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Databricks Certified Machine Learning Associate Exam Sample Questions (Q13-Q18):

NEW QUESTION # 13
Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?

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

Answer: A

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 # 14
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?

A. Fewer hyperparameter values need to be tested when using a train-validation split
B. Fewer models need to be trained when using a train-validation split
C. A holdout set is not necessary when using a train-validation split
D. Reproducibility is achievable when using a train-validation split
E. Bias is avoidable when using a train-validation split

Answer: B

NEW QUESTION # 15
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. Standard cluster
B. Single Node cluster
C. SQL Warehouse
D. None of these compute tools support this task

Answer: A

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 # 16
Which of the following machine learning algorithms typically uses bagging?

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

Answer: B

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 # 17
Which of the following machine learning algorithms typically uses bagging?

A. Linear regression
B. Random forest
C. Gradient boosted trees
D. Decision tree
E. K-means

Answer: B

Explanation:
Random Forest is a machine learning algorithm that typically uses bagging (Bootstrap Aggregating). Bagging involves training multiple models independently on different random subsets of the data and then combining their predictions. Random Forests consist of many decision trees trained on random subsets of the training data and features, and their predictions are averaged to improve accuracy and control overfitting. This method enhances model robustness and predictive performance.
Reference:
Ensemble Methods in Machine Learning (Understanding Bagging and Random Forests).

NEW QUESTION # 18
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