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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify Azure AI services for NLP - Describe Azure capabilities for NLP - Identify common NLP tasks |
| Topic 2: Describe features of Generative AI workloads on Azure | 15-20% | - Describe generative AI concepts - Describe Azure OpenAI Service capabilities - Identify responsible AI considerations for generative AI |
| Topic 3: Describe fundamental principles of machine learning on Azure | 30-35% | - Describe Azure Machine Learning capabilities - Describe core machine learning concepts - Describe features of no-code automated ML - Identify common machine learning tasks |
| Topic 4: Describe features of computer vision workloads on Azure | 15-20% | - Describe Azure capabilities for computer vision - Identify common computer vision tasks - Identify Azure AI services for computer vision |
| Topic 5: Describe AI workloads and considerations | 15-20% | - Identify features of common AI workloads - Identify guiding principles for responsible AI |
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NEW QUESTION # 284
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 285
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:
The Azure AI Language Service includes several natural language processing features, such as question answering, language understanding, entity recognition, sentiment analysis, and more. Each feature serves a distinct purpose, and understanding their differences is key to selecting the correct AI workload.
* "You can use Azure AI Language Service's question answering to query an Azure SQL database." - NOThe question answering feature is designed to retrieve answers from text-based knowledge sources (for example, FAQs, documents, or website content). It cannot directly query a database such as Azure SQL. Querying databases requires Azure Cognitive Search, Azure OpenAI, or custom integration using application logic, not the question answering model.
* "You should use Azure AI Language Service's question answering when you want a knowledge base to provide the same answer to different users who submit similar questions." - YESThis is the primary use case of question answering. It allows developers to build a knowledge base (KB) of predefined question-answer pairs or extract answers from documents. When users submit semantically similar questions (e.g., "What are your office hours?" or "When are you open?"), the service returns the same consistent answer.
* "Azure AI Language Service's question answering can determine the intent of a user utterance." - NODetermining user intent is part of the Language Understanding (LUIS) capability, not question answering. LUIS models map natural language inputs to intents and entities, typically used in bots or applications that execute tasks (like booking a meeting or checking weather).
Hence, correct answers are: No, Yes, No - aligning with the AI-900 official study guide and Microsoft Learn module "Identify Azure AI Language capabilities."
NEW QUESTION # 286
Which two scenarios are examples of a conversational AI workload? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: B,D
Explanation:
Explanation
B: A bot is an automated software program designed to perform a particular task. Think of it as a robot without a body.
C: Automated customer interaction is essential to a business of any size. In fact, 61% of consumers prefer to communicate via speech, and most of them prefer self-service. Because customer satisfaction is a priority for all businesses, self-service is a critical facet of any customer-facing communications strategy.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/ai-overview
https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/interactive-voice-response-bot
NEW QUESTION # 287
Which statement is an example of a Microsoft responsible AJ principle?
Answer: C
Explanation:
The correct answer is C. AI systems must be understandable, which corresponds to the Transparency principle of Microsoft's Responsible AI framework.
According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Identify guiding principles for responsible AI", Microsoft defines six key principles for responsible AI:
* Fairness - AI systems should treat everyone equitably.
* Reliability and safety - AI should function as intended, even under unexpected conditions.
* Privacy and security - AI must protect personal and business data.
* Inclusiveness - AI should empower everyone and engage diverse users.
* Transparency - AI systems should be understandable.
* Accountability - People should be accountable for AI systems.
The statement "AI systems must be understandable" aligns directly with the Transparency principle, ensuring that AI decisions and behaviors can be explained and interpreted by developers, users, and stakeholders.
Microsoft emphasizes that transparent AI builds trust, allows debugging, and ensures ethical usage.
Other options are incorrect:
* A. Use only publicly available data - Not a principle of Responsible AI.
* B. Protect the interests of the company - Focused on business goals, not ethical AI.
* D. Keep personal details public - Violates the Privacy and Security principle.
# Final Answer (Q179): C. AI systems must be understandable.
NEW QUESTION # 288
You need to scan the news for articles about your customers and alert employees when there is a negative article. Positive articles must be added to a press book.
Which natural language processing tasks should you use to complete the process? To answer, drag the appropriate tasks to the correct locations. 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 selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Entity recognition
the Named Entity Recognition module in Machine Learning Studio (classic), to identify the names of things, such as people, companies, or locations in a column of text.
Named entity recognition is an important area of research in machine learning and natural language processing (NLP), because it can be used to answer many real-world questions, such as:
* Which companies were mentioned in a news article?
* Does a tweet contain the name of a person? Does the tweet also provide his current location?
* Were specified products mentioned in complaints or reviews?
Box 2: Sentiment Analysis
The Text Analytics API's Sentiment Analysis feature provides two ways for detecting positive and negative sentiment. If you send a Sentiment Analysis request, the API will return sentiment labels (such as "negative",
"neutral" and "positive") and confidence scores at the sentence and document-level.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/named-entity-recognition
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-sentimen
NEW QUESTION # 289
......
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