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Databricks Certified Machine Learning Associate (Databricks Certified Machine Learning) - Databricks Exam Questions

Last updated on June 20, 2026

97% Exam Compliance
74 Total Questions
1
Question
A data scientist has replaced missing values in their feature set with each respective feature variable’s median value. A colleague suggests that the data scientist is throwing away valuable information by doing this.

Which of the following approaches can they take to include as much information as possible in the feature set?
Options
A Refrain from imputing the missing values in favor of letting the machine learning algorithm determine how to handle them
B Create a constant feature variable for each feature that contained missing values indicating the percentage of rows from the feature that was originally missing
C Create a binary feature variable for each feature that contained missing values indicating whether each row's value has been imputed
D Remove all feature variables that originally contained missing values from the feature set
E Impute the missing values using each respective feature variable's mean value instead of the median value
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2
Question
The implementation of linear regression in Spark ML first attempts to solve the linear regression problem using matrix decomposition, but this method does not scale well to large datasets with a large number of variables.

Which of the following approaches does Spark ML use to distribute the training of a linear regression model for large data?
Options
A Logistic regression
B Least-squares method
C Iterative optimization
D Singular value decomposition
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3
Question
A data scientist has developed a random forest regressor rfr and included it as the final stage in a Spark MLPipeline pipeline. They then set up a cross-validation process with pipeline as the estimator in the following code block:

Exhibit

Which of the following is a negative consequence of including pipeline as the estimator in the cross- validation process rather than rfr as the estimator?
Options
A The process will leak data from the training set to the test set during the evaluation phase
B The process will leak data prep information from the validation sets to the training sets for each model
C The process will be unable to parallelize tuning due to the distributed nature of pipeline
D The process will have a longer runtime because all stages of pipeline need to be refit or retransformed with each mode
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4
Question
A data scientist wants to tune a set of hyperparameters for a machine learning model. They have wrapped a Spark ML model in the objective function objective_function and they have defined the search space search_space.

As a result, they have the following code block:

Exhibit

Which of the following changes do they need to make to the above code block in order to accomplish the task?
Options
A Reduce num_evals to be less than 10
B Remove the algo=tpe.suggest argument
C Remove the trials=trials argument
D Change fmin() to fmax()
E Change SparkTrials() to Trials()
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5
Question
In which of the following situations is it preferable to impute missing feature values with their median value over the mean value?
Options
A When the features are of the boolean type
B When the features contain no missing no values
C When the features contain no outliers
D When the features contain a lot of extreme outliers
E When the features are of the categorical type
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