReservedConcurrentExecutions
  2. Invocations
  3. DeadLetterErrors
  4. IteratorSize
  5. ApproximateAgeOfOldestMessage

Correct Answers: 2,3

AWS Lambda automatically monitors functions on your behalf, reporting metrics through Amazon CloudWatch. These metrics include total invocation requests, latency, and error rates. The throttles, Dead Letter Queues errors and Iterator age for stream-based invocations are also monitored.

You can monitor metrics for Lambda and view logs by using the Lambda console, the CloudWatch console, the AWS CLI, or the CloudWatch API. 


Option 1 is incorrect because CloudWatch does not monitor Lambda’s reserved concurrent executions. You can view it through the Lambda console or via CLI manually.

Options 4 and 5 are incorrect because these two are not Lambda metrics.


Tutorials Dojo Study Guide and Cheatsheet

Question 2

You are deploying the package of your Lambda function, which is compressed as a ZIP file, to AWS. However, you are getting an error in the deployment process because the package is too large. Your manager instructed you to keep your deployment package small to make the development process much easier and more modularized. This should also help you avoid errors that can occur when you install and package dependencies with your function code.

Which of the following options is the MOST suitable solution that you as a developer should implement?

  1. Upload the deployment package to S3.
  2. Zip the deployment package again to further compress the zip file.
  3. Upload the other dependencies of your function as a separate Lambda Layer instead.
  4. Compress the deployment package as TAR file instead.

Correct Answer: 3

You can configure your Lambda function to pull in additional code and content in the form of layers. A layer is a ZIP archive that contains libraries, a custom runtime, or other dependencies. With layers, you can use libraries in your function without needing to include them in your deployment package.

Layers let you keep your deployment package small, which makes development easier. You can avoid errors that can occur when you install and package dependencies with your function code. For Node.js, Python, and Ruby functions, you can develop your function code in the Lambda console as long as you keep your deployment package under 3 MB.

A function can use up to 5 layers at a time. The total unzipped size of the function and all layers can’t exceed the unzipped deployment package size limit of 250 MB.

You can create layers, or use layers published by AWS and other AWS customers. Layers support resource-based policies for granting layer usage permissions to specific AWS accounts, AWS Organizations, or all accounts. Layers are extracted to the /opt directory in the function execution environment. Each runtime looks for libraries in a different location under /opt, depending on the language. Structure your layer so that function code can access libraries without additional configuration.

Hence, the correct answer is to upload the other dependencies of your function as a separate Lambda Layer instead.

Uploading the deployment package to S3 is incorrect because although you can upload large deployment packages of over 50 MB in size via S3, your function will still be in a single layer. This doesn’t meet the requirement of making the deployment package small and modularized. You have to use Lambda Layers instead.

Zipping the deployment package again to further compress the zip file is incorrect because doing this will not significantly make the ZIP file smaller.  

Compressing the deployment package as TAR file instead is incorrect because although it may decrease the size of the deployment package, it is still not enough to totally solve the issue. A compressed TAR file is not significantly smaller as compared to a ZIP file.



For more AWS practice exam questions with detailed explanations, check this out:Tutorials Dojo AWS Practice Tests


Additional Training Materials: AWS Lambda Video Courses on Udemy

  1. AWS Serverless APIs & Apps – A Complete Introduction by Maximilian Schwarzmüller
  2. AWS Lambda & Serverless Architecture Bootcamp (Build 5 Apps) by Riyaz Sayyad
  3. Build a Serverless App with AWS Lambda – Hands On! by Sundog Education
  4. AWS Lambda and the Serverless Framework – Hands On Learning! by Stephane Maarek



Pass your AWS and Azure Certifications with the Tutorials Dojo Portal

Tutorials Dojo portal

Our Bestselling AWS Certified Solutions Architect Associate Practice Exams

AWS Certified Solutions Architect Associate Practice Exams

Enroll Now – Our AWS Practice Exams with 95% Passing Rate

AWS Practice Exams Tutorials Dojo

Enroll Now – Our Azure Certification Exam Reviewers

azure reviewers tutorials dojo

Tutorials Dojo Study Guide and Cheat Sheets eBooks

Tutorials Dojo Study Guide and Cheat Sheets-2

FREE Intro to Cloud Computing for Beginners

FREE AWS Practice Test Samplers

Browse Other Courses

Generic Category (English)300x250

Recent Posts