Databricks Exam Databricks Machine Learning Associate Topic 2 Question 11 Discussion

Actual exam question for Databricks's Databricks Machine Learning Associate exam

Question #: 11
Topic #: 2

[All Databricks Machine Learning Associate Questions]

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 price is greater than 0.

Which of the following code blocks will accomplish this task?

Aspark_df[spark_df['price'] > 0]

Bspark_df.filter(col('price') > 0)

CSELECT * FROM spark_df WHERE price > 0

Dspark_df.loc[spark_df['price'] > 0,:]

Espark_df.loc[:,spark_df['price'] > 0]

Show Suggested Answer

Suggested Answer: C

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.

Databricks documentation on Random Forest: Random Forest in Spark ML

by Tambra at Jul 17, 2024, 12:09 PM

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Pearlene

2 days ago

Definitely not A, that's for pandas, not Spark.

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Ashanti

8 days ago

I think option B is the right one!

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Marcos

14 days ago

Option C seems like SQL syntax, which we covered, but I don't think it applies directly to Spark DataFrames.

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Frank

19 days ago

I recall a similar question where we had to filter DataFrames, and I think the correct approach was using the filter method like in option B.

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Mose

24 days ago

I'm not entirely sure, but I feel like option A might not work since it looks more like pandas syntax.

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Mariann

1 month ago

I think option B looks familiar; I remember using the filter method in Spark during practice.

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Ivan

1 month ago

I think I'd go with option B as well. The `filter()` function looks like the most straightforward way to apply a condition to the DataFrame in Spark.

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Casie

1 month ago

I'm a bit torn between B and C. The SQL-style syntax in C is appealing, but I know we're supposed to be using the Spark DataFrame API, so I'll probably go with option B to be safe.

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Aleta

1 month ago

Hmm, I'm not too familiar with Spark syntax, so I'm a bit unsure about the best approach here. I might try option A or D, since they look more similar to the pandas DataFrame syntax I'm used to.

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Lea

1 month ago

This looks like a straightforward filtering task. I'd probably go with option B, using the `filter()` function on the Spark DataFrame.

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Cristal

1 month ago

Option B seems like the most Spark-specific and efficient way to handle this. I'd focus on understanding the `filter()` function and how to use column names in Spark.

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Daniel

1 month ago

I'm pretty confident about this one - the mission or vision statement is the foundation for building the job worth hierarchy, so the answer has to be True.

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