Master AI-102 Azure with these 5 Practice Questions

Master AI-102 Azure with these 5 Practice Questions

Table of Contents

  1. Introduction
  2. Microsoft EI102 Exam Questions
  3. Question 16: Create a Web App on Azure Virtual Machine
  4. Question 17: Predictive Maintenance and Anomaly Detection
  5. Question 18: Streaming Speech-to-Text
  6. Question 19: Detecting Learner Presence in Internet-Based Training
  7. Question 20: Provisioning Q&A Maker Service
  8. Conclusion

Introduction

This article will cover a series of Microsoft EI102 exam questions and provide explanations and solutions for each question. The questions are available on the examtopics.com Website. We will dive into the details and provide step-by-step answers to help You prepare for the exam effectively.

2. Microsoft EI102 Exam Questions

In this section, we will discuss the Microsoft EI102 exam questions. These questions are designed to test your knowledge and understanding of various topics related to Microsoft technologies.

3. Question 16: Create a Web App on Azure Virtual Machine

In question 16, We Are asked to create a web app named app one that runs on an Azure virtual machine named vm1. The virtual machine is located on the virtual network v network. Our goal is to ensure that app one can directly connect to the Azure Cognitive Search service named service one without routing traffic over the public internet. We are provided with a solution that involves deploying service one with a public endpoint and configuring a network security group. We will analyze whether this solution meets the goal and provide our answer.

4. Question 17: Predictive Maintenance and Anomaly Detection

In question 17, the Scenario involves performing predictive maintenance by collecting IoT sensor data from industrial machines. Each machine has 15 different sensors, and data is collected at one minute intervals. The goal is to identify unusual values in each time series data set. We will discuss the concept of time series data and how to identify unusual values using the Anomaly Detector service. We will also determine which cognitive service is best suited for this task.

5. Question 18: Streaming Speech-to-Text

Question 18 focuses on developing a streaming speech-to-text solution. The requirement is to convert streaming MP3 data into text. We will examine the provided code and complete it by selecting the appropriate stream format and speech recognition method. We will discuss the importance of streaming data and the role of speech recognition in this scenario.

6. Question 19: Detecting Learner Presence in Internet-Based Training

Question 19 deals with developing an internet-based training solution for remote learners. The challenge is to detect whether learners are present and paying Attention during the training. We will explore different cognitive services that can be used to achieve this goal, such as face recognition and speech analysis. We will determine the most suitable service for verifying learner presence and attention.

7. Question 20: Provisioning Q&A Maker Service

Question 20 involves the provisioning of a Q&A Maker service. We are tasked with creating an app service plan and identifying the automatically created Azure resources associated with the Q&A Maker service. We will discuss the concept of Q&A Maker, the role of app service plans, and the related Azure Cognitive Search service. We will analyze the options provided and select the correct answer based on the given scenario.

Conclusion

In conclusion, this article has covered various Microsoft EI102 exam questions and provided detailed explanations and solutions for each question. By understanding these concepts and scenarios, you will be well-prepared for the exam and can confidently tackle similar questions. Keep practicing and exploring Microsoft technologies to enhance your knowledge and skills.


Question 16: Create a Web App on Azure Virtual Machine

In question 16, we are asked to create a web app named app one that runs on an Azure virtual machine named vm1. The virtual machine is located on the virtual network v network. Our goal is to ensure that app one can directly connect to the Azure Cognitive Search service named service one without routing traffic over the public internet. The provided solution suggests deploying service one with a public endpoint and configuring a network security group. However, we need to evaluate whether this solution meets the goal effectively.

After analyzing the scenario and considering the requirements, it is evident that the solution proposed in the question is not sufficient to achieve the desired outcome. While deploying service one with a public endpoint can establish connectivity, it fails to address the requirement of avoiding traffic routing over the public internet.

To meet the goal of enabling app one to connect directly to service one without utilizing the public internet, the appropriate solution would be to deploy service one using Azure Private Link. By leveraging Azure Private Link, we can create a private endpoint for service one, which will allow app one on vm1 to establish a secure and private connection without traversing the public internet. This approach ensures data privacy and reduces potential security risks.

Therefore, the provided solution in the question does not meet the goal, and the correct solution involves deploying service one with Azure Private Link.

Pros:

  • Enhanced data privacy and security.
  • Avoidance of traffic routing over the public internet, reducing potential security risks.

Cons:

  • Additional configuration and setup required for Azure Private Link.

Question 17: Predictive Maintenance and Anomaly Detection

In question 17, the scenario involves performing predictive maintenance by collecting IoT sensor data from industrial machines. Each machine has 15 different sensors, and data is collected at one-minute intervals. The goal is to identify unusual values in each time series data set.

To identify unusual values in time series data, we can leverage the Azure Cognitive Services Anomaly Detector. The Anomaly Detector service utilizes machine learning algorithms to detect anomalies or unusual Patterns in time series data.

Time series data can be visualized as a graph, where the x-axis represents time, and the y-axis represents the measured metric. Anomalies can be identified as peaks or sudden deviations from the consistent pattern of the data.

By using the Anomaly Detector service, we can analyze the collected time series data sets and identify any unusual values. The service will detect abnormal patterns, peaks, or sudden changes in the data, alerting us to potential issues or anomalies in the industrial machines. This information can be used for proactive maintenance and Timely interventions to avoid critical failures.

Therefore, the correct cognitive service to use for identifying unusual values in time series data is the Anomaly Detector service.

Pros:

  • Efficient detection of anomalies in time series data.
  • Proactive maintenance based on anomaly detection.
  • Timely intervention to avoid critical failures.

Cons:

  • Requires proper configuration and fine-tuning of the Anomaly Detector service for accurate detection of anomalies.

Question 18: Streaming Speech-to-Text

In question 18, a streaming speech-to-text solution is being developed. The goal is to convert streaming MP3 data into text. The provided code needs to be completed to achieve this task effectively.

To convert streaming speech to text, we can utilize the Speech SDK provided by Azure. The Speech SDK offers various functionalities for speech recognition, including streaming capabilities.

Analyzing the code snippet, we can observe that we need to select the appropriate stream format and speech recognizer method. As the input is in the form of MP3 data, we must utilize the compressed format for the audio stream.

The correct code completion for converting streaming speech can be achieved by using the following lines of code:

AudioStreamFormat streamFormat = AudioStreamFormat.GetCompressedFormat(AudioStreamContainer.MP3);
PullAudioInputStream audioInputStream = new PullAudioInputStream(streamFormat, myMp3Input);
SpeechRecognizer recognizer = new SpeechRecognizer(speechConfig, audioInputStream);

Here, we are defining the stream format as the compressed format of MP3 using the AudioStreamFormat.GetCompressedFormat method. We then create a PullAudioInputStream object, passing the stream format and the MP3 input. Finally, we initialize the SpeechRecognizer object, providing the speech configuration and the audio input stream.

By completing the code in this manner, we ensure that the streaming MP3 data is correctly converted to text using the appropriate stream format and speech recognition functionality.

Pros:

  • Real-time conversion of streaming speech to text.
  • Accurate recognition of speech even in noisy environments.
  • Suitable for applications such as transcription services, virtual assistants, and more.

Cons:

  • Stream format compatibility and configuration can require additional troubleshooting and testing.

Question 19: Detecting Learner Presence in Internet-Based Training

In question 19, the scenario involves developing an internet-based training solution for remote learners. However, it has been observed that some learners leave their desks for long periods of time during the training Sessions. To ensure learner engagement and active participation, the task is to determine the cognitive service that can be used to detect learner presence and attention.

To detect learner presence and attention in internet-based training, we can leverage the Azure Cognitive Services Face and Speech APIs. These APIs offer various functionalities for facial recognition and speech analysis, which can be utilized to verify learner engagement and attentiveness.

The Face API can be used to detect the presence of a learner in front of the camera. By analyzing the learner's facial expressions and identifying a face in the video feed, we can determine if the learner is physically present during the training session.

The Speech API can be employed to analyze the learner's audio input. By detecting speech patterns and activity, we can determine if the learner is actively participating and engaged in the training session.

Therefore, to detect learner presence and attention in internet-based training, we should use the Face API to verify the presence of a learner in front of the camera and the Speech API to analyze the learner's audio input.

Pros:

  • Active monitoring of learner presence and attentiveness.
  • Real-time feedback on learner engagement.
  • Potential for automated interventions based on analysis results.

Cons:

  • Requires proper camera and audio setup for accurate detection.
  • Privacy concerns related to facial recognition and audio analysis must be addressed.

Question 20: Provisioning Q&A Maker Service

In question 20, the task is to provision a Q&A Maker service in the resource group rg1. An app service plan named ap1 has already been created in the same resource group. The question further asks which additional Azure resources will be automatically created when provisioning the Q&A Maker service.

When provisioning the Q&A Maker service, there are two additional Azure resources that are automatically created.

  1. Azure Cognitive Search: Q&A Maker utilizes Azure Cognitive Search to power the search capabilities for the pre-defined set of questions and answers stored in the database. Consequently, when provisioning the Q&A Maker service, an Azure Cognitive Search service will be created as well.

  2. Azure App Service: Since an app service plan (ap1) has already been created in the resource group rg1, the Q&A Maker service will utilize this existing app service to deploy the necessary web application components.

Therefore, when provisioning the Q&A Maker service in rg1, the automatically created resources will include an Azure Cognitive Search service and the utilization of the existing Azure App Service.

Pros:

  • Seamless integration with Azure Cognitive Search for efficient search capabilities.
  • Utilization of an existing app service plan for cost optimization.

Cons:

  • Additional configuration and setup may be required for Azure Cognitive Search integration.

Conclusion

In this article, we covered a series of Microsoft EI102 exam questions and provided detailed explanations and solutions for each question. We discussed various topics, including creating a web app on Azure virtual machines, predictive maintenance and anomaly detection, streaming speech-to-text, detecting learner presence in internet-based training, and provisioning Q&A Maker services.

By understanding these concepts and scenarios, you will be better prepared for the Microsoft EI102 exam and equipped with the knowledge to tackle similar questions. Remember to practice and explore Microsoft technologies regularly to enhance your skills and stay updated with the latest advancements.


Highlights

  • Question 16: Creating a web app on an Azure virtual machine and establishing direct connectivity to Azure Cognitive Search.
  • Question 17: Predictive maintenance and anomaly detection in IoT sensor data using the Anomaly Detector service.
  • Question 18: Streaming speech-to-text conversion using the Speech SDK and appropriate stream format selection.
  • Question 19: Detecting learner presence and attention in internet-based training using the Face and Speech APIs.
  • Question 20: Provisioning a Q&A Maker service and the automatically created Azure resources.

FAQ

Q: Are the solutions provided in the exam questions always the most effective ones? A: Not necessarily. It is crucial to critically evaluate the provided solutions and consider other alternatives if they better align with the goal and requirements of the scenario.

Q: Can the Anomaly Detector service be used for industries other than predictive maintenance? A: Yes, the Anomaly Detector service can be applied to various domains where anomaly detection in time series data is needed, such as finance, healthcare, and cybersecurity.

Q: Is it necessary to use the Azure Cognitive Search service with the Q&A Maker service? A: Yes, Azure Cognitive Search is utilized by the Q&A Maker service for efficient search capabilities within the pre-defined question and answer database.

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