Databricks Machine Learning Associate exam dumps

Databricks Machine Learning Associate practice question 279 of 656

Databricks Certified Machine Learning Associate. Associate level, Databricks. Free question with the correct answer and a full explanation.

Databricks Machine Learning Associate Question 279

Single answer

You are working on a machine learning project in Databricks using Spark ML. You have a DataFrame named data containing features and labels for a supervised learning problem. You need to split the data into training and test sets with 80% of the data used for training and 20% for testing. Which of the following code snippets correctly performs this data split?

  1. A

    trainingData, testData = data.randomSplit([0.8, 0.2])

  2. B

    trainingData, testData = data.split([0.8, 0.2])

  3. C

    trainingData, testData = data.randomSplit([0.8, 0.2], seed=42)

  4. D

    trainingData, testData = data.random_split([0.8, 0.2])

Show answer and explanation

Correct answer: C

Explanation

In Spark ML, the randomSplit method is used to split a DataFrame into multiple subsets based on specified proportions. Adding a seed ensures that the split is reproducible, which is important for debugging and consistency in experiments. The correct syntax is data.randomSplit([proportion1, proportion2], seed=optional_seed).

  • A. Incorrect.

    This option is almost correct, but it does not include a seed for reproducibility, which is a recommended practice when splitting data.

  • B. Incorrect.

    This option is incorrect because Spark ML does not have a split method. The method to use is randomSplit.

  • C. Correct.

    This option is correct because it uses the randomSplit method with the specified proportions (80% and 20%) and includes a seed for reproducibility.

  • D. Incorrect.

    This option is incorrect because random_split is not a valid method in Spark ML. The method should be randomSplit.

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