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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement agentic solutions | 20-25% | - Build AI agents
|
| Topic 2: Implement generative AI solutions | 25-30% | - Optimize and evaluate models
|
| Topic 3: Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Topic 4: Plan and manage Azure AI solutions | 25-30% | - Manage AI solution lifecycle
|
| Topic 5: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
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NEW QUESTION # 32
You need to configure Agent1 to answer customer questions about only the Contoso products. The solution must meet the business requirements.
What should you do?
Answer: A
Explanation:
The correct answer is B. Modify the system message instructions . The case study states that Agent1 answers general questions about Contoso products and that the business requirement is for Agent1 to answer questions only about the products sold by Contoso . This is a behavioral boundary for the agent, so it should be implemented in the highest-priority instructions that define the agent's role, allowed scope, and refusal behavior.
Microsoft Foundry guidance states that a system message is used to steer model behavior, define the assistant' s role and boundaries, and add safety or quality constraints for the scenario. The system message should instruct Agent1 to answer only when the question concerns Contoso products, use the configured Contoso product documentation as grounding, and politely refuse or redirect questions about non-Contoso products.
Top-p sampling and temperature control randomness, not business-domain scope. Increasing temperature would make responses less deterministic. Few-shot examples can support desired behavior, but examples alone are weaker than explicit system-level instructions for defining operating boundaries. Reference topics:
system message design, prompt engineering, agent instructions, response constraints, and grounded generative AI behavior.
NEW QUESTION # 33
You have a Microsoft Foundry project that contains an agent. The agent uses Azure AI Search for Retrieval Augmented Generation (RAG). You plan to ingest and index PDF product manuals. You need to build a solution that supports semantic similarity matching. The solution must ensure that the agent retrieves relevant data when user questions use different wording than the product manuals.
Answer: A
Explanation:
Use vector search because it performs similarity matching based on semantic meaning rather than requiring literal keyword overlap. During ingestion, the product manuals should be divided into appropriately sized chunks. An embedding model converts each chunk into a numerical vector, which Azure AI Search stores in a vector field. At query time, the user's question is also converted into an embedding, and the service retrieves chunks whose vectors are closest to the query vector. Consequently, wording such as "reset the device to factory settings" can match manual content such as "restore the unit's original configuration," despite minimal lexical overlap. Azure AI Search explicitly defines vector fields as enabling similarity searches over mathematically represented content and supports this retrieval pattern for RAG applications.
Semantic ranking improves the ordering of an existing result set but is not the indexing mechanism that stores embeddings and performs nearest-neighbor similarity matching. Suggesters provide search-as-you-type functionality, while analyzers control tokenization and lexical processing for text fields.
This aligns with the AI-103 Study Guide objectives for implementing RAG, configuring Azure AI Search grounding, generating embeddings, and selecting vector retrieval techniques for agentic solutions.
NEW QUESTION # 34
Hotspot Question
You have a Microsoft Foundry project that contains a deployed chat model.
You have a Python service that sends API requests to the model. The service is integrated with an automated validation system that compares generated outputs against approved response patterns.
Stakeholders report that small wording differences are causing validation mismatches.
You need to update the request parameters to improve output stability. The solution must maximize reasoning quality.
How should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 35
You need to measure the public perception of your brand on social media by using natural language processing. Which Azure service should you use?
Answer: D
Explanation:
Azure Language in Foundry Tools provides sentiment analysis and opinion mining , which are the appropriate natural language processing capabilities for evaluating public perception. Sentiment analysis processes unstructured social-media text and assigns classifications such as positive, neutral, or negative, together with confidence scores. Opinion mining extends this analysis by associating expressed sentiment with particular aspects, attributes, products, or services mentioned in the text. This allows the application to determine not only the overall attitude toward the brand but also which specific brand characteristics are receiving favorable or unfavorable reactions.
Azure Document Intelligence is intended primarily for extracting text, fields, tables, and structures from documents. Content Safety detects potentially harmful material rather than measuring consumer opinion.
Azure Vision processes visual content and is not the primary service for sentiment classification of written social-media posts.
Azure Language can be accessed through REST APIs, client libraries, or supported containers. The application submits the social-media text and receives document-level and sentence-level sentiment results that can be aggregated into brand-perception metrics, dashboards, or alerts.
Study Guide alignment: Implement text analysis solutions - perform sentiment analysis and opinion mining, process unstructured text, and interpret confidence scores and sentiment classifications.
NEW QUESTION # 36
You have a Microsoft Foundry project that contains an agent. The agent uses Azure Al Search as the retriever.
You plan to ingest PDFs into an Azure Al Search index to ensure that the agent can ground responses in texts in both documents and embedded images.
Users require citations that link to the source files.
You need to ensure that during indexing, the images are extracted into a structure that can be used as input for the built-in optical character recognition (OCR) skill.
Which indexing approach should you use?
Answer: C
Explanation:
The correct indexing approach is to use an indexer to extract image data into a normalized_images collection . In Azure AI Search enrichment pipelines, embedded images in PDFs are not passed directly from the text content field into OCR. Instead, the indexer must perform document cracking and image extraction by enabling the indexer image action. Microsoft's Azure AI Search documentation states that image-processing skills such as OCR and image analysis expect normalized images, and that enabling imageAction causes embedded images to be extracted and normalized for downstream skills.
The OCR skill is designed to receive image input from /document/normalized_images/*. Microsoft's skillset tutorial specifically states that the OCR skill assumes a normalized_images field exists and that this field is generated by setting the indexer imageAction configuration to generateNormalizedImages. The document extraction skill reference also confirms that generateNormalizedImages creates an array of normalized images during document cracking for OCR and image analysis.
Option A is incorrect because OCR does not run directly against the index content field. Option B maps outputs after enrichment; it does not extract images. Option D reshapes data but does not create the required normalized image collection. Reference topics: Azure AI Search indexers, AI enrichment, OCR skill, imageAction, and normalized_images.
NEW QUESTION # 37
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