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Quiz 2025 Databricks Updated Latest Databricks-Machine-Learning-Associate Dumps Questions

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Databricks Databricks-Machine-Learning-Associate Exam Syllabus Topics:

Topic
Details

Topic 1

ML Workflows: The topic focuses on Exploratory Data Analysis, Feature Engineering, Training, Evaluation and Selection.

Topic 2

Spark ML: It discusses the concepts of Distributed ML. Moreover, this topic covers Spark ML Modeling APIs, Hyperopt, Pandas API, Pandas UDFs, and Function APIs.

Topic 3

Databricks Machine Learning: It covers sub-topics of AutoML, Databricks Runtime, Feature Store, and MLflow.

Topic 4

Scaling ML Models: This topic covers Model Distribution and Ensembling Distribution.

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Databricks Certified Machine Learning Associate Exam Sample Questions (Q39-Q44):

NEW QUESTION # 39
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 clone the notebooks in the repository into a new Databricks Repo and make the necessary changes.
B. They can create a new branch in Databricks, commit their changes, and push those changes to the Git provider.
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 Databricks Workspace folder and make the necessary changes.

Answer: B

Explanation:
The best approach for the data scientist to take in this scenario is to create a new branch in Databricks, commit their changes, and push those changes to the Git provider. This approach allows the data scientist to make updates and improvements to the feature engineering part of the preprocessing pipeline without affecting the main codebase that runs daily. By creating a new branch, they can work on their changes in isolation. Once the changes are ready and tested, they can be merged back into the main branch through a pull request, ensuring a smooth integration process and allowing for code review and collaboration with other team members.
Reference:
Databricks documentation on Git integration: Databricks Repos

NEW QUESTION # 40
A health organization is developing a classification model to determine whether or not a patient currently has a specific type of infection. The organization's leaders want to maximize the number of positive cases identified by the model.
Which of the following classification metrics should be used to evaluate the model?

A. Accuracy
B. Area under the residual operating curve
C. Precision
D. RMSE
E. Recall

Answer: E

Explanation:
When the goal is to maximize the identification of positive cases in a classification task, the metric of interest is Recall. Recall, also known as sensitivity, measures the proportion of actual positives that are correctly identified by the model (i.e., the true positive rate). It is crucial for scenarios where missing a positive case (false negative) has serious implications, such as in medical diagnostics. The other metrics like Precision, RMSE, and Accuracy serve different aspects of performance measurement and are not specifically focused on maximizing the detection of positive cases alone.
Reference:
Classification Metrics in Machine Learning (Understanding Recall).

NEW QUESTION # 41
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?

A. spark_df.to_sql()
B. import pandas as pd
df = pd.DataFrame(spark_df)
C. import pyspark.pandas as ps
df = ps.DataFrame(spark_df)
D. import pyspark.pandas as ps
df = ps.to_pandas(spark_df)
E. spark_df.to_pandas()

Answer: C

Explanation:
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

NEW QUESTION # 42
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 made up of Spark DataFrames and additional metadata
C. pandas API on Spark DataFrames are more performant than Spark DataFrames
D. pandas API on Spark DataFrames are less mutable versions of Spark DataFrames
E. pandas API on Spark DataFrames are unrelated to Spark DataFrames

Answer: B

Explanation:
Pandas API on Spark (previously known as Koalas) provides a pandas-like API on top of Apache Spark. It allows users to perform pandas operations on large datasets using Spark's distributed compute capabilities. Internally, it uses Spark DataFrames and adds metadata that facilitates handling operations in a pandas-like manner, ensuring compatibility and leveraging Spark's performance and scalability.
Reference
pandas API on Spark documentation: https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html

NEW QUESTION # 43
A machine learning engineer would like to develop a linear regression model with Spark ML to predict the price of a hotel room. They are using the Spark DataFrame train_df to train the model.
The Spark DataFrame train_df has the following schema:

The machine learning engineer shares the following code block:

Which of the following changes does the machine learning engineer need to make to complete the task?

A. They need to convert the features column to be a vector
B. They need to call the transform method on train df
C. They need to split the features column out into one column for each feature
D. They do not need to make any changes
E. They need to utilize a Pipeline to fit the model

Answer: A

Explanation:
In Spark ML, the linear regression model expects the feature column to be a vector type. However, if the features column in the DataFrame train_df is not already in this format (such as being a column of type UDT or a non-vectorized type), the engineer needs to convert it to a vector column using a transformer like VectorAssembler. This is a critical step in preparing the data for modeling as Spark ML models require input features to be combined into a single vector column.
Reference
Spark MLlib documentation for LinearRegression: https://spark.apache.org/docs/latest/ml-classification-regression.html#linear-regression

NEW QUESTION # 44
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