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NEW QUESTION # 117
You are developing an application that uses the Azure Language SDK and a Microsoft Foundry resource.
You need to return a list of important topics from unstructured text.
Which Python method should you use?
Answer: A
Explanation:
To return important topics from unstructured text by using Azure Language SDK, use:
client.extract_key_phrases()
Microsoft states that key phrase extraction evaluates unstructured text and returns a list of key phrases or main topics. The Azure Text Analytics client library for Python includes the extract_key_phrases method for this task.
Why the other options are incorrect:
A). client.recognize_linked_entities() identifies entities and links them to a knowledge base.
B). client.recognize_entities() identifies named entities such as people, organizations, locations, dates, and quantities.
C). client.begin_extract_summary() starts an extractive summarization operation, not key phrase extraction.
NEW QUESTION # 118
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: natural language processing
The interactive answering of questions entered by a user as part of an application is a direct example of natural language processing (NLP).
How NLP Powers Interactive Q&A
Text interpretation: The system uses linguistic rules and machine learning models to read and interpret the user's typed input.
Intent recognition: It figures out what the user wants or needs by analyzing the meaning behind the words.
Response generation: It formulates and delivers a human-like reply back through the application interface.
Reference:
https://www.gauthmath.com/solution/1800845210023942/The-interactive-answering-of-questions-entered-by-a-user-as-part-of-an-applicati
NEW QUESTION # 119
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: transcription
Azure Content Understanding in Foundry Tools uses transcription preprocessing to convert spoken words from audio and video files into text-based transcripts.
Optimized Preprocessing: Transcription runs as an initial extraction step to provide rich textual context before passing data to generative AI models.
Transcript Formatting: Conversational audio is turned into searchable WebVTT transcripts containing word-level or sentence-level timestamp.
Speaker Separation: Features like diarization identify and distinguish different speakers, while speaker role detection can pinpoint specific roles (such as agent and customer).
Multilingual Handling: It supports automatic language identification and multi-language phrase transcription.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/video/overview
NEW QUESTION # 120
You need to compare the costs of large language models (LLMs) for a generative AI solution.
What should you use in the Microsoft Foundry portal?
Answer: A
Explanation:
To compare the costs of large language models in Microsoft Foundry portal, use the Model leaderboard.
Microsoft documentation states that the model leaderboard helps compare models across quality, safety, estimated cost, and throughput. It also supports trade-off charts and side-by-side model comparison for features, performance, and estimated cost.
NEW QUESTION # 121
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
Answer
Generative AI models predict the next token in a sequence based on patterns learned during training.
Yes
A system prompt can be used to influence the behavior and tone of the responses generated by a generative AI model.
Yes
Increasing the Temperature parameter makes the responses generated by generative AI models more deterministic and consistent.
No
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Fundamentals/Course Guide
/topics:
The first statement is Yes. Generative language models generate text autoregressively by estimating probabilities for the next token based on preceding context and patterns learned during model training. This next-token prediction mechanism is fundamental to how transformer-based generative models produce sequences of text.
The second statement is also Yes. A system prompt provides high-level instructions that establish how the model should behave. It can define role, response style, tone, constraints, and other behavioral expectations.
The AI-901 study guide explicitly includes creating effective system and user prompts as part of implementing generative AI applications in Microsoft Foundry.
The third statement is No. Increasing Temperature does the opposite of making output more deterministic.
Microsoft documents that higher temperature values increase randomness and output diversity, while lower values make responses more focused and predictable. At a temperature approaching zero, model selection becomes substantially more deterministic because higher-probability tokens are favored more strongly.
NEW QUESTION # 122
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