Databricks Databricks Machine Learning Associate Practice Exams
Last updated on Oct 21,2026- Exam Code: Databricks Machine Learning Associate
- Exam Name: Databricks Certified Machine Learning Associate Exam
- Certification Provider: Databricks
- Latest update: Oct 21,2026
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
A data scientist is wanting to explore summary statistics for Spark DataFrame spark_df. The data scientist wants to see the count, mean, standard deviation, minimum, maximum, and interquartile range (IQR) for each numerical feature.
Which of the following lines of code can the data scientist run to accomplish the task?
- A . spark_df.summary ()
- B . spark_df.stats()
- C . spark_df.describe().head()
- D . spark_df.printSchema()
- E . spark_df.toPandas()
In Spark, how would a data scientist filter a DataFrame `spark_df` to include only rows where the column ‘price’ is greater than 0?
- A . `spark_df.filter(col("price") > 0)`
- B . `spark_df[spark_df["price"] > 0]`
- C . `SELECT * FROM spark_df WHERE price > 0`
- D . `spark_df.where("price > 0")`
Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?
- A . pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata
- B . pandas API on Spark DataFrames are more performant than Spark DataFrames
- C . pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata
- D . pandas API on Spark DataFrames are less mutable versions of Spark DataFrames
- E . pandas API on Spark DataFrames are unrelated to Spark DataFrames
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?
- A . Logistic regression
- B . Singular value decomposition
- C . Iterative optimization
- D . Least-squares method
Which of the following machine learning algorithms typically uses bagging?
- A . IGradient boosted trees
- B . K-means
- C . Random Forest
- D . Decision tree
A new data scientist has started working on an existing machine learning project. The project is a scheduled Job that retrains every day. The project currently exists in a Repo in Databricks. The data scientist has been tasked with improving the feature engineering of the pipeline’s preprocessing stage. The data scientist wants to make necessary updates to the code that can be easily adopted into the project without changing what is being run each day.
Which approach should the data scientist take to complete this task?
- A . They can create a new branch in Databricks, commit their changes, and push those changes to the Git provider.
- B . They can clone the notebooks in the repository into a Databricks Workspace folder and make the necessary changes.
- C . They can create a new Git repository, import it into Databricks, and copy and paste the existing code from the original repository before making changes.
- D . They can clone the notebooks in the repository into a new Databricks Repo and make the necessary changes.
A machine learning engineering team has a Job with three successive tasks. Each task runs a single notebook. The team has been alerted that the Job has failed in its latest run.
Which of the following approaches can the team use to identify which task is the cause of the failure?
- A . Run each notebook interactively
- B . Review the matrix view in the Job’s runs
- C . Migrate the Job to a Delta Live Tables pipeline
- D . Change each Task’s setting to use a dedicated cluster
An organization is developing a feature repository and is electing to one-hot encode all categorical feature variables. A data scientist suggests that the categorical feature variables should not be one-hot encoded within the feature repository.
Which of the following explanations justifies this suggestion?
- A . One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms.
- B . One-hot encoding is dependent on the target variable’s values which differ for each apaplication.
- C . One-hot encoding is computationally intensive and should only be performed on small samples of training sets for individual machine learning problems.
- D . One-hot encoding is not a common strategy for representing categorical feature variables numerically.
Which statement describes a Spark ML transformer?
- A . A transformer is an algorithm which can transform one DataFrame into another DataFrame
- B . A transformer is a hyperparameter grid that can be used to train a model
- C . A transformer chains multiple algorithms together to transform an ML workflow
- D . A transformer is a learning algorithm that can use a DataFrame to train a model