DEA-C01 · Question #59
A data engineer maintains custom Python scripts that perform a data formatting process that many AWS Lambda functions use. When the data engineer needs to modify the Python scripts, the data…
The correct answer is B. Package the custom Python scripts into Lambda layers. Apply the Lambda layers to the Lambda. After you create a layer, you can apply it to any number of functions in your account. Without layers, you need to include the same dependencies in each individual deployment package. https://docs.aws.amazon.com/lambda/latest/dg/chapter-layers.html
Question
A data engineer maintains custom Python scripts that perform a data formatting process that many AWS Lambda functions use. When the data engineer needs to modify the Python scripts, the data engineer must manually update all the Lambda functions. The data engineer requires a less manual way to update the Lambda functions. Which solution will meet this requirement?
Options
- AStore a pointer to the custom Python scripts in the execution context object in a shared Amazon
- BPackage the custom Python scripts into Lambda layers. Apply the Lambda layers to the Lambda
- CStore a pointer to the custom Python scripts in environment variables in a shared Amazon S3
- DAssign the same alias to each Lambda function. Call reach Lambda function by specifying the
How the community answered
(35 responses)- A3% (1)
- B80% (28)
- C6% (2)
- D11% (4)
Explanation
After you create a layer, you can apply it to any number of functions in your account. Without layers, you need to include the same dependencies in each individual deployment package. https://docs.aws.amazon.com/lambda/latest/dg/chapter-layers.html
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