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To pass the AI-102 Exam, candidates must demonstrate their skills in developing and deploying solutions that use Azure Cognitive Services, Azure Machine Learning, and other Azure AI tools. They must also be able to design and implement AI workflows, train and deploy machine learning models, and integrate AI solutions with other Azure services.
NEW QUESTION # 380
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:
Explanation:
To make a chatbot use uploaded company data from the Chat playground ("use your data") , your code must:
Create a chat request (not a plain completion), so you need the ChatCompletionsOptions type for the request options.
This is the options class used for chat completions across Azure OpenAI SDKs and is the one that supports attaching data sources (extensions).
Configure the Azure AI Search data source as a chat extension so the model grounds answers on your indexed documents.
In the SDK, this is done by adding an AzureCognitiveSearchChatExtensionConfiguration entry under the extensions/dataSources collection and setting properties like SearchKey and IndexName . This is the specific configuration object for Azure AI Search integration with Chat Completions.
Therefore, the two dropdowns should be completed as:
First dropdown: ChatCompletionsOptions()
Second dropdown: AzureCognitiveSearchChatExtensionConfiguration
This matches how "Use your data (RAG)" is implemented in Azure OpenAI-chat request + Azure AI Search chat extension-so the bot can answer questions using your uploaded/company data. Microsoft Learn Microsoft Azure AI Solution References Azure OpenAI "Use your data" (concepts): how chat uses data sources like Azure AI Search. Microsoft Learn Azure OpenAI client library for .NET (overview of chat options and extensions support). Microsoft Learn Chat Completions Options (SDK docs, shows dataSources/extensions concept in chat options).
Examples/discussion of AzureCognitiveSearchChatExtensionConfiguration usage with SearchKey
/IndexName.
NEW QUESTION # 381
You are building a chatbot that will use question answering in Azure Cognitive Service for Language.
You have a PDF named Docl.pdf that contains a product catalogue and a price list You upload Docl.pdf and train the model.
During testing, users report that the chatbot responds correctly to the following question: What is the price of
< product>?
The chatbot fails to respond to the following question: How much does <product* cost?
You need to ensure that the chatbot responds correctly to both questions.
Solution: From Language Studio, you create an entity for cost, and then retrain and republish the model.
Does this meet the goal?
Answer: A
Explanation:
In Azure Cognitive Service for Language (Question Answering), when users ask natural language questions, the service tries to match them to knowledge base content and synonyms.
* In the scenario, the model correctly answers "What is the price of <product>?" but fails on "How much does <product> cost?".
* This indicates that the service does not automatically recognize "price" and "cost" as equivalent terms.
* Creating an entity for cost will not fix this issue. Entities in custom text classification or conversational language understanding are used for extracting structured data (like dates, names, or locations) from text, not for synonym matching in question answering.
The correct solution is to add alternate phrasing (synonyms/alternate questions) in the QnA project for each knowledge base entry. For example, under the QnA pair that contains "What is the price of <product>?", you would add "How much does <product> cost?" as an alternate question. This way, both variations trigger the same answer.
Therefore, the proposed solution (create an entity for cost) does not meet the goal.
The answer: B. No
* Add alternative questions to improve matching - Azure AI Language Question Answering
* Improve accuracy with synonyms and alternate phrasing
* Overview of Question Answering in Azure AI Language
NEW QUESTION # 382
You have an Azure subscription. The subscription contains an Azure OpenAI resource that hosts a GPT-4 model named Modell and an app named Appl. App1 uses Model1.
You need to ensure that App1 will NOT return answers that include hate speech.
What should you configure for Model1?
Answer: A
Explanation:
To prevent an Azure OpenAI model (such as GPT-4 or GPT-3.5) from returning outputs that include hate speech, you must configure Azure AI Content Safety content filters on the model deployment. Content filters evaluate prompts and/or completions for categories like Hate/Unfairness, Harassment/Violence, Sexual, and Self-harm and can block or redact responses when they exceed your selected threshold. Applying a content filter to Model1 ensures that App1 will not return hate speech, satisfying the requirement.
Why the other options are incorrect:
* A. Frequency penalty - discourages token/repetition in output; it does not moderate harmful content.
* B. Abuse monitoring - provides telemetry/policy monitoring for potential misuse; it is not a response- time content filter and does not block specific hate content.
* D. Temperature - adjusts randomness/creativity; it does not enforce safety.
Microsoft Azure AI References
* Azure OpenAI Service - Safety system & content filtering (configure per deployment; categories include Hate).
* Azure AI Content Safety - Overview and moderation categories.
* Azure OpenAI Studio - Attach Content Safety filters to deployments (prompt/completion filtering).
NEW QUESTION # 383
You are building a retail chatbot that will use a QnA Maker service.
You upload an internal support document to train the model. The document contains the following question:
"What is your warranty period?"
Users report that the chatbot returns the default QnA Maker answer when they ask the following question:
"How long is the warranty coverage?"
The chatbot returns the correct answer when the users ask the following question: 'What is your warranty period?" Both questions should return the same answer.
You need to increase the accuracy of the chatbot responses.
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. (Choose three.)
Answer:
Explanation:
Explanation:
This scenario is about improving the accuracy of QnA Maker responses when user phrasing is different but semantically the same.
* Uploaded document contains: "What is your warranty period?"
* Users ask: "How long is the warranty coverage?"
* QnA Maker fails because it does not automatically equate these two phrasings.
* Both must map to the same answer.
Problem
* Add alternative phrasing to the question and answer (QnA) pair.
* In QnA Maker, you can add synonyms or paraphrases for a question.
* Example: Add "How long is the warranty coverage?" as an alternative phrasing to the existing QnA pair.
* This ensures the system treats both phrasings as the same question.
* Retrain the model.
* After modifying the knowledge base with new alternatives, you must retrain the model.
* This step incorporates the updates into the QnA Maker service.
* Republish the model.
* To make the changes live and accessible by the chatbot endpoint, the retrained model must be republished.
* Without republishing, the chatbot will still use the old knowledge base.
Correct Sequence of Actions
* Add a new QnA pair # Not needed, because we want both questions to map to the same existing answer, not duplicate answers.
* Add additional questions to the document # That's not required when QnA Maker already supports adding alternative phrasing within the portal or JSON editor.
Actions That Are Not Required
* Add alternative phrasing to the question and answer (QnA) pair.
* Retrain the model.
* Republish the model.
Final Answer (Sequence)
* QnA Maker - Add alternative phrasing
* Train and publish a QnA Maker knowledge base
Microsoft References
NEW QUESTION # 384
You are building an app that will scan confidential documents and use the Language service to analyze the contents.
You provision an Azure Cognitive Services resource.
You need to ensure that the app can make requests to the Language service endpoint. The solution must ensure that confidential documents remain on-premises.
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:
1 - Provision an on-premises Kubernetes...
2 - Pull an image from the Microsoft Container Registry...
3 - Run the container and specify an API key...
NEW QUESTION # 385
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