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Databricks Databricks-Machine-Learning-Associate Exam Sample Questions


Question # 1

A data scientist is using Spark SQL to import their data into a machine learning pipeline. Once the data is imported, the data scientist performs machine learning tasks using Spark ML. Which of the following compute tools is best suited for this use case?
A. Single Node cluster
B. Standard cluster
C. SQL Warehouse
D. None of these compute tools support this task


B. Standard cluster
Explanation:

For a data scientist using Spark SQL to import data and then performing machine learning tasks using Spark ML, the best-suited compute tool is a Standard cluster. A Standard cluster in Databricks provides the necessary resources and scalability to handle large datasets and perform distributed computing tasks efficiently, making it ideal for running Spark SQL and Spark ML operations.

References:

Databricks documentation on clusters: Clusters in Databricks




Question # 2

Which of the following evaluation metrics is not suitable to evaluate runs in AutoML experiments for regression problems?
A. F1
B. R-squared
C. MAE
D. MSE


A. F1
Explanation:

The code block provided by the machine learning engineer will perform the desired inference when the Feature Store feature set was logged with the model at model_uri. This ensures that all necessary feature transformations and metadata are available for the model to make predictions. The Feature Store in Databricks allows for seamless integration of features and models, ensuring that the required features are correctly used during inference.

References:

Databricks documentation on Feature Store: Feature Store in Databricks




Question # 3

In which of the following situations is it preferable to impute missing feature values with their median value over the mean value?
A. When the features are of the categorical type
B. When the features are of the boolean type
C. When the features contain a lot of extreme outliers
D. When the features contain no outliers
E. When the features contain no missingno values


C. When the features contain a lot of extreme outliers
Explanation:

Imputing missing values with the median is often preferred over the mean in scenarios where the data contains a lot of extreme outliers. The median is a more robust measure of central tendency in such cases, as it is not as heavily influenced by outliers as the mean. Using the median ensures that the imputed values are more representative of the typical data point, thus preserving the integrity of the dataset's distribution. The other options are not specifically relevant to the question of handling outliers in numerical data.

References:

Data Imputation Techniques (Dealing with Outliers).





Question # 4

A data scientist has created two linear regression models. The first model uses price as a label variable and the second model uses log(price) as a label variable. When evaluating the RMSE of each model bycomparing the label predictions to the actual price values, the data scientist notices that the RMSE for the second model is much larger than the RMSE of the first model. Which of the following possible explanations for this difference is invalid?
A. The second model is much more accurate than the first model
B. The data scientist failed to exponentiate the predictions in the second model prior tocomputingthe RMSE
C. The datascientist failed to take the logof the predictions in the first model prior to computingthe RMSE
D. The first model is much more accurate than the second model
E. The RMSE is an invalid evaluation metric for regression problems


E. The RMSE is an invalid evaluation metric for regression problems
Explanation:

The Root Mean Squared Error (RMSE) is a standard and widely used metric for evaluating the accuracy of regression models. The statement that it is invalid is incorrect. Here’s a breakdown of why the other statements are or are not valid:

Transformations and RMSE Calculation:If the model predictions were transformed (e.g., using log), they should be converted back to their original scale before calculating RMSE to ensure accuracy in the evaluation. Missteps in this conversion process can lead to misleading RMSE values.

Accuracy of Models:Without additional information, we can't definitively say which model is more accurate without considering their RMSE values properly scaled back to the original price scale. Appropriateness of RMSE:RMSE is entirely valid for regression problems as it provides a measure of how accurately a model predicts the outcome, expressed in the same units as the dependent variable.

References

"Applied Predictive Modeling" by Max Kuhn and Kjell Johnson (Springer, 2013), particularly the chapters discussing model evaluation metrics.





Question # 5

A machine learning engineer is converting a decision tree from sklearn to Spark ML. They notice that they are receiving different results despite all of their data and manually specified hyperparameter values being identical. Which of the following describes a reason that the single-node sklearn decision tree and the Spark ML decision tree can differ?
A. Spark ML decision trees test every feature variable in the splitting algorithm
B. Spark ML decision trees automatically prune overfit trees
C. Spark ML decision trees test more split candidates in the splitting algorithm
D. Spark ML decision trees test a random sample of feature variables in the splitting algorithm
E. Spark ML decision trees test binned features values as representative split candidates


E. Spark ML decision trees test binned features values as representative split candidates
Explanation:

One reason that results can differ between sklearn and Spark ML decision trees, despite identical data and hyperparameters, is that Spark ML decision trees test binned feature values as representative split candidates. Spark ML uses a method called "quantile binning" to reduce the number of potential split points by grouping continuous features into bins. This binning process can lead to different splits compared to sklearn, which tests all possible split points directly. This difference in the splitting algorithm can cause variations in the resulting trees.

References:

Spark MLlib Documentation (Decision Trees and Quantile Binning).




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