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Databricks Machine Learning Associate exam dumps: questions 601 to 655 of 655

Page 7 of the free Databricks Machine Learning Associate question bank for the Databricks Certified Machine Learning Associate exam. Questions 601 to 655 are listed below, the first 5 in full with answers and explanations. Back to page 1 for the exam overview and FAQ.

Question bank last updated January 2025

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Free Databricks Machine Learning Associate practice questions

Questions 601 to 605 of 655

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Databricks Machine Learning Associate Question 601

Select 2

You are tasked with evaluating the performance of a regression model built to predict house prices. The model predictions and true values are provided, and you compute the following metrics: RMSE = 25, MAE = 20, and R-squared = 0.85. Based on the metrics, which of the following statements accurately describe the model's performance?

A
The model's predictions are, on average, 25 units away from the true values.
B
The model captures 85% of the variance in the target variable.
C
The model's predictions have a mean absolute error of 25 units.
D
The RMSE being higher than the MAE indicates the presence of large prediction errors.
E
An R-squared value of 0.85 means the model is underfitting the data.

Show answer and explanation

Correct answers: B, D

Explanation

To evaluate regression models, metrics like RMSE, MAE, and R-squared provide insights into the model's error and explanatory power. A high R-squared value (0.85 here) indicates that the model explains a significant portion of the variance in the target variable. Additionally, the relationship between RMSE and MAE can indicate the presence of large errors, as RMSE penalizes larger deviations more than MAE.

A. Incorrect.
This statement is incorrect because the RMSE (Root Mean Squared Error) represents the square root of the average squared differences between predicted and actual values, not the average absolute difference.
B. Correct.
This statement is correct. R-squared (coefficient of determination) quantifies the proportion of the variance in the target variable explained by the model. A value of 0.85 indicates the model captures 85% of the variance.
C. Incorrect.
This statement is incorrect because the Mean Absolute Error (MAE) is explicitly provided as 20, not 25.
D. Correct.
This statement is correct. RMSE being higher than MAE suggests the presence of larger errors in the predictions, as RMSE penalizes larger errors more heavily due to squaring.
E. Incorrect.
This statement is incorrect. An R-squared value of 0.85 indicates good performance and does not imply underfitting. Underfitting typically results in a low R-squared value.

Databricks Machine Learning Associate Question 602

Select 3

You are training a regression model to predict house prices based on features such as square footage, number of bedrooms, and location. After training the model, you evaluate its performance using multiple metrics. Which of the following metrics indicate how well the model explains the variance in the target variable and how far the predictions are from the actual values on average?

A
Root Mean Squared Error (RMSE)
B
Mean Absolute Error (MAE)
C
R-squared
D
Precision
E
Mean Squared Error (MSE)

Show answer and explanation

Correct answers: A, B, C

Explanation

In regression problems, metrics like RMSE and MAE are used to evaluate the average magnitude of prediction errors, while R-squared is used to understand how well the model explains the variance in the target variable. Precision is irrelevant for regression tasks as it is a classification metric, and while MSE is related to RMSE, it is less interpretable because it is not in the same unit as the target variable.

A. Correct.
Root Mean Squared Error (RMSE) measures the average magnitude of the prediction errors, with larger errors being penalized more heavily due to squaring.
B. Correct.
Mean Absolute Error (MAE) calculates the average absolute difference between predicted and actual values, making it a useful metric for understanding average prediction error.
C. Correct.
R-squared explains the proportion of the variance in the target variable that is captured by the model, providing a measure of the model’s explanatory power.
D. Incorrect.
Precision is a classification metric that measures the proportion of true positive predictions out of all positive predictions, and it is not applicable to regression problems.
E. Incorrect.
Mean Squared Error (MSE) measures the average squared difference between predictions and actual values, but it is not as interpretable as RMSE or MAE in terms of the unit of the target variable.

Databricks Machine Learning Associate Question 603

Select 3

You have trained a regression model to predict house prices using a dataset containing features such as the number of bedrooms, square footage, and location. After evaluating the model, you obtain the following metrics: RMSE = 25,000, MAE = 20,000, and R-squared = 0.85. Which of the following conclusions can you draw from these metrics?

A
The RMSE being higher than the MAE indicates that the model's predictions include some larger errors.
B
An R-squared value of 0.85 suggests that 85% of the variance in the target variable is explained by the model.
C
A lower MAE compared to RMSE always means that the model is underfitting.
D
If R-squared is close to 1, the model perfectly predicts all data points in the test set.
E
The combination of RMSE, MAE, and R-squared metrics provides insights into both the accuracy of the model and the quality of the predictions.

Show answer and explanation

Correct answers: A, B, E

Explanation

Evaluating a regression model requires understanding various metrics such as RMSE, MAE, and R-squared. RMSE and MAE provide insights into the magnitude and average of prediction errors, while R-squared measures how well the model explains the variance in the data. Combining these metrics helps assess both the accuracy and quality of the model's predictions. In this scenario, the provided metrics indicate a reasonably accurate model with some larger prediction errors, as shown by the RMSE being higher than the MAE.

A. Correct.
Correct. When RMSE is higher than MAE, it indicates that the model's prediction errors include some large deviations, as RMSE penalizes larger errors more heavily compared to MAE.
B. Correct.
Correct. R-squared measures how well the model explains the variance in the target variable, and a value of 0.85 indicates that 85% of the variance is explained by the model.
C. Incorrect.
Incorrect. A lower MAE compared to RMSE does not necessarily indicate underfitting. It simply reflects the distribution of errors, with RMSE being more sensitive to large errors.
D. Incorrect.
Incorrect. An R-squared value close to 1 indicates high explanatory power but does not guarantee perfect predictions for all data points. Other metrics, like RMSE or MAE, are needed to assess prediction accuracy.
E. Correct.
Correct. RMSE evaluates the magnitude of errors, MAE measures the average error, and R-squared evaluates variance explained, providing a comprehensive view of model performance.

Databricks Machine Learning Associate Question 604

Select 3

A data scientist is evaluating the performance of a regression model predicting house prices based on historical data. They calculate the following metrics for the model: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. Which of the following statements correctly interprets these metrics?

A
A lower RMSE indicates better predictive performance.
B
MAE penalizes larger errors more heavily than RMSE.
C
R-squared represents the proportion of variance in the target variable explained by the model.
D
An R-squared value of 0.95 indicates the model explains almost all variability in the target variable.

Show answer and explanation

Correct answers: A, C, D

Explanation

Understanding regression metrics is crucial for evaluating model performance. RMSE measures the average magnitude of prediction errors, penalizing large errors more heavily than MAE. R-squared provides a measure of how well the model explains the variability in the target variable. Correctly interpreting these metrics ensures the data scientist can assess model effectiveness appropriately.

A. Correct.
Correct. Lower RMSE values indicate the model's predictions are closer to the actual values, reflecting better performance.
B. Incorrect.
Incorrect. RMSE penalizes larger errors more heavily due to the squaring of errors, whereas MAE provides a linear penalty.
C. Correct.
Correct. R-squared quantifies the proportion of variance in the dependent variable that can be explained by the independent variables in the model.
D. Correct.
Correct. An R-squared value of 0.95 suggests that 95% of the variability in the target variable is explained by the model, indicating strong predictive performance.

Databricks Machine Learning Associate Question 605

Select 3

You are training a regression model in Databricks to predict house prices based on features like square footage, number of bedrooms, and location. After training the model, you evaluate it using the following metrics: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). Which of the following statements correctly interprets these metrics?

A
A lower RMSE indicates that the model's predictions are closer to the actual values on average.
B
MAE measures the average of the squared differences between predicted and actual values.
C
R-squared represents the proportion of variance in the target variable that is explained by the model.
D
A higher R-squared value indicates that the model has better predictive performance.
E
MAE is more sensitive to outliers compared to RMSE.

Show answer and explanation

Correct answers: A, C, D

Explanation

Regression metrics like RMSE, MAE, and R-squared provide key insights into the performance of a regression model. RMSE quantifies the spread of prediction errors, MAE measures average prediction error without squaring, and R-squared explains how well the model captures the variance in the target variable. A good understanding of these metrics helps assess model quality and identify areas for improvement.

A. Correct.
Correct: RMSE measures the standard deviation of the residuals (prediction errors). A lower RMSE indicates better model performance as the predictions are closer to the actual values.
B. Incorrect.
Incorrect: MAE measures the average of the absolute differences between predicted and actual values, not the squared differences.
C. Correct.
Correct: R-squared quantifies the proportion of the variance in the dependent variable that is explained by the independent variables in the model.
D. Correct.
Correct: A higher R-squared value generally indicates that the model explains more variability in the target variable, suggesting better performance.
E. Incorrect.
Incorrect: RMSE is more sensitive to outliers than MAE because it squares the errors, amplifying larger deviations.

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Databricks Machine Learning Associate practice questions 601 to 655 of 655

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