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| Section | Objectives |
|---|---|
| Topic 1: Plan and Manage Azure AI Solutions | - Responsible AI principles and governance - Azure AI resource provisioning and configuration - Model selection and lifecycle management |
| Topic 2: Implement Natural Language Processing Solutions | - Translation and multilingual support - Language understanding and intent recognition - Text analytics and summarization |
| Topic 3: Develop Generative AI Applications and Agents | - Azure OpenAI Service integration
|
| Topic 4: Knowledge Mining and Information Retrieval | - RAG (Retrieval Augmented Generation) patterns - Azure AI Search configuration - Indexing and semantic search |
| Topic 5: Implement Computer Vision Solutions | - OCR and document intelligence - Image classification and object detection |
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NEW QUESTION # 21
You are developing an app that will perform a sentiment analysis of social media posts by using the Azure AI Language service.
You perform a test on a sample post.
You need to quantify the results of the test.
Which JSON property should you review?
Answer: D
Explanation:
Sentiment analysis
The sentiment analysis feature assigns sentiment labels, such as "negative," "neutral," and
"positive." The service determines these labels using the highest confidence score. Sentiment is evaluated at both the sentence level and the document level. This feature also returns confidence scores between 0 and 1 for each document & sentences within it for positive, neutral, and negative sentiment.
In Azure AI Language's sentiment analysis, confidenceScores are numerical values between 0 and 1 that represent the probability that the text belongs to a specific sentiment (positive, neutral, or negative). A score closer to 1 indicates a higher confidence from the service that the text exhibits that sentiment, while a lower score signifies less confidence in that particular label. The service calculates these scores for both individual sentences and the entire document, providing a granular understanding of sentiment.
How to interpret confidenceScores:
High Score (close to 1): The model is very sure about the assigned sentiment. For example, a positive score of 0.95 means the model is 95% confident the text is positive.
Low Score (close to 0): The model is not very sure about the assigned sentiment.
Scores for each sentiment: For any given piece of text, the service returns a score for positive, neutral, and negative sentiment. The sentiment label that receives the highest score is assigned as the overall sentiment for that text.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/sentiment-opinion-mining/overview
NEW QUESTION # 22
You are designing a content management system.
You need to ensure that the reading experience is optimized for users who have reduced comprehension and learning differences, such as dyslexia. The solution must minimize development effort.
Which Azure service should you include in the solution?
Answer: C
Explanation:
Include Azure AI Immersive Reader in your solution. It is an applied AI service that provides built- in accessibility features like read-aloud (text-to-speech), real-time translation, line focusing, and customizable typography. It requires no machine learning expertise and integrates easily to minimize development effort.
Reference:
https://azure.microsoft.com/en-us/products/ai-services/ai-immersive-reader
NEW QUESTION # 23
Case Study 1 - Contoso, Ltd
Overview
Company Information
Contoso, Ltd is a multinational retail company that builds, deploys, and manages generative AI and agent-based solutions by using Microsoft Foundry.
Existing Environment
Identity Environment
Contoso uses Microsoft Entra ID for identity management, authentication, and authorization capabilities that enable agents to access organizational resources and services.
Contoso recently formed a new AI engineering team named Agent1Dev Team to optimize and maintain existing AI solutions.
The team collaborates with solution architects, DevOps engineers, and security engineers to design, implement. monitor, and secure AI applications.
Contoso also has a team named Agent1Test Team that is responsible for validating AI solutions before the solution deployments.
Generative Environment
Contoso has a Microsoft Foundry deployment that contains two projects named Project1 and Project2.
Project1
Project1 contains a customer support agent named Agent1 that assists customers with product inquiries and troubleshooting requests.
Agent1 has the following configurations:
- Agent1 uses a base model deployment.
- A safety evaluation pipeline is NOT enabled.
- Tool invocation approval workflows are NOT enabled.
- Conversation memory constraints are NOT configured.
Agent1 interacts with customers by using digital support channels and answers general questions about Contoso products.
Project1 is deployed to an Azure region located in the European Union (EU).
Agent1Dev Team will use Project1 to optimize and maintain Agent1.
Project2
Project2 contains a deployed video generation model. The marketing department at Contoso has access to Project2 and plans to use the model to develop a video creation solution.
Development of the solution is incomplete.
Data Environment
Contoso stores product-related information in Azure resources that support AI applications.
The Azure environment contains an Azure Blob Storage account named storage1 that stores product detail sheets for all the Contoso products.
The product sheets include specifications, feature descriptions, and product support information that Agent1 can use to answer customer questions. The product sheets are stored in the PDF format.
Problem Statements
Contoso identifies the following issues:
- Agent1 has only general knowledge of the Contoso products.
- A recent chat interaction with Agent1 was analyzed for sentiment. The results of the analysis have NOT been processed yet.
- Agent1 does NOT use the detailed product information in the product sheets stored in storage1 when responding to customer questions.
- The finance department at Contoso reports that vendor invoices must be reviewed manually to ensure that the invoices match the terms defined in the vendor contracts. The invoices contain tables, logos, and varied layouts that make the documents difficult to process consistently.
Requirements
Planned Changes
Contoso plans to implement the following changes:
- Implement a solution for Project1 that analyzes the vendor invoices by evaluating both the visual layout and the textual content of the invoices, so that the invoice details can be verified against the vendor contract terms.
- Update the base model deployment used by Agent1 and standardize the model version to ensure continuity and consistent responses.
- Enable Agent1 to retrieve and use the detailed product information from the product sheets stored in storage1.
- Implement an indexing solution for the product sheets that Agent1 can use to answer customer questions.
- Complete the development of the video creation solution.
Technical Requirements
Contoso identifies the following technical requirements:
- The model deployment used by Agent1 must support scalable, high-throughput generative AI workloads and dynamically scale to handle variable customer support traffic, without requiring reserved throughput capacity.
- The product sheets must be processed by using an indexing pipeline that enables semantic and vector search, so that Agent1 can retrieve the relevant product information.
- Responses generated by using the product sheet information must be relevant, complete, and accurate.
- Agent1 must be able to use the product sheets to answer natural language questions about product details.
- The model version used by Agent1 must remain consistent to ensure stable responses.
- The data processed by the model must remain within the EU.
Security and Compliance Requirements
Contoso identifies the following security and compliance requirements:
- API keys must NOT be used to access Foundry-deployed models.
- Access to the Azure resources must follow the principle of least privilege.
- The developers at Contoso must authenticate to Microsoft Foundry resources by using Microsoft Entra authentication.
- Access to Project1 must be assigned to the members of Agent1Dev Team by using a security group named SC_Agent1_Dev.
- Access to Project1 must be assigned to the members of Agent1Test Team by using a security group named SC_Agent1_Test.
- Agent1 must never reveal customer information, even if a document that contains customer data is added erroneously to the product sheet repository in storage1.
- The product sheets might contain images that include embedded text. Agent1 must be protected from malicious instructions potentially hidden within the images.
Business Requirements
Contoso identifies the following business requirements:
- Users that interact with Agent1 must have a personalized experience in future interactions, including the ability for Agent1 to retain conversation context and recall relevant information from previous interactions.
- Agent1 must answer questions only about the products sold by Contoso.
You need to configure an indexing pipeline for Agent1 to retrieve the relevant product information in storage1. The solution must meet the technical requirement.
Which two built-in skills should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,E
Explanation:
The most essential skills for this scenario are Azure OpenAI Embedding and Text Split.
Azure OpenAI Embedding: This skill is critical for generating the vector representations (embeddings) of your text, which directly enables the required vector search capability.
Text Split: This skill is essential because LLMs and embedding models have strict token limits.
Breaking large product detail sheets into smaller chunks ensures the text fits into the embedding model and improves the accuracy of semantic search.
Incorrect:
[Not B]
Entity Recognition: This extracts specific entities like names, dates, or locations. While helpful for advanced filtering, it is not a foundational requirement to enable basic semantic or vector search.
[Not D]
Merge: This skill combines text from multiple fields into a single string. Since product sheets are already unified documents, splitting and chunking them is the priority rather than merging separate fields.
[Not E]
Language Detection: This identifies the language of the input text. Unless your product sheets are completely multilingual and require conditional routing to different language models, this skill is secondary.
[Not F]
Key Phrase Extraction: This pulls out main talking points or keywords. This is primary used for traditional keyword tagging or basic search indexing, whereas your requirement specifically dictates vector and semantic-based retrieval.
Scenario:
Technical Requirements;
*-> The product sheets must be processed by using an indexing pipeline that enables semantic and vector search, so that Agent1 can retrieve the relevant product information.
Data environment: The Azure environment contains an Azure Blob Storage account named storage1 that stores product detail sheets for all the Contoso products.
Planned changes: Enable Agent1 to retrieve and use the detailed product information from the product sheets stored in storage1.
Reference:
https://www.rheininsights.com/blog/en/Retrieval+Augmented+Generation+with+Azure+AI+Search
+and+Atlassian+Confluence.php
NEW QUESTION # 24
You have a Microsoft Foundry project that contains a workflow for a customer support triage process.
You have an Ask a question node that stores user responses in a local variable named Var01.
You need to create the following Power Fx expressions:
* An if/else condition expression that ensures that Var01 contains a value
* A Send message expression that returns the stored user response in uppercase How should you configure the expressions? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
If/else condition expression: Not(IsBlank(Local.Var01))
Send message expression: {Upper(Local.Var01)}
The correct if/else condition is Not(IsBlank(Local.Var01)) because the workflow must continue only when the Ask a question node has captured a nonblank value. In Power Fx, IsBlank checks whether a value is blank, while Not reverses that Boolean result; therefore, Not(IsBlank(Local.Var01)) evaluates to true only when Var01 contains user input. IsEmpty(Local.Var01) is not the correct choice because IsEmpty checks whether a table contains records, not whether a text variable has a value. The Power Fx formula reference distinguishes these functions by defining IsBlank for blank values and IsEmpty for empty tables.
The Send message expression must be {Upper(Local.Var01)}. Microsoft Foundry workflow guidance uses the same pattern for an Ask a question node saved as Var01, followed by a Send message action that outputs
{Upper(Local.Var01)}. The Local. prefix is required because the response is stored in a local workflow variable, and Upper() converts the stored text response to uppercase. Reference topics: Microsoft Foundry workflows, Power Fx expressions, local variables, if/else branching, and Send message actions.
NEW QUESTION # 25
You are developing prompts for a Micosoft Foundry project that classifies incoming support tickets by category.
You need to improve accuracy by showing the model how correct classifications look, without retaining the model or storing knowledge permanently.
Which prompt engineering approach should you use?
Answer: A
Explanation:
Few-shot prompting is the best approach for this project. This technique improves classification accuracy by including a few high-quality examples directly inside the prompt, giving the model a clear pattern to follow without modifying its weights or storing data permanently.
Zero retraining: Works entirely through in-context learning during the API call.
No permanent storage: The knowledge disappears as soon as the inference request is completed.
Immediate accuracy boost: Demonstrates formatting, nuances, and edge cases directly to the model.
Reference:
https://www.linkedin.com/pulse/top-interview-questions-answers-prompt-engineering-nitin-sharma-ka4fc
NEW QUESTION # 26
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