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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	Databricks Machine Learning: It covers sub-topics of AutoML, Databricks Runtime, Feature Store, and MLflow.
Topic 3	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 4	Scaling ML Models: This topic covers Model Distribution and Ensembling Distribution.

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Databricks Certified Machine Learning Associate Exam Sample Questions (Q40-Q45):

NEW QUESTION # 40
Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?

A. pandas API on Spark DataFrames are less mutable versions of Spark DataFrames
B. pandas API on Spark DataFrames are more performant than Spark DataFrames
C. pandas API on Spark DataFrames are unrelated to Spark DataFrames
D. pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata
E. pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata

Answer: E

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 # 41
Which of the following machine learning algorithms typically uses bagging?

A. IGradient boosted trees
B. Decision tree
C. K-means
D. Random forest

Answer: D

Explanation:
Random Forest is a machine learning algorithm that typically uses bagging (Bootstrap Aggregating). Bagging is a technique that involves training multiple base models (such as decision trees) on different subsets of the data and then combining their predictions to improve overall model performance. Each subset is created by randomly sampling with replacement from the original dataset. The Random Forest algorithm builds multiple decision trees and merges them to get a more accurate and stable prediction.
Reference:
Databricks documentation on Random Forest: Random Forest in Spark ML

NEW QUESTION # 42
Which of the following tools can be used to distribute large-scale feature engineering without the use of a UDF or pandas Function API for machine learning pipelines?

A. Spark ML
B. PvTorch
C. Keras
D. Scikit-learn
E. pandas

Answer: A

Explanation:
Spark ML (Machine Learning Library) is designed specifically for handling large-scale data processing and machine learning tasks directly within Apache Spark. It provides tools and APIs for large-scale feature engineering without the need to rely on user-defined functions (UDFs) or pandas Function API, allowing for more scalable and efficient data transformations directly distributed across a Spark cluster. Unlike Keras, pandas, PyTorch, and scikit-learn, Spark ML operates natively in a distributed environment suitable for big data scenarios.
Reference:
Spark MLlib documentation (Feature Engineering with Spark ML).

NEW QUESTION # 43
A data scientist is attempting to tune a logistic regression model logistic using scikit-learn. They want to specify a search space for two hyperparameters and let the tuning process randomly select values for each evaluation.
They attempt to run the following code block, but it does not accomplish the desired task:

Which of the following changes can the data scientist make to accomplish the task?

A. Replace the GridSearchCV operation with cross_validate
B. Replace the penalty= ['12', '11'] argument with penalty=uniform ('12', '11')
C. Replace the random_state=0 argument with random_state=1
D. Replace the GridSearchCV operation with ParameterGrid
E. Replace the GridSearchCV operation with RandomizedSearchCV

Answer: E

Explanation:
The user wants to specify a search space for hyperparameters and let the tuning process randomly select values. GridSearchCV systematically tries every combination of the provided hyperparameter values, which can be computationally expensive and time-consuming. RandomizedSearchCV, on the other hand, samples hyperparameters from a distribution for a fixed number of iterations. This approach is usually faster and still can find very good parameters, especially when the search space is large or includes distributions.
Reference
Scikit-Learn documentation on hyperparameter tuning: https://scikit-learn.org/stable/modules/grid_search.html#randomized-parameter-optimization

NEW QUESTION # 44
A data scientist is using Spark SQL to import their data into a machine learning pipeline. Once the data is imported, the data scientist performs machine learning tasks using Spark ML.
Which of the following compute tools is best suited for this use case?

A. SQL Warehouse
B. Standard cluster
C. Single Node cluster
D. None of these compute tools support this task

Answer: B

Explanation:
For a data scientist using Spark SQL to import data and then performing machine learning tasks using Spark ML, the best-suited compute tool is a Standard cluster. A Standard cluster in Databricks provides the necessary resources and scalability to handle large datasets and perform distributed computing tasks efficiently, making it ideal for running Spark SQL and Spark ML operations.
Reference:
Databricks documentation on clusters: Clusters in Databricks

NEW QUESTION # 45
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