Microsoft DP-600 Exam - Topic 3 Question 15 Discussion

Actual exam question for Microsoft's DP-600 exam

Question #: 15
Topic #: 3

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?

AOption A

BOption B

COption C

DOption D

Show Suggested 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.

by Rebecka at Jun 23, 2024, 07:52 PM

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Aleisha

10 months ago

Totally agree, B minimizes shuffling effectively!

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Curtis

10 months ago

Is it just me, or does Option A seem too simple?

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Adria

10 months ago

Wait, why would anyone choose Option D? Seems off.

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Fredric

10 months ago

I think Option B is the best choice here.

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Herman

10 months ago

Looks like we need to join on customer_id for sure!

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Arleen

11 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

11 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

11 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

11 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

11 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

11 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

11 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

11 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

11 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

12 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

12 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

1 year 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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