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  1. Home
  2. Databricks Certification
  3. Databricks-Machine-Learning-Associate Exam
  4. Databricks.Databricks-Machine-Learning-Associate.v2025-12-03.q26 Dumps
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Question 16

A data scientist has been given an incomplete notebook from the data engineering team. The notebook uses a Spark DataFrame spark_df on which the data scientist needs to perform further feature engineering. Unfortunately, the data scientist has not yet learned the PySpark DataFrame API.
Which of the following blocks of code can the data scientist run to be able to use the pandas API on Spark?

Correct Answer: A
To use the pandas API on Spark, which is designed to bridge the gap between the simplicity of pandas and the scalability of Spark, the correct approach involves importing the pyspark.pandas (recently renamed to pandas_api_on_spark) module and converting a Spark DataFrame to a pandas-on-Spark DataFrame using this API. The provided syntax correctly initializes a pandas-on-Spark DataFrame, allowing the data scientist to work with the familiar pandas-like API on large datasets managed by Spark.
Reference
Pandas API on Spark Documentation: https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html
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Question 17

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

Correct Answer: C
To view the notebook that was run to create an MLflow run, you can click the "Source" link in the row corresponding to the run in the MLflow experiment page. The "Source" link provides a direct reference to the source notebook or script that initiated the run, allowing you to review the code and methodology used in the experiment. This feature is particularly useful for reproducibility and for understanding the context of the experiment.
Reference:
MLflow Documentation (Viewing Run Sources and Notebooks).
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Question 18

Which statement describes a Spark ML transformer?

Correct Answer: A
In Spark ML, a transformer is an algorithm that can transform one DataFrame into another DataFrame. It takes a DataFrame as input and produces a new DataFrame as output. This transformation can involve adding new columns, modifying existing ones, or applying feature transformations. Examples of transformers in Spark MLlib include feature transformers like StringIndexer, VectorAssembler, and StandardScaler.
Reference:
Databricks documentation on transformers: Transformers in Spark ML
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Question 19

A data scientist learned during their training to always use 5-fold cross-validation in their model development workflow. A colleague suggests that there are cases where a train-validation split could be preferred over k-fold cross-validation when k > 2.
Which of the following describes a potential benefit of using a train-validation split over k-fold cross-validation in this scenario?

Correct Answer: E
A train-validation split is often preferred over k-fold cross-validation (with k > 2) when computational efficiency is a concern. With a train-validation split, only two models (one on the training set and one on the validation set) are trained, whereas k-fold cross-validation requires training k models (one for each fold).
This reduction in the number of models trained can save significant computational resources and time, especially when dealing with large datasets or complex models.
Reference:
Model Evaluation with Train-Test Split
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Question 20

A data scientist has a Spark DataFrame spark_df. They want to create a new Spark DataFrame that contains only the rows from spark_df where the value in column discount is less than or equal 0.
Which of the following code blocks will accomplish this task?

Correct Answer: C
To filter rows in a Spark DataFrame based on a condition, the filter method is used. In this case, the condition is that the value in the "discount" column should be less than or equal to 0. The correct syntax uses the filter method along with the col function from pyspark.sql.functions.
Correct code:
from pyspark.sql.functions import col filtered_df = spark_df.filter(col("discount") <= 0) Option A and D use Pandas syntax, which is not applicable in PySpark. Option B is closer but misses the use of the col function.
Reference:
PySpark SQL Documentation
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