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Microsoft Exam DP-600 Topic 3 Question 15 Discussion

Actual exam question for Microsoft's DP-600 exam
Question #: 15
Topic #: 3
[All DP-600 Questions]

You are analyzing customer purchases in a Fabric notebook by using PySpanc You have the following DataFrames:

You need to join the DataFrames on the customer_id column. The solution must minimize data shuffling. You write the following code.

Which code should you run to populate the results DataFrame?

A)

B)

C)

D)

Show Suggested Answer Hide Answer
Suggested Answer: B

Tabular Editor is an advanced tool for editing Tabular models outside of Power BI Desktop that allows you to script out changes and apply them across multiple columns or tables. To accomplish the task programmatically, you would:

Open the model in Tabular Editor.

Create an Advanced Script using C# to iterate over all tables and their respective columns.

Within the script, check if the column name ends with 'Key'.

For columns that meet the condition, set the properties accordingly: IsHidden = true, IsNullable = false, SummarizeBy = None, IsAvailableInMDX = false.

Additionally, mark the column as a key column.

Save the changes and deploy them back to the Fabric tenant.


Contribute your Thoughts:

Latrice
2 months ago
I'm going with Option B, because why not? It's like a game of 'Where's Waldo?' for your data, and Spark's 'broadcast' feature is like the winning lottery ticket.
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Mirta
2 days ago
Definitely, Spark's 'broadcast' feature is a game-changer.
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Janey
1 months ago
I agree, Option B is like finding Waldo in your data.
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Alberto
1 months ago
I think Option B is the way to go. It minimizes data shuffling.
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Phillip
2 months ago
Option A all the way, baby! Spark's 'join()' method is the way to go. It's like a dance party for your data, and you're the DJ!
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Brynn
24 days ago
User3
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Chana
1 months ago
User2
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Gerald
1 months ago
User1
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Galen
2 months ago
Hmm, this is a tough one. Maybe Option D is the way to go? I mean, who doesn't love a good ol' cross join? It's like a surprise party for your data!
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Alpha
29 days ago
Let's go with Option C then, it seems like the safest choice.
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Cristy
1 months ago
I agree, Option C seems like a good option.
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Yuki
1 months ago
I'm not so sure about that, Option C looks promising too.
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Jaclyn
1 months ago
I think Option D might be the best choice.
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Derrick
2 months ago
I'm not sure, this seems tricky. But I'll go with Option C just to be safe. Can't go wrong with a good old pandas merge, right?
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Jodi
2 months ago
Option B looks like the winner to me. Spark's join() method with 'broadcast' seems like the way to go for minimizing data shuffling.
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Louisa
1 months ago
Yes, Option B is the most efficient. 'broadcast' with Spark's join() method is the way to go for optimizing performance.
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Theodora
2 months ago
I agree, Option B is the best choice. Using 'broadcast' with Spark's join() method will definitely help minimize data shuffling.
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Felicidad
2 months ago
I'm not sure, but I think Option C could also work well. It's a tough decision.
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Anastacia
2 months ago
I agree with Lamar, Option B looks like the best choice for minimizing data shuffling.
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Lamar
3 months ago
I think we should run Option B.
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