Microsoft DP-420 Exam - Topic 2 Question 58 Discussion

Actual exam question for Microsoft's DP-420 exam

Question #: 58
Topic #: 2

You have a container in an Azure Cosmos DB Core (SQL) API account. The container stores telemetry data from IoT devices. The container uses telemetryId as the partition key and has a throughput of 1,000 request units per second (RU/s). Approximately 5,000 IoT devices submit data every five minutes by using the same telemetryId value.

You have an application that performs analytics on the data and frequently reads telemetry data for a single IoT device to perform trend analysis.

The following is a sample of a document in the container.

You need to reduce the amount of request units (RUs) consumed by the analytics application.

What should you do?

ADecrease the offerThroughput value for the container.

BIncrease the offerThroughput value for the container.

CMove the data to a new container that has a partition key of deviceId.

DMove the data to a new container that uses a partition key of date.

Show Suggested Answer

Suggested Answer: B

by Sueann at Feb 08, 2025, 12:23 PM

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Dahlia

3 months ago

Not sure about the date partition key, seems less relevant for telemetry data.

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Nu

3 months ago

Definitely agree with moving the data to a new container.

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Catalina

3 months ago

Surprised that decreasing throughput is even an option here!

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Heidy

4 months ago

I think increasing the throughput won't really solve the RU issue.

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Lavonda

4 months ago

Moving to a new container with deviceId as the partition key could help.

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Sabine

4 months ago

I’m leaning towards option C, but I’m a bit uncertain if it’s the best choice since we might still have a lot of data for each device.

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Sabine

4 months ago

I practiced a similar question where we had to optimize RUs, and I feel like increasing throughput could actually lead to higher costs instead of reducing RUs.

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Shelba

4 months ago

I think moving to a new container with deviceId as the partition key might help, but I need to double-check how that affects query performance.

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Vanda

5 months ago

I remember that using the right partition key can really impact performance, but I'm not sure if switching to deviceId or date is better for reducing RUs.

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Twana

5 months ago

I'm feeling pretty confident about this one. Increasing the offerThroughput value for the container seems like the most straightforward way to reduce the RU consumption.

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Gilberto

5 months ago

Okay, I think I've got a strategy here. Since the analytics app frequently reads data for a single IoT device, I'll try moving the data to a new container with deviceId as the partition key.

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Alease

5 months ago

Hmm, I'm a bit confused about the partition key and how that relates to the RU consumption. I'll need to review the Azure Cosmos DB concepts again.

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Chauncey

5 months ago

This looks like a tricky one. I'll need to carefully read through the details and think about the best way to optimize the RU consumption.

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Buck

10 months ago

Ah, the joys of Azure Cosmos DB! Where the partition keys are made up and the RUs don't matter. Just toss some more money at it, and all your problems will go away. Truly the cloud equivalent of turning it off and on again.

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Shad

10 months ago

B is the way to go! Increasing the throughput is the easiest solution, and it will just cost you more money. Why bother with all this partition key nonsense when you can just throw more RUs at the problem?

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Leota

8 months ago

B) Increasing the throughput is the easiest solution, and it will just cost you more money.

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Olen

8 months ago

A) Decrease the offerThroughput value for the container.

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Kris

9 months ago

B) Increase the offerThroughput value for the container.

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Jeff

10 months ago

Hmm, I'm leaning towards D. Partitioning by date sounds like a more natural fit for time-series data like this. Plus, it might make it easier to manage the data in the long run.

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Aracelis

8 months ago

Partitioning by date seems like a logical solution for trend analysis on time-series data. It could improve performance.

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Tatum

9 months ago

Moving the data to a new container with a partition key of date could definitely help reduce the RUs consumed.

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Huey

10 months ago

I think D is a good choice too. It can help optimize the queries for trend analysis based on date.

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Edison

10 months ago

I'm not sure about that. Wouldn't partitioning by date be better? That way, the application can easily access data for a specific time period, which could be useful for trend analysis.

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Helaine

8 months ago

True, that could make accessing data for a single IoT device more efficient.

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Leonor

8 months ago

Moving data to a new container with a partition key of deviceId might also be a good option.

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Jules

9 months ago

But wouldn't decreasing the offerThroughput value help reduce RUs consumed?

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Talia

10 months ago

Partitioning by date could be a good idea for trend analysis.

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Justa

11 months ago

That's an interesting idea. It could help reduce the RUs consumed by the analytics application.

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Shonda

11 months ago

I disagree, I believe we should move the data to a new container that has a partition key of deviceId.

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Ruth

11 months ago

Choosing C seems like the best option here. Partitioning the data by deviceId will allow the analytics application to read data for a single IoT device more efficiently, reducing the RUs consumed.

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