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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Plan and manage Azure AI solutions | 25–30% | - Design Azure AI infrastructure
|
| Topic 2: Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Topic 3: Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Topic 4: Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Topic 5: Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
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NEW QUESTION # 126
You have 100,000 images.
You need to build an app that will perform the following actions:
* Identify road signs in the images and extract the text on the signs.
* Analyze the text to identify well-known locations.
The solution must minimize development effort.
Answer:
Explanation:
Explanation:
* Extract the text: Azure Vision in Foundry Tools
* Identify well-known locations: Azure Language in Foundry Tools
Azure Vision in Foundry Tools is the appropriate service for extracting text from road signs. Its pretrained Optical Character Recognition capability detects printed or handwritten text in images and returns recognized words, lines, and their coordinates. Microsoft specifically identifies street signs as an applicable OCR scenario. Because the OCR model is already trained, it avoids the data labeling and custom-model training that would increase development effort.
After OCR produces the text, Azure Language in Foundry Tools can analyze it through entity linking . Entity linking recognizes and disambiguates well-known entities and connects them to entries in a recognized knowledge base. This is suitable for determining that text on a sign represents a known geographic location rather than merely returning an unclassified text string. Named entity recognition can also classify extracted entities under categories such as Location, but entity linking provides the stronger interpretation implied by
"well-known locations."
Azure Document Intelligence is optimized for structured and text-heavy business documents, while Azure AI Search provides indexing and retrieval rather than primary OCR and linguistic analysis.
Study Guide alignment: implement OCR-based ingestion and use Foundry Tools to extract entities from text .
NEW QUESTION # 127
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have a multimodal AI generative model that accepts image uploads and uses extracted image text to generate responses.
You discover that users can upload unsafe images and embed hidden instructions into images to manipulate the model.
You need to implement controls to mitigate the risk.
Solution: You configure image moderation to block unsafe content before processing the images.
Does this meet the goal?
Answer: B
Explanation:
Correct:
* You configure a prompt shield for documents.
Prompt Shield for Documents: Highly Effective (Critical Defense)
How it helps: This shield specifically scans untrusted, third-party data inputs (like external documents or text extracted from uploaded images).
Mechanism: It evaluates the extracted image text before it is sent to the LLM to identify hidden jail
* You configure a prompt shield for user prompts.
Prompt Shield for User Prompts: Partially Effective (Defense in Depth)
How it helps: This shield targets direct jailbreak attempts written manually by the user in the text prompt field accompanying the upload.
Mechanism: It prevents the user from typing supporting instructions that prime the model to execute the hidden instructions found within the image.
* You configure image moderation to block unsafe content before processing the images.
Implementing rigorous image moderation is one of the most effective ways to secure multimodal AI systems against these threats. Moderation acts as a necessary gatekeeper, preventing malicious inputs from ever reaching the generative model.
Incorrect:
* You configure protected material detection.
Protected Material Detection: Ineffective for this Threat
Why it does not help: This feature is designed to scan model outputs to prevent the generation of copyrighted text, proprietary source code, or licensed imagery.
Limitation: It does not scan inputs for adversarial instructions and will not prevent a user from manipulating the model's logic.
Reference:
https://www.upgrad.com/blog/what-is-multimodal-ai/
https://learn.microsoft.com/en-us/azure/ai-services/content-safety/concepts/jailbreak-detection
NEW QUESTION # 128
You have a web app named App1 that sends requests to a multimodal chat model deployment in a Microsoft Foundry project. User messages can contain both text and images. Currently, App1 includes image URLs as plain text inside the message content, so the model cannot recognize them as images. You need to send the message as a structured array that includes both the text portion and the image reference.
Answer: D
NEW QUESTION # 129
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: C
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 # 130
You have a Microsoft Foundry project that contains an agent.
The agent ingests scanned PDF vendor invoices that contain tables and embedded QR codes.
The agent must preserve the PDF layout in the extracted output to ensure that downstream processing can reference sections and tables.
You plan to call Azure Content Understanding in Foundry Tools.
You need to extract content and layout elements and detect QR codes without requiring a language model deployment.
Which built-in analyzer should you use?
Answer: A
Explanation:
To extract content, preserve tables and document layout, and detect embedded QR codes without deploying a large language model (LLM), you should use the built-in prebuilt-layout analyzer.
Note:
Unlike schema-driven extraction models in Content Understanding that utilize generative AI orchestration, the Layout analyzer is a highly efficient machine-learning-based model. It natively outputs structural geometry and decodes barcodes without requiring an active LLM deployment or provisioned throughput.
Structural Preservation: It extracts headers, paragraphs, and nested sections, returning precise spatial bounding boxes for every single element. Downstream agents can utilize this geometric metadata to anchor or cross-reference sections accurately.
Advanced Table Mapping: It maps intricate, multi-page invoice tables, capturing text alongside row and column indices. You can configure the output structure format natively into Markdown or HTML tables to maintain formatting cleanliness.
Built-in QR and Barcode Decoding: By default, the configuration parameter enableBarcode is set to true. The analyzer scans the scanned PDF image, isolates 2D code regions, and appends the decoded string payload into the output JSON alongside text blocks.
Zero LLM Dependency: It does not route text to foundational models like GPT-4o for its extraction, keeping processing latency low and lowering operational costs significantly.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/quickstart/content-understanding-studio
NEW QUESTION # 131
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