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Microsoft DP-600 Exam - 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:

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Aleisha
4 months ago
Totally agree, B minimizes shuffling effectively!
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Curtis
4 months ago
Is it just me, or does Option A seem too simple?
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Adria
4 months ago
Wait, why would anyone choose Option D? Seems off.
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Fredric
4 months ago
I think Option B is the best choice here.
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Herman
4 months ago
Looks like we need to join on customer_id for sure!
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Arleen
5 months ago
I feel like Option B could be the answer since it looks like it specifies the join type clearly, but I’m not entirely confident.
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Kyoko
5 months ago
I’m a bit confused about the syntax in these options. I remember something about using 'merge' but can't recall the exact parameters.
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Dorian
5 months ago
I think we practiced a similar question where we had to join DataFrames on a key. I feel like Option C might be the right choice.
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Gracia
5 months ago
I remember we discussed minimizing data shuffling in class, but I’m not sure which join method to use here.
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Ezekiel
5 months ago
I'm pretty confident that Option B is the right answer here. Broadcast joins are generally more efficient than regular joins, especially when one of the DataFrames is small enough to fit in memory on each partition.
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Lennie
5 months ago
Hmm, I'm a bit confused by the different join methods presented. I'll need to double-check the documentation on how each one works to determine the best approach for minimizing data shuffling.
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Iola
5 months ago
This looks like a tricky question on DataFrame joins in PySpark. I'll need to carefully review the code options and think through the data shuffling requirements.
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Art
5 months ago
Okay, I think I've got this. Based on the requirement to minimize data shuffling, I'm leaning towards Option B since it uses a broadcast join, which should be more efficient.
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Chaya
5 months ago
Hmm, I'm a bit unsure about this one. The options seem similar, but I'll try to think through the differences between them and see if I can figure out the right answer.
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Jade
6 months ago
I think one of the main benefits of virtual machines is that you can run multiple instances on the same hardware, which really saves costs.
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Amira
6 months ago
I remember learning about multicast in class, but I'm a bit fuzzy on the details. I'll try to eliminate the options that I'm more certain about.
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Latrice
10 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
9 months ago
Definitely, Spark's 'broadcast' feature is a game-changer.
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Janey
10 months ago
I agree, Option B is like finding Waldo in your data.
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Alberto
10 months ago
I think Option B is the way to go. It minimizes data shuffling.
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Phillip
11 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
10 months ago
User3
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Chana
10 months ago
User2
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Gerald
10 months ago
User1
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Galen
11 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
10 months ago
Let's go with Option C then, it seems like the safest choice.
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Cristy
10 months ago
I agree, Option C seems like a good option.
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Yuki
10 months ago
I'm not so sure about that, Option C looks promising too.
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Jaclyn
10 months ago
I think Option D might be the best choice.
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Derrick
11 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
11 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
10 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
11 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
11 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
11 months ago
I agree with Lamar, Option B looks like the best choice for minimizing data shuffling.
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Lamar
12 months ago
I think we should run Option B.
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