Free Databricks Databricks-Certified-Machine-Learning-Associate Exam Questions
Absolute Free Databricks-Certified-Machine-Learning-Associate Exam Practice for Comprehensive Preparation
-
Databricks Databricks-Certified-Machine-Learning-Associate Exam Questions
-
Provided By: Databricks
- Exam: Databricks Certified Machine Learning Associate
- Certification: Databricks Certified Associate
-
Total Questions: 75
-
Updated On: Apr 05, 2025
-
Rated: 4.9 |
-
Online Users: 150
-
Question 1
-
What is the name of the method that transforms categorical features into a series of binary indicator feature variables?
Answer: C
-
What is the name of the method that transforms categorical features into a series of binary indicator feature variables?
-
Question 2
-
A data scientist is wanting to explore the Spark DataFrame spark_df. The data scientist wants visual histograms displaying the distribution of numeric features to be included in the exploration.
Which of the following lines of code can the data scientist run to accomplish the task?
Answer: E
-
A data scientist is wanting to explore the Spark DataFrame spark_df. The data scientist wants visual histograms displaying the distribution of numeric features to be included in the exploration.
-
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?
Answer: C
-
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.
-
Question 4
-
Which of the following evaluation metrics is not suitable to evaluate runs in AutoML experiments for regression problems?
Answer: A
-
Which of the following evaluation metrics is not suitable to evaluate runs in AutoML experiments for regression problems?
-
Question 5
-
A data scientist is performing hyperparameter tuning using an iterative optimization algorithm. Each evaluation of unique hyperparameter values is being trained on a single compute node. They are performing eight total evaluations across eight total compute nodes. While the accuracy of the model does vary over the eight evaluations, they notice there is no trend of improvement in the accuracy. The data scientist believes this is due to the parallelization of the tuning process.
Which change could the data scientist make to improve their model accuracy over the course of their tuning process?
Answer: C
-
A data scientist is performing hyperparameter tuning using an iterative optimization algorithm. Each evaluation of unique hyperparameter values is being trained on a single compute node. They are performing eight total evaluations across eight total compute nodes. While the accuracy of the model does vary over the eight evaluations, they notice there is no trend of improvement in the accuracy. The data scientist believes this is due to the parallelization of the tuning process.