Microsoft AI-102 Exam - Topic 3 Question 51 Discussion

Actual exam question for Microsoft's AI-102 exam

Question #: 51
Topic #: 3

You are developing a monitoring system that will analyze engine sensor data, such as rotation speed, angle, temperature, and pressure. The system must generate an alert in response to atypical values.

What should you include in the solution?

AApplication Insights in Azure Monitor

Bmetric alerts in Azure Monitor

CMultivariate Anomaly Detection

DUnivariate Anomaly Detection

Show Suggested Answer

Suggested Answer: A

by Chauncey at Oct 07, 2023, 11:19 PM

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Coletta

4 months ago

Application Insights? Not sure if that's the best fit here.

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Ena

4 months ago

Wait, can we really rely on Azure for this?

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Talia

4 months ago

Univariate detection? Seems too basic for this.

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Melvin

5 months ago

I think multivariate anomaly detection is the way to go.

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Maryrose

5 months ago

Definitely need metric alerts in Azure Monitor!

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Glenna

5 months ago

Application Insights sounds familiar, but I’m not convinced it’s the right choice for just monitoring engine sensors.

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Zoila

5 months ago

I practiced a similar question where we had to choose between univariate and multivariate detection. I guess it depends on how complex the data is?

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Heidy

5 months ago

I'm not entirely sure, but I remember something about Multivariate Anomaly Detection being useful for analyzing multiple sensor data points together.

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Van

5 months ago

I think we might need to use metric alerts in Azure Monitor since they can trigger notifications based on specific thresholds.

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Fidelia

5 months ago

I've used Application Insights before, and it's a powerful tool for monitoring and alerting. But in this case, I think the metric alerts in Azure Monitor are probably the most straightforward solution. I'll go with option B.

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Darrin

5 months ago

Okay, let's think this through step-by-step. We need to monitor the engine sensor data and generate alerts for atypical values. Option C, Multivariate Anomaly Detection, seems like it could be a good fit since it can analyze multiple data streams together.

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Ivette

5 months ago

Hmm, I'm not sure about this one. There are a few different options here, and I'm not entirely confident in my understanding of them. I'll need to review the details of each approach before deciding.

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Long

6 months ago

This question seems straightforward - we need to use Azure Monitor to set up alerts for the engine sensor data. I think option B, metric alerts, is the way to go.

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Rene

6 months ago

Okay, let me see. A small p-value means we're rejecting the null hypothesis, so the risk would be a Type 1 error, where we incorrectly reject the null when it's actually true. I think that's the right answer.

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Stephane

6 months ago

Hmm, the question is focused on driving cost-efficient leads, but the results show a lift in message association rather than leads. I think I'll go with option B and allocate more budget to the Lead Generation objective.

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Natalie

6 months ago

I think this is a pretty straightforward question. The key is using the client's IP address as the source IP for the Citrix ADC-to-server connections, so I'm going to go with USIP.

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Frederica

6 months ago

This question seems a bit tricky. I'll need to carefully read through the options and think about how each one could impact the risk profile's effectiveness.

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Rodrigo

6 months ago

Okay, I've got this! UCF must be referring to the University of Central Florida, so the terms they're looking for are likely administrative or policy-related. I'll select the options that sound the most relevant.

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Kris

10 months ago

If this was a car engine, I'd say Option B is the way to go. Gotta keep an eye on those metrics, like the engine's vital signs.

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Irma

9 months ago

User 4: Univariate Anomaly Detection could also be useful for detecting anomalies in individual sensor readings.

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Crista

9 months ago

User 3: I think we should also consider Multivariate Anomaly Detection to detect complex patterns in the data.

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Mica

9 months ago

User 2: Yeah, we need to set thresholds for those metrics to trigger alerts when something goes wrong.

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Cecily

10 months ago

User 1: I agree, Option B with metric alerts sounds like the best choice for monitoring engine sensor data.

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Nathalie

10 months ago

Option C all the way! Multivariate Anomaly Detection is like the Marvel team of anomaly detection - it can handle multiple superheroes (er, sensor data points) at once.

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Noemi

10 months ago

Hold up, why are we even considering Application Insights (Option A)? That's more for web apps, not industrial sensor monitoring.

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Nenita

9 months ago

C) Multivariate Anomaly Detection

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Elvera

10 months ago

B) metric alerts in Azure Monitor

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Chanel

10 months ago

A) Application Insights in Azure Monitor

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Darrin

11 months ago

Hmm, I'm not sure if Univariate Anomaly Detection (Option D) is the right fit here. Analyzing each sensor data point in isolation might miss the bigger picture.

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Veronique

10 months ago

C) Multivariate Anomaly Detection

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Destiny

10 months ago

B) metric alerts in Azure Monitor

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Emogene

11 months ago

A) Application Insights in Azure Monitor

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Sherita

11 months ago

I'd go with Option B. Metric alerts in Azure Monitor seem tailored for this use case, as they can trigger alerts based on predefined thresholds for the sensor data.

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Latrice

11 months ago

I agree, Option B seems like the most suitable solution for monitoring engine sensor data.

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Yvonne

11 months ago

Option B) metric alerts in Azure Monitor is a good choice for this. It can help trigger alerts based on predefined thresholds for sensor data.

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Sophia

11 months ago

Option C looks like the best choice for this monitoring system. Multivariate Anomaly Detection can analyze multiple sensor data points to identify atypical patterns.

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