Microsoft DP-700 Exam - Topic 2 Question 7 Discussion

Actual exam question for Microsoft's DP-700 exam

Question #: 7
Topic #: 2

[All DP-700 Questions]

You have an Azure event hub. Each event contains the following fields:

BikepointID

Street

Neighbourhood

Latitude

Longitude

No_Bikes

No_Empty_Docks

You need to ingest the events. The solution must only retain events that have a Neighbourhood value of Chelsea, and then store the retained events in a Fabric lakehouse.

What should you use?

Aa KQL queryset

Ban eventstream

Ca streaming dataset

DApache Spark Structured Streaming

Show Suggested Answer

Suggested Answer: D

To compute the standard deviation of the temperature from the thermal sensor data, you would use the Aggregate transform operator in Eventstream1. The Aggregate operator allows you to apply functions like sum, average, count, and statistical functions like standard deviation across a group of rows or events. This operator is ideal for operations that require summarizing or computing statistics over a dataset, such as calculating the standard deviation.

by Monte at Jan 23, 2025, 02:18 PM

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Helene

3 months ago

No way, D is definitely the way to go!

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Aileen

3 months ago

I’m surprised they want to filter by Neighbourhood like that.

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Shalon

3 months ago

Wait, why not just use a streaming dataset?

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Kenda

4 months ago

A KQL queryset would work too, right?

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Annice

4 months ago

I think D is the best choice for this.

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Isadora

4 months ago

I feel like the eventstream option might not be enough for the filtering requirement. Spark seems more appropriate for this scenario.

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Muriel

4 months ago

I practiced a similar question where we had to filter events, and I think a streaming dataset could be the right choice, but I’m not confident.

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Sage

5 months ago

I'm not entirely sure, but I remember something about KQL being useful for querying data. Would that work here?

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Lindsey

5 months ago

I think we might need to use Apache Spark Structured Streaming for this, since it can handle real-time data and filtering.

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Lai

5 months ago

This seems pretty straightforward to me. I'd just use a KQL queryset to filter the events with a Neighbourhood value of Chelsea, and then store the results in the Fabric lakehouse. KQL is a powerful query language for working with Azure data sources, so I think that would be the most efficient approach here.

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Blondell

5 months ago

Okay, I've got a strategy for this. I'd use Apache Spark Structured Streaming to ingest the events from the Azure event hub. That way, I can apply the Neighbourhood filter in real-time as the data is coming in, and then write the filtered events directly to the Fabric lakehouse. Spark Streaming should give me the performance and scalability I need for this kind of streaming data processing.

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Jannette

5 months ago

Hmm, I'm a bit unsure about this one. I'm not super familiar with Azure event hubs and Fabric lakehouses. I think I'd need to do some more research on the specific technologies involved before deciding on the best approach. Maybe I'll try to find some examples online to get a better understanding.

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Cecil

5 months ago

This looks like a classic data ingestion and filtering problem. I'd start by using a streaming dataset to ingest the events from the Azure event hub, and then apply a filter to only retain the events with a Neighbourhood value of Chelsea before storing them in the Fabric lakehouse.

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Glenn

10 months ago

Haha, I bet the answer is just to use a KQL queryset and then manually filter out the events. That's probably the most 'Azure' solution, right?

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Beata

8 months ago

User 4: Just filter out the events with Neighbourhood value of Chelsea.

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Robt

9 months ago

User 3: It's definitely the most Azure way to do it.

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Hildred

9 months ago

User 2: Yeah, that sounds like the right choice.

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Kristofer

9 months ago

User 1: I think the answer is to use a KQL queryset.

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Rikki

9 months ago

Hayley: Yeah, it's a more efficient way to handle the data ingestion process.

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Hayley

10 months ago

User 2: That's correct. Using KQL will make it easier to retain only the events you need for the Fabric lakehouse.

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Kiley

10 months ago

User 1: Actually, the best option is to use a KQL queryset to filter out the events with Neighbourhood value of Chelsea.

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Lillian

10 months ago

I'm leaning towards option C, a streaming dataset. That seems like it would be a good fit for this use case, and it might be a bit simpler to set up than the Spark solution.

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Dick

11 months ago

Hmm, I'm not sure about that. Wouldn't an eventstream be a more straightforward solution? It's designed for ingesting and processing streaming data like this.

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Rhea

10 months ago

I agree, using a KQL queryset would allow us to easily retain events with a Neighbourhood value of Chelsea before storing them in a Fabric lakehouse.

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Theola

10 months ago

I think a KQL queryset would be more appropriate for filtering events based on specific criteria like Neighbourhood.

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Gracia

11 months ago

I think option D, Apache Spark Structured Streaming, is the way to go. It can handle the streaming data and filter out the events based on the Neighbourhood value.

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Dorthy

10 months ago

I'm not sure about the others, but Apache Spark Structured Streaming sounds like the best choice for this task.

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Jamal

10 months ago

I would go with Apache Spark Structured Streaming as well, it seems like the most suitable option for this scenario.

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Rozella

10 months ago

I think using a KQL queryset might also work well for filtering out events based on the Neighbourhood value.

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Amie

10 months ago

I agree, Apache Spark Structured Streaming is a powerful tool for handling streaming data.

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Celestina

11 months ago

I'm not sure, but I think Apache Spark Structured Streaming could also work for this scenario.

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Nobuko

11 months ago

I agree with Leanora, KQL would be the best option to filter events by Neighbourhood.

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Leanora

11 months ago

I think we should use a KQL queryset for this.

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