Databricks Databricks-Machine-Learning-Associate Free Sample Exam Questions 2026

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Page: 1 / 15
Total Questions: 74

Questions & Answers PDF

• Question 1

  

  Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?

  • pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata
  • pandas API on Spark DataFrames are more performant than Spark DataFrames
  • pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata
  • pandas API on Spark DataFrames are less mutable versions of Spark DataFrames
  • pandas API on Spark DataFrames are unrelated to Spark DataFrames

  Reveal Answer Answer: C Next Question

• Question 2

  

  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?

  • They can create a new branch in Databricks, commit their changes, and push those changes to the Git provider.
  • They can clone the notebooks in the repository into a Databricks Workspace folder and make the necessary changes.
  • 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.
  • They can clone the notebooks in the repository into a new Databricks Repo and make the necessary changes.

  Reveal Answer Answer: A Next Question

• Question 3

  

  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?

  • Logistic regression
  • Singular value decomposition
  • Iterative optimization
  • Least-squares method

  Reveal Answer Answer: C Next Question

• Question 4

  

  Which statement describes a Spark ML transformer?

  • A transformer is an algorithm which can transform one DataFrame into another DataFrame
  • A transformer is a hyperparameter grid that can be used to train a model
  • A transformer chains multiple algorithms together to transform an ML workflow
  • A transformer is a learning algorithm that can use a DataFrame to train a model

  Reveal Answer Answer: A Next Question

• Question 5

  

  Which of the following is a benefit of using vectorized pandas UDFs instead of standard PySpark UDFs?

  • The vectorized pandas UDFs allow for the use of type hints
  • The vectorized pandas UDFs process data in batches rather than one row at a time
  • The vectorized pandas UDFs allow for pandas API use inside of the function
  • The vectorized pandas UDFs work on distributed DataFrames
  • The vectorized pandas UDFs process data in memory rather than spilling to disk

  Reveal Answer Answer: B Next Question

• Question 6

  

  A machine learning engineer is trying to scale a machine learning pipeline by distributing its single- node model tuning process. After broadcasting the entire training data onto each core, each core in the cluster can train one model at a time. Because the tuning process is still running slowly, the engineer wants to increase the level of parallelism from 4 cores to 8 cores to speed up the tuning process. Unfortunately, the total memory in the cluster cannot be increased.In which of the following scenarios will increasing the level of parallelism from 4 to 8 speed up the tuning process?

  • When the tuning process in randomized
  • When the entire data can fit on each core
  • When the model is unable to be parallelized
  • When the data is particularly long in shape
  • When the data is particularly wide in shape

  Reveal Answer Answer: B Next Question

• Question 7

  

  Which of the following approaches can be used to view the notebook that was run to create an MLflow run?

  • Open the MLmodel artifact in the MLflow run paqe
  • Click the "Models" link in the row corresponding to the run in the MLflow experiment paqe
  • Click the "Source" link in the row corresponding to the run in the MLflow experiment page
  • Click the "Start Time" link in the row corresponding to the run in the MLflow experiment page

  Reveal Answer Answer: C Next Question

• Question 8

  

  A data scientist is developing a single-node machine learning model. They have a large number of model configurations to test as a part of their experiment. As a result, the model tuning process takes too long to complete. Which of the following approaches can be used to speed up the model tuning process?

  • Implement MLflow Experiment Tracking
  • Scale up with Spark ML
  • Enable autoscaling clusters
  • Parallelize with Hyperopt

  Reveal Answer Answer: D Next Question

• Question 9

  

  A data scientist uses 3-fold cross-validation and the following hyperparameter grid when optimizing model hyperparameters via grid search for a classification problem:Hyperparameter 1: [2, 5, 10]Hyperparameter 2: [50, 100]Which of the following represents the number of machine learning models that can be trained in parallel during this process?

  • 3
  • 5
  • 6
  • 18

  Reveal Answer Answer: D Next Question

• Question 10

  

  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?

  • One-hot encoding is not supported by most machine learning libraries.
  • One-hot encoding is dependent on the target variable's values which differ for each application.
  • One-hot encoding is computationally intensive and should only be performed on small samples oftraining sets for individual machine learning problems.
  • One-hot encoding is not a common strategy for representing categorical feature variables numerically.
  • One-hot encoding is a potentially problematic categorical variable strategy for some machine learning algorithms.

  Reveal Answer Answer: E Next Question

Page: 1 / 15
Total Questions: 74

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