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Databricks Exam Databricks Machine Learning Associate Topic 4 Question 18 Discussion

Actual exam question for Databricks's Databricks Machine Learning Associate exam
Question #: 18
Topic #: 4
[All Databricks Machine Learning Associate Questions]

Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?

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Suggested Answer: C

To filter rows in a Spark DataFrame based on a condition, the filter method is used. In this case, the condition is that the value in the 'discount' column should be less than or equal to 0. The correct syntax uses the filter method along with the col function from pyspark.sql.functions.

Correct code:

from pyspark.sql.functions import col filtered_df = spark_df.filter(col('discount') <= 0)

Option A and D use Pandas syntax, which is not applicable in PySpark. Option B is closer but misses the use of the col function.


PySpark SQL Documentation

by Lea at Sep 16, 2024, 07:34 PM

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Justine
1 months ago
I heard the pandas API on Spark DataFrames is so advanced, it can even write your code for you. Just sit back, relax, and let the metadata do the work!
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Marshall
16 days ago
A) pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata
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Joseph
1 months ago
Ah, the age-old battle of Spark vs. pandas. It's like the Godzilla vs. King Kong of the data science world. May the most mutant DataFrame win!
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Carylon
1 months ago
Wait, are there really people out there who think the pandas API is unrelated to Spark DataFrames? That's like saying apples are unrelated to fruit. Option E is just plain wrong.
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Stephanie
18 days ago
A) pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata
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Veronika
2 months ago
Hold up, are we sure the pandas API is more performant than Spark DataFrames? I thought Spark was all about the big data crunching. Option B seems a bit suspect to me.
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Corrina
14 days ago
User 3: Maybe pandas API on Spark DataFrames are just single-node versions with additional metadata.
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Adolph
27 days ago
User 2: I'm not so sure about that. Option B does seem a bit suspect.
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Yolande
1 months ago
User 1: I think pandas API on Spark DataFrames are more performant than Spark DataFrames.
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Tayna
2 months ago
Hmm, I was leaning towards option A, but I can see how option C makes more sense. Gotta love those extra metadata layers!
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Daren
4 days ago
Yeah, it's interesting how the two are connected through Spark DataFrames and additional metadata.
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Kenda
17 days ago
True, the extra metadata layers definitely add value to the relationship between native Spark DataFrames and pandas API on Spark DataFrames.
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Launa
1 months ago
I agree, but option C also makes sense as pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata.
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Talia
1 months ago
I think option A is correct, they are single-node versions of Spark DataFrames with additional metadata.
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Skye
2 months ago
I think option C is the correct answer. The pandas API on Spark DataFrames is built on top of Spark DataFrames and adds additional metadata to them.
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Ilene
1 months ago
I think option A is more accurate. It's like a single-node version of Spark DataFrames.
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Ilene
2 months ago
I agree, option C makes sense. It adds extra functionality to Spark DataFrames.
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Kyoko
2 months ago
Hmm, that makes sense too. I can see how both answers could be valid.
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Charolette
2 months ago
I disagree, I believe the answer is C) pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata.
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Kyoko
2 months ago
I think the answer is A) pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata.
upvoted 0 times
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