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The AI-102 Certification Exam covers a wide range of topics, including Azure Cognitive Services, Natural Language Processing (NLP), and Computer Vision. You will be tested on your ability to develop and implement AI solutions using various Azure services and tools.
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Microsoft AI-102 exam is suitable for individuals who have experience in developing AI solutions and are looking to showcase their expertise in designing and implementing AI solutions using Microsoft Azure services. AI-102 exam also provides an opportunity for professionals to enhance their skills in the field of AI and prepare for a career in AI development.
Microsoft AI-102 certification exam is ideal for AI developers, data scientists, and solution architects who want to demonstrate their expertise in developing AI solutions on Azure. Candidates who pass this certification exam will be able to design and implement AI solutions that can process natural language, recognize images and sounds, and make predictions based on data. The AI-102 Certification Exam also covers topics such as data preparation, feature engineering, model selection, and deployment.
NEW QUESTION # 316
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
NEW QUESTION # 317
You are building a chatbot.
You need to use the Content Moderator service to identify messages that contain sexually explicit language.
Which section in the response from the service will contain the category score, and which category will be assigned to the message? To answer, select the appropriate options in the answer area, NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 318
You have a Computer Vision resource named contoso1 that is hosted in the West US Azure region.
You need to use contoso1 to make a different size of a product photo by using the smart cropping feature.
How should you complete the API URL? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 319
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 have an Azure Cognitive Search service.
During the past 12 months, query volume steadily increased.
You discover that some search query requests to the Cognitive Search service are being throttled.
You need to reduce the likelihood that search query requests are throttled.
Solution: You migrate to a Cognitive Search service that uses a higher tier.
Does this meet the goal?
Answer: A
Explanation:
A simple fix to most throttling issues is to throw more resources at the search service (typically replicas for query-based throttling, or partitions for indexing-based throttling). However, increasing replicas or partitions adds cost, which is why it is important to know the reason why throttling is occurring at all.
Reference:
https://docs.microsoft.com/en-us/azure/search/search-performance-analysis
NEW QUESTION # 320
You are examining the Language service output of an application.
The text analyzed is: Our tour guide took us up the Space Needle during our trip to Seattle last week.
The response contains the data shown in the following table.
Which Language service API is used 10 analyze the Text?
Answer: A
Explanation:
The given text:
" Our tour guide took us up the Space Needle during our trip to Seattle last week. " The Language service output shows:
Text
Category
ConfidenceScore
Tour guide
PersonType
0.45
Space Needle
Location
0.38
Trip
Event
0.78
Seattle
Location
0.78
Last week
DateTime
0.80
* NER identifies named entities (people, places, dates, events, organizations, etc.) in text and classifies them into categories with confidence scores.
* In the table, we see categories such as PersonType, Location, Event, DateTime # these are classic outputs of NER.
* A. Entity Linking # Identifies entities and links them to a knowledge base (like Wikipedia). Output would include links/IDs rather than just categories.
* C. Key Phrase Extraction # Returns important phrases ("tour guide", "Space Needle", "trip", "Seattle",
"last week") but does not assign categories like PersonType, Location, DateTime.
* D. Sentiment Analysis # Returns sentiment scores (positive/neutral/negative), not entity categories.
Why the correct answer is Named Entity Recognition (NER): Why not the others:
The Answer: B. Named Entity Recognition
* Named Entity Recognition in Azure AI Language
* Entity Linking vs Named Entity Recognition
Microsoft References
NEW QUESTION # 321
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