Databricks Machine Learning Professional exam dumps

Databricks Machine Learning Professional practice question 235 of 280

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

Databricks Machine Learning Professional Question 235

Single answer

You deployed a machine learning model to predict customer churn based on historical data. Over time, the input data distribution for key features such as 'customer engagement score' and 'monthly spend' has shifted significantly due to changes in customer behavior patterns. What is the most likely explanation for the model's declining accuracy, and what action should you prioritize?

  1. A

    The model is overfitting to the training data, and you should add more regularization.

  2. B

    The model is experiencing concept drift, and you should retrain it with more recent data.

  3. C

    The model's architecture is outdated, and you should switch to a more complex algorithm.

  4. D

    The input data contains missing values, and you should handle them using imputation techniques.

Show answer and explanation

Correct answer: B

Explanation

Concept drift refers to the change in the statistical properties of input features or the target variable over time, which can negatively affect model performance. In this scenario, customer behavior patterns have changed, causing the input data distribution to shift. Retraining the model with recent data allows it to adapt to the new patterns and mitigate the effects of concept drift.

  • A. Incorrect.

    Overfitting refers to a model performing well on training data but poorly on unseen data, which is not relevant in this case as the issue is related to changing input data distributions over time.

  • B. Correct.

    Concept drift occurs when the statistical properties of the target variable or input data change over time, leading to a decline in model performance. Retraining the model with recent data can help it adapt to the new patterns.

  • C. Incorrect.

    Changing the model architecture is not the immediate solution to address concept drift. The issue is related to data changes, not the complexity of the algorithm.

  • D. Incorrect.

    While missing values can impact a model's performance, the scenario described involves changes in data distribution, not missing data.

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