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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identify AI concepts and responsibilities | 40-45% | - Identify AI concepts and features
|
| Topic 2: Implement AI solutions by using Microsoft Foundry | 55-60% | - Work with models in Microsoft Foundry
|
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NEW QUESTION # 93
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:
NEW QUESTION # 94
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, a multimodal generative AI model is required to process prompts that contain images. These models can understand and combine different types of data, such as text and visual inputs, at the same time.
Box 2: Yes
Yes, you can use both methods to provide an image in the portal.
In the Microsoft Foundry chat playground, when interacting with vision-enabled models (like gpt-
4o or gpt-4o-mini), the interface allows you to upload local image files directly or paste a publicly accessible URL endpoint.
Box 3: Yes
A multimodal model can interpret multiple images in a single prompt. Most modern vision- language models accept several image inputs at once for tasks like comparison or sequential analysis.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/gpt-with-vision
NEW QUESTION # 95
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 correct text analysis technique is Named Entity Recognition (NER).
Microsoft defines NER as a feature that identifies and categorizes entities in unstructured text, including people, places, and organizations.
Sentiment analysis detects positive, negative, or neutral opinion. Summarization creates shorter versions of text. Key phrase extraction identifies important phrases, but it does not specifically classify mentions as people, organizations, or locations.
NEW QUESTION # 96
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The correct answer is classify email messages as work-related or personal. Natural language processing (NLP) is concerned with analyzing, interpreting, and processing human language, including unstructured text contained in documents, messages, reviews, and emails. Text classification is therefore a direct NLP workload: an AI model analyzes the linguistic content of an email and assigns it to a predefined category such as work-related or personal.
Microsoft ' s current AI-901 study guide explicitly requires candidates to identify scenarios for common AI workloads, including text analysis, and to understand common text-analysis techniques. Microsoft Azure Language in Foundry Tools similarly supports capabilities for extracting information from text and classifying text, demonstrating that categorization based on textual content is an NLP function.
Predicting future car rentals is primarily a regression/forecasting problem because the output is a numerical quantity. Predicting whether a website visitor will transact is typically a binary classification machine- learning problem based on behavioral or transactional features, not necessarily language. Stopping industrial machinery when a temperature threshold is exceeded is a rule-based monitoring/control scenario.
NEW QUESTION # 97
You are authoring a Conversational Language Understanding application to support a music festival.
You want users to be able to ask questions about scheduled shows, such as: "Which act is playing on the main stage?" The question "Which act is playing on the main stage?" is an example of which type of element?
Answer: B
NEW QUESTION # 98
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