AI-102 CERTIFIED QUESTIONS & TEST AI-102 DUMPS

AI-102 Certified Questions & Test AI-102 Dumps

AI-102 Certified Questions & Test AI-102 Dumps

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Tags: AI-102 Certified Questions, Test AI-102 Dumps, Valid AI-102 Exam Answers, AI-102 Latest Braindumps Book, AI-102 Reliable Cram Materials

2025 Latest 2Pass4sure AI-102 PDF Dumps and AI-102 Exam Engine Free Share: https://drive.google.com/open?id=1M2hEZ81juqwHdkfjgyYgYkxeNBz87Gui

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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.

The Designing and Implementing a Microsoft Azure AI Solution certification exam covers various topics related to designing and implementing AI solutions using Azure services, including natural language processing, computer vision, speech recognition, and machine learning. AI-102 Exam also covers topics related to data preparation, data wrangling, and data analysis, which are essential skills for designing and implementing AI solutions. Candidates are expected to have a good understanding of Azure services, including Azure Cognitive Services, Azure Machine Learning, and Azure Databricks.

>> AI-102 Certified Questions <<

Microsoft AI-102 Questions To Make Sure Results [2025]

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Microsoft AI-102 Exam is a certification that validates a candidate's knowledge and skills in designing and implementing AI solutions on the Microsoft Azure platform. It is an advanced-level exam that requires a solid understanding of Azure services and tools, as well as expertise in AI technologies such as machine learning, natural language processing, and computer vision.

Microsoft Designing and Implementing a Microsoft Azure AI Solution Sample Questions (Q45-Q50):

NEW QUESTION # 45
You are building a multilingual chatbot.
You need to send a different answer for positive and negative messages.
Which two Text Analytics APIs should you use? Each correct answer presents part of the solution. (Choose two.) NOTE: Each correct selection is worth one point.

  • A. Sentiment Analysis
  • B. Key Phrases
  • C. Named Entity Recognition
  • D. Detect Language
  • E. Linked entities from a well-known knowledge base

Answer: A,D

Explanation:
Explanation
B: The Text Analytics API's Sentiment Analysis feature provides two ways for detecting positive and negative sentiment. If you send a Sentiment Analysis request, the API will return sentiment labels (such as "negative",
"neutral" and "positive") and confidence scores at the sentence and document-level.
D: The Language Detection feature of the Azure Text Analytics REST API evaluates text input for each document and returns language identifiers with a score that indicates the strength of the analysis.
This capability is useful for content stores that collect arbitrary text, where language is unknown. Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to- sentiment-analysis?tabs=version-3-1
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to- language-detection


NEW QUESTION # 46
You have a bot that was built by using the Microsoft Bot Framework composer as shown in the following exhibit.

Use the drop-down menus 10 select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 47
You are creating an enrichment pipeline that will use Azure Cognitive Search. The knowledge store contains unstructured JSON data and scanned PDF documents that contain text.
Which projection type should you use for each data type? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: Object projection
Object projections are JSON representations of the enrichment tree that can be sourced from any node.
Box 2: File projection
File projections are similar to object projections and only act on the normalized_images collection.
Reference:
https://docs.microsoft.com/en-us/azure/search/knowledge-store-projection-overview


NEW QUESTION # 48
You are building a model to detect objects in images.
The performance of the model based on training data is shown in the following exhibit.

Answer:

Explanation:

Explanation:


NEW QUESTION # 49
You are developing an application to recognize employees' faces by using the Face Recognition API. Images of the faces will be accessible from a URI endpoint.
The application has the following code.

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:
A. True
B. True
C. True
B: see this example code from documentation that uses PersonGroup of size 10,000 :
https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-how-to-topics/how-to-add-faces the question wants to trick you into thinking you need to use a LargePersonGroup for a size of 10,000 - but the documentation for it doesn't include this limitation or criteria: https://docs.microsoft.com/en-us/azure
/cognitive-services/face/face-api-how-to-topics/how-to-use-large-scale
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/face-api-how-to-topics/use-persondirectory


NEW QUESTION # 50
......

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