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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
| Topic 2: Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
| Topic 3: Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
| Topic 4: Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
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NEW QUESTION # 39
Which parameter should you configure to produce a more diverse range of tokens in the responses from a chat solution that uses the Azure OpenAI GPT-3.5 model?
Answer: A
Explanation:
In Azure OpenAI Service, model behavior during text or chat generation is controlled by several parameters, such as temperature, max tokens, top_p, presence penalty, and frequency penalty. According to Microsoft Learn's documentation for Azure OpenAI GPT models, the presence penalty influences how likely the model is to introduce new or diverse tokens in its responses.
Specifically, the presence penalty discourages the model from repeating previously used tokens, encouraging it to explore new topics or ideas instead of sticking to existing ones. Increasing the presence penalty value typically results in more diverse and creative outputs, while lowering it makes responses more repetitive or focused.
Option analysis:
* A. Max response (Max tokens): Controls the maximum length of the generated response, not its diversity.
* B. Past messages included: Defines how much chat history the model considers for context; it doesn't affect diversity directly.
* C. Presence penalty: Encourages novelty and introduces new tokens-this is correct for increasing response variety.
* D. Stop sequence: Specifies a sequence of characters or tokens where the model should stop generating output.
NEW QUESTION # 40
Capturing text from images is an example of which type of Al capability?
Answer: B
NEW QUESTION # 41
You need to develop a web-based AI solution for a customer support system. Users must be able to interact with a web app that will guide them to the best resource or answer.
Which service should you integrate with the web app to meet the goal?
Answer: A
Explanation:
QnA Maker is a cloud-based API service that lets you create a conversational question-and-answer layer over your existing data. Use it to build a knowledge base by extracting questions and answers from your semistructured content, including FAQs, manuals, and documents. Answer users' questions with the best answers from the QnAs in your knowledge base-automatically. Your knowledge base gets smarter, too, as it continually learns from user behavior.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/qna-maker/
NEW QUESTION # 42
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/linear-regression
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize-model-clustering Regression is a form of machine learning that is used to predict a numeric label based on an item's features.
https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/introduction
NEW QUESTION # 43
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
NEW QUESTION # 44
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