Validation Testing MACHINE-LEARNING-PROFESSIONAL Practice Questions (Page 1)

Questions 1–5

1. 1foundation · easy

   What is a common challenge of organizing unit tests in a separate file from the functions they test?

   • AIt increases the execution time of the tests
   • BIt prevents the use of unit testing frameworks
   • CIt makes the code less readable
   • DIt makes it harder to keep tests and code in sync
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2. 2application · medium

   A team has a large Databricks notebook with many functions. They want to add unit tests but are concerned about the notebook becoming too long and hard to maintain. What is the most effective approach?

   • ARefactor the functions into a Python package and create a separate test suite that imports the package.
   • BAdd test cells at the bottom of the notebook and use a parameter to skip them in production.
   • CWrite tests in a separate notebook that redefines the functions to test.
   • DSkip unit testing and rely on integration tests only.
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4. 3application · medium

   A data scientist has a function in a Databricks notebook that calculates a feature. They want to write a unit test to verify the function returns the correct output for a given input. What is the best practice for this?

   • AWrite the test in the same notebook cell as the function.
   • BUse the Databricks REST API to call the function.
   • CRun the function in a Databricks job and check the logs.
   • DCreate a separate test notebook that imports the function from a Python file.
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5. 4application · medium

   A data scientist has written a function in a Databricks notebook that cleans text data. They want to write a unit test for this function. What is the best way to do this?

   • AUse the Databricks CLI to run the function in a separate environment.
   • BMove the function to a Python file and create a test notebook that imports the file and runs assertions.
   • CAdd a cell in the same notebook that calls the function with sample inputs and checks the output.
   • DCreate a separate notebook that imports the function from the original notebook and runs assertions.
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6. 5foundation · easy

   A data scientist writes a function in a Databricks notebook to compute the mean of a list of numbers. To verify the function returns the expected output for a given input, which approach should be used?

   • ARun the function in a separate cluster and compare the output to a known value
   • BUse a SQL query to validate the function's logic against a sample table
   • CManually inspect the function's code to confirm it is correct
   • DWrite a unit test that calls the function with a known input and asserts the output equals the expected value
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