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Databricks Machine Learning Associate Exam - Topic 4 Question 18 Discussion

Which of the following describes the relationship between native Spark DataFrames and pandas API on Spark DataFrames?
C) pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata
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
D) pandas API on Spark DataFrames are less mutable versions of Spark DataFrames

Databricks Machine Learning Associate Exam - 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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Haydee
7 months ago
B sounds wrong, Spark DataFrames are usually more performant.
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Shawnna
8 months ago
Wait, are pandas API really just Spark DataFrames with extra stuff?
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Laurene
8 months ago
Totally agree with C, it makes sense!
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Joanna
8 months ago
I thought A was the right answer, seems off.
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Ming
8 months ago
C is correct, pandas API on Spark is built on Spark DataFrames.
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Verona
9 months ago
I thought pandas API on Spark was supposed to be more performant, but now I'm questioning if that's really the case compared to native Spark DataFrames.
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Harrison
9 months ago
I feel like the answer might be related to how pandas API on Spark builds on Spark DataFrames, but I can't remember the exact wording.
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Lindsay
9 months ago
I think I saw a practice question that mentioned the relationship involving additional metadata, but I can't recall if it was about performance or mutability.
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Leatha
9 months ago
I remember something about how pandas API on Spark is designed to work with Spark DataFrames, but I'm not sure if it's just a single-node version or something else.
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Kathrine
9 months ago
I'm pretty confident on this one. The pandas API on Spark DataFrames is made up of Spark DataFrames plus some additional metadata, but it's not a single-node version and isn't more performant. I think the answer is C.
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Alonzo
9 months ago
Okay, I've got a strategy for this. The key is to understand how the pandas API on Spark DataFrames relates to the native Spark DataFrames. I'll compare the capabilities and characteristics of each to figure out the right answer.
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Gilma
9 months ago
I think this question is testing our understanding of the relationship between Spark DataFrames and the pandas API on Spark DataFrames. I'll need to carefully review the differences between the two to determine the correct answer.
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Bethanie
9 months ago
Hmm, this one seems tricky. I know the pandas API on Spark DataFrames provides some additional functionality, but I'm not sure if it's a single-node version or just has additional metadata. I'll have to think this through.
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Nenita
9 months ago
I'm a bit confused here. Is equivalence class testing the right approach? We'd need to identify the different equivalence classes, like "Enough coins inserted" and "Not enough coins inserted".
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Justine
1 year 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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Nobuko
1 year ago
E) pandas API on Spark DataFrames are unrelated to Spark DataFrames
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Lonna
1 year ago
C) pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata
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Marshall
1 year ago
A) pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata
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Joseph
1 year 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 year 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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Deandrea
1 year ago
B) pandas API on Spark DataFrames are more performant than Spark DataFrames
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Karima
1 year ago
C) pandas API on Spark DataFrames are made up of Spark DataFrames and additional metadata
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Stephanie
1 year ago
A) pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata
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Veronika
1 year 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
1 year ago
User 3: Maybe pandas API on Spark DataFrames are just single-node versions with additional metadata.
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Adolph
1 year ago
User 2: I'm not so sure about that. Option B does seem a bit suspect.
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Yolande
1 year ago
User 1: I think pandas API on Spark DataFrames are more performant than Spark DataFrames.
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Tayna
1 year 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
1 year ago
Yeah, it's interesting how the two are connected through Spark DataFrames and additional metadata.
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Kenda
1 year 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 year 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 year ago
I think option A is correct, they are single-node versions of Spark DataFrames with additional metadata.
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Skye
1 year 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 year ago
I think option A is more accurate. It's like a single-node version of Spark DataFrames.
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Ilene
1 year ago
I agree, option C makes sense. It adds extra functionality to Spark DataFrames.
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Kyoko
1 year ago
Hmm, that makes sense too. I can see how both answers could be valid.
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Charolette
1 year 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
1 year ago
I think the answer is A) pandas API on Spark DataFrames are single-node versions of Spark DataFrames with additional metadata.
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