Lambda layers for shared code in AWS - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Lambda Layers Usage

What is the primary benefit of using Lambda layers for shared code in AWS Lambda functions?

A They allow multiple Lambda functions to share common code without duplicating it in each function's deployment package.

B They automatically scale Lambda functions based on the shared code size.

C They provide a graphical interface to edit Lambda function code directly in the AWS console.

D They enable Lambda functions to run in a dedicated virtual private cloud (VPC).

💡 Hint

Think about how to avoid repeating the same code in many places.

Lambda layers let you package libraries and code separately, so multiple functions can use them without copying the code into each function.

❓ Configuration

intermediate

Configuring a Lambda Function with a Layer

You have created a Lambda layer containing shared Python libraries. How do you configure your Lambda function to use this layer?

A Include the layer's code directly inside the Lambda function's deployment package.

B Add the layer's ARN to the Lambda function's Layers configuration in the AWS console or deployment template.

C Set an environment variable pointing to the layer's S3 bucket location.

D Create a new IAM role that grants access to the layer's code.

💡 Hint

Think about how AWS links layers to functions.

Lambda functions use layers by referencing the layer's ARN in their configuration, which tells AWS to include the layer's content at runtime.

❓ Architecture

advanced

Layer Versioning Impact on Lambda Deployment

You have a Lambda layer with multiple versions. Your Lambda function uses version 3 of this layer. You publish version 4 of the layer with updates. What happens to your Lambda function?

A AWS merges versions 3 and 4 and uses the combined code automatically.

B The Lambda function automatically switches to version 4 without redeployment.

C The Lambda function continues using version 3 until you explicitly update it to use version 4.

D The Lambda function fails to run until you update the layer version in its configuration.

💡 Hint

Consider how versioning controls what code a function uses.

Lambda functions use a specific version of a layer. They do not update automatically when new versions are published. You must update the function configuration to use the new version.

❓ security

advanced

Security Best Practices for Lambda Layers

Which practice improves security when using Lambda layers with shared code?

A Disable encryption on the layer's content to improve performance.

B Embed sensitive credentials directly inside the layer's code for easy access.

C Use layers only from public sources without reviewing their content.

D Limit permissions on the layer's S3 bucket and restrict who can publish or update layers.

💡 Hint

Think about controlling access and protecting code integrity.

Restricting access to layer storage and publishing prevents unauthorized changes and protects your Lambda functions from malicious code.

❓ service_behavior

expert

Lambda Layer Size Limits and Deployment Impact

You have a Lambda layer of 60 MB unzipped size. Your Lambda function deployment package is 50 MB unzipped. What is the maximum unzipped size of the combined deployment package and layers at runtime, and what happens if the limit is exceeded?

A The combined unzipped size limit is 250 MB; if exceeded, deployment fails with an error.

B The combined unzipped size limit is 100 MB; if exceeded, Lambda automatically compresses the package to fit.

C There is no size limit for layers combined with deployment packages.

D The combined unzipped size limit is 150 MB; if exceeded, Lambda truncates the package silently.

💡 Hint

Check AWS Lambda limits for deployment package sizes.

AWS Lambda limits the total unzipped size of deployment package plus layers to 250 MB. If this is exceeded, deployment fails and you must reduce size.

Practice

1. What is the main purpose of using Lambda layers in AWS Lambda functions?

easy

A. To share common code and libraries across multiple Lambda functions

B. To increase the memory size of a Lambda function

C. To schedule Lambda functions to run at specific times

D. To monitor Lambda function execution logs

Solution

1. Step 1: Understand Lambda layers purpose

   Lambda layers are designed to hold shared code or libraries that multiple Lambda functions can use.

2. Step 2: Compare options with purpose

   Options A, C, and D describe unrelated Lambda features like memory, scheduling, and logging.

3. Final Answer:

   To share common code and libraries across multiple Lambda functions -> Option A

4. Quick Check:

   Lambda layers = shared code [OK]

Hint: Layers share code across functions, not manage memory or logs [OK]

Common Mistakes:

• Confusing layers with memory settings
• Thinking layers schedule functions
• Mixing layers with logging features

2. Which of the following is the correct way to add a Lambda layer to a function using AWS CLI?

easy

A. aws lambda update-function-configuration --function-name MyFunction --layers arn:aws:lambda:region:account-id:layer:MyLayer:1

B. aws lambda create-layer-version --function-name MyFunction --layer-name MyLayer

C. aws lambda add-layer-version --function-name MyFunction --layer-name MyLayer

D. aws lambda attach-layer --function-name MyFunction --layer-name MyLayer

Solution

1. Step 1: Identify correct CLI command to add layer

   The correct command to add a layer to a Lambda function is update-function-configuration with the --layers parameter specifying the layer ARN.

2. Step 2: Review options for syntax correctness

   aws lambda update-function-configuration --function-name MyFunction --layers arn:aws:lambda:region:account-id:layer:MyLayer:1 uses the correct command and syntax. Options A, B, and D use invalid or non-existent commands.

3. Final Answer:

   aws lambda update-function-configuration --function-name MyFunction --layers arn:aws:lambda:region:account-id:layer:MyLayer:1 -> Option A

4. Quick Check:

   Update function config with layers ARN = aws lambda update-function-configuration --function-name MyFunction --layers arn:aws:lambda:region:account-id:layer:MyLayer:1 [OK]

Hint: Use update-function-configuration with --layers ARN [OK]

Common Mistakes:

• Using create-layer-version to attach layers
• Trying non-existent commands like add-layer-version
• Not specifying full ARN for the layer

3. Given this Python Lambda function code snippet using a layer for shared utilities:

import shared_utils

def lambda_handler(event, context):
    return shared_utils.process(event)

What will happen if the Lambda function is invoked but the layer is not attached?

medium

A. The function will timeout waiting for the layer

B. The function will run successfully without errors

C. The function will return None silently

D. The function will raise a ModuleNotFoundError for 'shared_utils'

Solution

1. Step 1: Understand import behavior without layer

   If the layer providing shared_utils is missing, Python cannot find the module and raises an error.

2. Step 2: Identify the error type

   Python raises ModuleNotFoundError when an import fails.

3. Final Answer:

   The function will raise a ModuleNotFoundError for 'shared_utils' -> Option D

4. Quick Check:

   Missing layer = ModuleNotFoundError [OK]

Hint: Missing layer means missing module, causing import error [OK]

Common Mistakes:

• Assuming function runs without the layer
• Expecting silent failure or None return
• Thinking function times out due to missing layer

4. You updated your Lambda layer code and published a new version. After updating your function configuration to use the new layer version, the function still uses old code. What is the most likely cause?

medium

A. The function needs a restart to pick up the new layer version

B. The function configuration was not updated to the new layer version ARN

C. The new layer version was not published correctly

D. Lambda layers automatically update all functions, so this is impossible

Solution

1. Step 1: Understand layer versioning

   Each layer version has a unique ARN. Functions must explicitly reference the new version ARN to use updated code.

2. Step 2: Identify common mistake

   If the function still uses old code, it likely still references the old layer version ARN.

3. Final Answer:

   The function configuration was not updated to the new layer version ARN -> Option B

4. Quick Check:

   Update function config with new layer ARN to use new code [OK]

Hint: Update function config with new layer ARN after publishing [OK]

Common Mistakes:

• Assuming layers auto-update all functions
• Not publishing new layer version properly
• Thinking function restart is needed

5. You have two Lambda functions, both using a shared layer with version 3. You want to update the shared code without affecting the functions immediately. What is the best practice to update the shared code safely?

hard

A. Delete version 3 and create a new layer with the same version number

B. Overwrite version 3 of the layer with new code so both functions get updates automatically

C. Publish a new layer version 4 with updated code, then update each function to use version 4 when ready

D. Update the functions' code directly instead of using layers

Solution

1. Step 1: Understand layer version immutability

   Lambda layer versions are immutable; you cannot overwrite an existing version.

2. Step 2: Apply best practice for safe updates

   Publish a new version with updated code and update functions individually to control rollout.

3. Final Answer:

   Publish a new layer version 4 with updated code, then update each function to use version 4 when ready -> Option C

4. Quick Check:

   New layer version + update functions individually = safe update [OK]

Hint: Publish new layer version; update functions selectively [OK]

Common Mistakes:

• Trying to overwrite existing layer version
• Deleting and recreating layers with same version
• Updating function code instead of using layers