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Databricks-Machine-Learning-Associate Practice Questions

Question # 1
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 more performant than Spark DataFrames
C. pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata
D. pandas API on Spark DataFrames are less mutable versions of Spark DataFrames


C. pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata

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.

References

pandas API on Spark documentation:https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html

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?
A. spark_df.describe()
B. dbutils.data(spark_df).summarize()
C. This task cannot be accomplished in a single line of code.
D. spark_df.summary()
E. dbutils.data.summarize (spark_df)


E. dbutils.data.summarize (spark_df)

Explanation:

To display visual histograms and summaries of the numeric features in a Spark DataFrame, the Databricks utility functiondbutils.data.summarizecan be used. This function provides a comprehensive summary, including visual histograms.

Correct code:

dbutils.data.summarize(spark_df)

Other options likespark_df.describe()andspark_df.summary()provide textual statistical summaries but do not include visual histograms.

References:

Databricks Utilities Documentation

Question # 3
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. Single Node cluster
B. Standard cluster
C. SQL Warehouse
D. None of these compute tools support this task


B. Standard cluster

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.

References:

Databricks documentation on clusters: Clusters in Databricks

Question # 4
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. Keras
B. pandas
C. PvTorch
D. Spark ML
E. Scikit-learn


D. Spark ML

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.

References:

Spark MLlib documentation (Feature Engineering with Spark ML).


Question # 5
Which of the following machine learning algorithms typically uses bagging?
A. IGradient boosted trees
B. K-means
C. Random forest
D. Decision tree


C. Random forest

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.

References:

Databricks documentation on Random Forest: Random Forest in Spark ML


Question # 6
Which of the following tools can be used to parallelize the hyperparameter tuning process for single-node machine learning models using a Spark cluster?
A. MLflow Experiment Tracking
B. Spark ML
C. Autoscaling clusters
D. Hyperopt
E. Delta Lake


D. Hyperopt


Question # 7
Which of the Spark operations can be used to randomly split a Spark DataFrame into a training DataFrame and a test DataFrame for downstream use?
A. TrainValidationSplit
B. DataFrame.where
C. CrossValidator
D. TrainValidationSplitModel
E. DataFrame.randomSplit


E. DataFrame.randomSplit


Question # 8
Which of the following statements describes a Spark ML estimator?
A. An estimator is a hyperparameter grid that can be used to train a model
B. An estimator chains multiple algorithms together to specify an ML workflow
C. An estimator is a trained ML model which turns a DataFrame with features into a DataFrame with predictions
D. An estimator is an algorithm which can be fit on a DataFrame to produce a Transformer
E. An estimator is an evaluation tool to assess to the quality of a model


D. An estimator is an algorithm which can be fit on a DataFrame to produce a Transformer


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