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NEW QUESTION # 59
Hotspot Question
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:
Box 1: Yes
Yes, an agent must be published as an Agent Application before external production applications can send requests to it through a stable, production-ready endpoint.
While you can write code, build, and test an agent inside a shared development workspace (such as an Azure AI Foundry project), it is not designed for broad external distribution in that state.
Box 2: No
No, you do not need to fine-tune a model before you can publish an AI agent in the Microsoft Foundry portal.
When building an agentic workflow in Microsoft Foundry Agent Service, you can attach a standard, out-of-the-box foundation model deployment (such as GPT-4o or gpt-4o-mini) to your agent definition. Fine-tuning is entirely optional and is reserved for specific performance optimization.
Box 3: No
In the Microsoft Foundry portal, an agent is not published automatically when you save its configuration; saving only updates your draft, and you must explicitly select the publish option to make it live.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/agent-applications
https://itnext.io/how-microsoft-foundry-makes-ai-agents-unstoppable-with-fine-tuning-and-rft-5abeeb55f662
NEW QUESTION # 60
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Ongoing monitoring, feedback, and evaluation are goals of the Reliability & Safety principle.
The Microsoft responsible AI principle of Reliability and safety focuses on ensuring that AI systems operate consistently, safely, and as intended across different conditions. This includes testing, monitoring, collecting feedback, and evaluating system performance over time.
Why the other options are incorrect:
Human oversight and control aligns more closely with accountability.
Data governance and management aligns more closely with privacy and security.
The minimization of stereotyping, demeaning, and erasing outputs aligns more closely with fairness and inclusiveness.
NEW QUESTION # 61
For each of the following statements, select if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Statement
Answer
Image analysis capabilities in Microsoft Foundry Tools can perform optical character recognition (OCR).
Yes
Image analysis capabilities in Microsoft Foundry Tools can generate captions and descriptive tags for images.
Yes
Image analysis capabilities in Microsoft Foundry Tools are designed primarily to create new images from text prompts.
No
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Fundamentals/Course Guide
/topics:
The correct sequence is Yes, Yes, No .
The first statement is Yes . Azure Vision Image Analysis in Microsoft Foundry Tools supports optical character recognition (OCR) . Microsoft documents Image Analysis 4.0 as supporting synchronous OCR through its Read capability, which extracts printed or handwritten text contained within images.
The second statement is also Yes . Image Analysis provides both captioning and image tagging . Captions generate a human-readable description of image content, while tags identify recognizable objects, scenes, actions, and other visual concepts. Microsoft explicitly lists capabilities such as Captions, Dense Captions, Tags, Object Detection, People detection, and Read/OCR as Image Analysis features.
The third statement is No . Image Analysis is primarily concerned with extracting information from existing images , not generating new images. Creating an image from a textual prompt is a generative AI/image- generation workload, typically performed by image-generation models. Image Analysis instead interprets visual input by extracting text, captions, tags, objects, and related information.
NEW QUESTION # 62
Your company processes customer support emails.
You need to implement an AI solution that automatically identifies mentions of people, organizations, and locations in the emails.
Which text analysis technique should you use?
Answer: A
Explanation:
The adequate text analysis feature is Named Entity Recognition (NER), which is a core prebuilt capability of the Azure AI Language service.
Prebuilt Categorization: The prebuilt NER feature automatically parses unstructured text (like emails) to identify and group elements into standard classes, including Person, Organization, and Location.
No Training Required: Because it relies on state-of-the-art pretrained transformer models, it can be deployed immediately out-of-the-box without requiring data labeling or custom model training.
High Extensibility: If your emails contain highly specialized terms (such as proprietary product IDs or industry-specific roles), you can easily scale to Custom NER within the same architecture.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/language-service/named-entity-recognition/overview
NEW QUESTION # 63
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:
No
Yes
No
Comprehensive and Detailed Explanation with all Azure AI documents: =
Statement 1: Speech synthesis converts spoken audio into written text. = No Speech synthesis, also called text to speech, converts written text into spoken audio. Converting spoken audio into written text is speech recognition or speech to text.
Statement 2: Speech recognition can detect and transcribe spoken input. = Yes Speech recognition is used to identify spoken language in audio and convert it into text.
Statement 3: Text to speech in Azure Speech in Foundry Tools can translate text input into audible speech in a different language. = No Text to speech converts text into spoken audio. Translation into another language is a separate translation capability, not standard text-to-speech synthesis.
NEW QUESTION # 64
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