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Microsoft AI-102 is a certification exam designed for professionals who want to showcase their skills in designing and implementing Microsoft Azure AI solutions. AI-102 exam evaluates the candidate's proficiency in designing AI solutions using Azure Cognitive Services, Azure Machine Learning, and other Azure AI services. The AI-102 Certification Exam is the successor to the AI-100 exam, which was previously used to assess candidates' skills in designing and implementing AI solutions on Azure.
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Microsoft Designing and Implementing a Microsoft Azure AI Solution Sample Questions (Q213-Q218):
NEW QUESTION # 213
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You create a web app named app1 that runs on an Azure virtual machine named vm1. Vm1 is on an Azure virtual network named vnet1.
You plan to create a new Azure Cognitive Search service named service1.
You need to ensure that app1 can connect directly to service1 without routing traffic over the public internet.
Solution: You deploy service1 and a public endpoint, and you configure a network security group (NSG) for vnet1.
Does this meet the goal?
- A. Yes
- B. No
Answer: A
Explanation:
Explanation
https://docs.microsoft.com/en-us/azure/virtual-network/network-security-groups-overview#network-security-gro
https://docs.microsoft.com/en-us/azure/virtual-network/virtual-networks-overview
NEW QUESTION # 214
You are developing the knowledgebase.
You use Azure Video Analyzer for Media (previously Video indexer) to obtain transcripts of webinars.
You need to ensure that the solution meets the knowledgebase requirements.
What should you do?
- A. Build a custom Person model for webinar presenters
- B. Configure audio indexing for videos only
- C. Create a custom language model
- D. Enable multi-language detection for videos
Answer: B
Explanation:
Can search content in different formats, including video
Audio and video insights (multi-channels). When indexing by one channel, partial result for those models will be available.
Keywords extraction: Extracts keywords from speech and visual text.
Named entities extraction: Extracts brands, locations, and people from speech and visual text via natural language processing (NLP).
Topic inference: Makes inference of main topics from transcripts. The 2nd-level IPTC taxonomy is included.
Artifacts: Extracts rich set of "next level of details" artifacts for each of the models.
Sentiment analysis: Identifies positive, negative, and neutral sentiments from speech and visual text.
Reference:
https://docs.microsoft.com/en-us/azure/azure-video-analyzer/video-analyzer-for-media-docs/video-indexer-overview
NEW QUESTION # 215
Hotspot Question
You have a chatbot that uses Azure OpenAI to generate responses.
You need to upload company data by using Chat playground. The solution must ensure that the chatbot uses the data to answer user questions.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 216
You are developing an application that will recognize faults in components produced on a factory production line. The components are specific to your business.
You need to use the Custom Vision API to help detect common faults.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Step 1: Create a project
Create a new project.
Step 2: Upload and tag the images
Choose training images. Then upload and tag the images.
Step 3: Train the classifier model.
Train the classifier
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/getting-started-build-a- classifier
NEW QUESTION # 217
You develop a test method to verify the results retrieved from a call to the Computer Vision API. The call is used to analyze the existence of company logos in images. The call returns a collection of brands named brands.
You have the following code segment.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* Query successful
An analysis of the provided code segment is needed to determine the truth value of the statements, which are not explicitly provided in the prompt. Since I don't have the statements, I will analyze the code and infer common types of questions related to this code structure to provide a helpful response.
The code segment is written in a C#-like syntax and iterates through a collection named brands.
C#
foreach (var brand in brands)
{
if (brand.Confidence >= .75)
Console.WriteLine($"Logo of {brand.Name} between {brand.Rectangle.X}, {brand.Rectangle.Y} and {brand.
Rectangle.W}, {brand.Rectangle.H}");
}
* Iteration: The foreach (var brand in brands) loop processes each item (presumably a detected brand or logo) in the brands collection.
* Filtering/Condition: The if (brand.Confidence >= .75) statement acts as a filter. It only processes a brand if its associated Confidence value (likely a floating-point number between 0.0 and 1.0) is greater than or equal to 0.75.
* Output: For brands that pass the confidence threshold, a line of text is printed to the console using string interpolation ($"").
* It reports the Name of the brand.
* It reports the bounding box coordinates, presumably X (horizontal position), Y (vertical position), W (width), and H (height), which are properties of the nested brand.Rectangle object.
NEW QUESTION # 218
......
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