Databricks Machine Learning Associate Exam - 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, 11:09 AM

Limited Time Offer

25%

Off

Get Premium Databricks Machine Learning Associate Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

Ailene

3 months ago

I thought you could use loc in Spark, but maybe I'm wrong?

upvoted 0 times

...

Nu

3 months ago

Wait, is D even a valid Spark command?

upvoted 0 times

...

Anthony

3 months ago

C looks like SQL, but it's not valid for DataFrames.

upvoted 0 times

...

Pearlene

4 months ago

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

upvoted 0 times

...

Ashanti

4 months ago

I think option B is the right one!

upvoted 0 times

...

Marcos

4 months ago

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

upvoted 0 times

...

Frank

4 months 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.

upvoted 0 times

...

Mose

4 months ago

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

upvoted 0 times

...

Mariann

5 months ago

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

upvoted 0 times

...

Ivan

5 months 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.

upvoted 0 times

...

Casie

5 months 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.

upvoted 0 times

...

Aleta

5 months 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.

upvoted 0 times

...

Lea

5 months ago

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

upvoted 0 times

...

Cristal

5 months 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.

upvoted 0 times

...

Daniel

5 months 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.

upvoted 0 times

...