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| Section | Weight | Objectives |
|---|---|---|
| Describe Artificial Intelligence workloads and considerations (15-20%) | 15-20% | - Identify features of common AI workloads
|
| Describe features of generative AI workloads on Azure (20-25%) | 20-25% | - Identify features of generative AI solutions
|
| Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%) | 15-20% | - Identify features of common NLP workload scenarios
|
| Describe features of computer vision workloads on Azure (15-20%) | 15-20% | - Identify Azure tools and services for computer vision tasks
|
| Describe fundamental principles of machine learning on Azure (15-20%) | 15-20% | - Identify common machine learning techniques
|
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NEW QUESTION # 12
You are reviewing best practices for using AI at your company.
Which Microsoft responsible AI principle is each task an example of? To answer, drag the appropriate principles to the correct tasks. Each task may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation:
Fairness
Privacy and security
Transparency
Reliability and safety
Comprehensive and Detailed Explanation with all Azure AI documents: =
Evaluating model outputs to ensure that decisions are NOT biased against specific demographic groups:
Fairness
Microsoft's responsible AI principle of fairness means AI systems should treat people fairly and avoid unfair bias across demographic groups.
Encrypting sensitive customer data and restricting system access to authorized personnel: Privacy and security The privacy and security principle requires AI systems to protect personal and sensitive data and prevent unauthorized access.
Informing users when they are interacting with an AI system and explaining the system's capabilities and limitations: Transparency The transparency principle requires that people understand when they are interacting with AI and understand the system's purpose, capabilities, and limitations.
Testing AI systems under different conditions to reduce unexpected failures: Reliability and safety The reliability and safety principle means AI systems should operate reliably and safely, including through testing and validation under expected and unexpected conditions.
NEW QUESTION # 13
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Statement 1: Generating a response to a user prompt occurs during the inference stage. = Yes When a deployed generative AI model receives a user prompt and generates an output, that process is called inference . The model is being used to produce a response, not being trained.
Statement 2: A generative AI model generates responses by copying stored documents directly from the model's training data. = No Generative AI models do not normally respond by simply copying stored documents from training data. They generate outputs based on learned statistical patterns. In some cases, models may reproduce memorized content, but that is not the normal or intended generation mechanism.
Statement 3: A generative AI model produces output by predicting the next token based on patterns learned from the model's training data. = Yes This is correct. Large language models generate text by predicting likely next tokens based on the prompt and learned patterns from training.
NEW QUESTION # 14
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: perform consistently under expected conditions
Reliability and safety is the specific Microsoft Responsible AI principle defined by the requirement that AI systems must operate reliably, safely, and consistently under expected conditions.
Reference:
https://learn.microsoft.com/en-us/agents/design-guidelines/responsible-ai
NEW QUESTION # 15
You deploy the Azure OpenAI service to generate images.
You need to ensure that the service provides the highest level of protection against harmful content.
What should you do?
Answer: A
Explanation:
Azure OpenAI, Content filtering
Azure OpenAI includes a content filtering system that works alongside core models, including image generation models. This system works by running both the prompt and completion through a set of classification models designed to detect and prevent the output of harmful content.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/content-filter
NEW QUESTION # 16
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
When content is submitted to Azure Content Understanding in Foundry Tools, the analysis is asynchronous.
This means the service does not return results immediately within the same HTTP request. Instead, it uses the standard Azure long-running operation (LRO) pattern - you call begin_analyze() to submit the content, which immediately returns a poller object, and then call poller.result() to wait for processing to complete and retrieve the structured extraction results.
Why the other options are wrong:
Synchronous is incorrect - the analysis pipeline involves multiple AI steps (OCR, speech transcription, schema mapping) that take time; a blocking synchronous call is not supported.
Returned only as unstructured plain text is incorrect - Azure Content Understanding returns richly structured JSON output with named fields mapped to your defined schema, not plain unstructured text.
Limited to OCR-only processing is incorrect - Content Understanding goes far beyond OCR; it supports document, audio, image, and video analyzers, and performs semantic field extraction using AI, not just character recognition.
This asynchronous design is consistent across all Azure AI services that perform complex, multi-step content processing.
NEW QUESTION # 17
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