Databricks Machine Learning Associate exam dumps

Databricks Machine Learning Associate practice question 288 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 288

Select 4

You are tasked with building a machine learning pipeline using Spark ML to predict customer churn. The dataset is stored in a Delta table and includes features like age, account_balance, and tenure. After creating a RandomForestClassifier model, you use the CrossValidator class to evaluate the model's performance. Which of the following steps are necessary to properly train and evaluate the model?

  1. A

    Split the dataset into training and test sets using randomSplit.

  2. B

    Set up a ParamGridBuilder to specify hyperparameter combinations for the CrossValidator.

  3. C

    Use the fit method on the CrossValidator with the test dataset to train the model.

  4. D

    Specify an evaluation metric such as accuracy or f1 for the CrossValidator.

  5. E

    Transform the test dataset using the trained model to evaluate its performance.

Show answer and explanation

Correct answers: A, B, D, E

Explanation

To properly train and evaluate a machine learning model using Spark ML, you need to split the dataset into training and test sets, define hyperparameter combinations with ParamGridBuilder, specify an evaluation metric for the CrossValidator, and evaluate the model on the test set by transforming it. It's important to train the model on the training set only and avoid using the test set during training.

  • A. Correct.

    Correct: Splitting the dataset into training and test sets is a standard practice in machine learning to ensure proper evaluation of model performance.

  • B. Correct.

    Correct: The ParamGridBuilder is used to define hyperparameter combinations for the CrossValidator to test during model tuning.

  • C. Incorrect.

    Incorrect: The fit method on the CrossValidator should only be called on the training dataset, not the test dataset.

  • D. Correct.

    Correct: Specifying an evaluation metric is required for the CrossValidator to assess model performance during hyperparameter tuning.

  • E. Correct.

    Correct: After training the model, the test dataset must be transformed using the trained model to evaluate its performance on unseen data.

Timed practice exam

Take a Databricks Machine Learning Associate practice test under exam conditions

48 questions in 90 minutes, drawn from this bank, with a score report and a per-question review when you finish.

Start timed exam

More Databricks Machine Learning Associate practice questions

  1. 289.You are tasked with building and evaluating a machine learning model using Spark ML in Databricks....
  2. 290.You are tasked with building a machine learning model to predict customer churn using Spark ML in...
  3. 291.You are tasked with building a machine learning model using Spark ML to predict house prices. After...
  4. 292.You are tasked with training and evaluating a machine learning model using Spark ML in Databricks....
  5. 293.You are building a machine learning pipeline in Databricks using Spark ML. You include a...
Back to the Databricks Machine Learning Associate exam dumps