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NEW QUESTION # 38
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 # 39
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:
* A text-to-image generator can be used to translate content into other languages. Answer: No
* A predictive analytics model can generate new marketing content for a company's online ads. Answer:
No
* A generative AI chatbot can engage customers in personalized conversations and recommend products.
The answer: Yes
* No - A text-to-image generator's primary function is to create images from text prompts , not translate text between languages. Translation is a natural language processing task typically handled by language models or dedicated translation services. A text-to-image model could illustrate translated content (e.g., generate an image based on a translated prompt), but it is not the tool used to perform the translation itself.
* No - Predictive analytics models are designed to predict outcomes (forecasts, probabilities, classifications) from historical patterns, such as predicting click-through rate, churn, or next-quarter demand. They are not designed to create new ad copy or marketing creatives. Generating new marketing content is a generative AI capability (text generation), not predictive analytics.
* Yes - A generative AI chatbot is well-suited to interactive, natural-language conversations . With access to product catalogs and business rules, it can ask clarifying questions, tailor responses to customer needs, and recommend products (for example, suggesting tents based on group size, season, and budget). This combines conversational generation with retrieval/recommendation logic behind the scenes, enabling personalized customer engagement at scale.
NEW QUESTION # 40
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: No
No - A generative AI model guarantees factually accurate responses if the model is trained on a large dataset.
A large training dataset does not guarantee that a generative AI model will provide factually accurate responses. While larger, diverse datasets generally improve performance and reduce certain types of errors, they do not eliminate the fundamental tendency of these models to generate incorrect information, known as "hallucinations".
Box 2: Yes
Yes - Content filtering and responsible AI safeguards help a generative AI model generate safe an inoffensive content.
Content filtering and responsible AI safeguards (e.g., in Azure AI Foundry or Amazon Bedrock ) act as essential, multi-layered, reactive mechanisms-covering both input and output-to detect and block harmful, illegal, or biased content. These systems use automated classifiers to, for example, filter for hate speech, sexual content, violence, and self-harm. They ensure safety by analyzing prompts and generating responses, often allowing for custom thresholds, to prevent models from generating unsafe or inappropriate output.
Box 3: No
No - A generative AI model always produce fair and unbiased results when the training data has been properly prepared and reviewed for fairness.
Even with perfectly prepared and reviewed training data, generative AI models can still produce biased results. While high-quality data is foundational, bias is a persistent challenge that can emerge from multiple sources throughout the AI lifecycle.
Reference:
https://mehmetozkaya.medium.com/limitations-of-large-language-models-llms-1790a14010db
https://monowar-mukul.medium.com/keeping-your-ai-safe-content-filters-in-azure-ai-foundry-
9a87c8447e11
https://www.sap.com/resources/what-is-ai-bias
NEW QUESTION # 41
Hotspot Question
What should you use for each task? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Azure Document Intelligence in Foundry Tools
Extracting structured-data forms and invoices.
Azure Document Intelligence (formerly known as Azure Form Recognizer) within the Foundry Tools ecosystem can be used to extract structured data, including key-value pairs, tables, and specific fields from forms and invoices. It is designed to transform unstructured or semi-structured data from PDFs, images, and other files into actionable, structured JSON output.
Box 2: Azure Language in Foundry Tools
Summarizing written content from business reports.
Azure Language in Foundry Tools (formerly Azure AI Language) includes specific features for summarizing written content, including business reports, in both extractive and abstractive formats.
Key Summarization Capabilities
Native Document Summarization: This feature can directly parse and summarize files in their original formats, such as PDF, Word (DOCX), and plain text.
Summarization Approaches:
-Extractive: Selects the most important original sentences from the document to create a summary.
-Abstractive: Generates new, concise sentences that capture the main idea without directly copying the source text.
Powered by Advanced Models: The service utilizes Large Language Models (LLMs) and Small Language Models (SLMs), such as GPT-4o and Phi-3.5-mini, to provide high-quality, low-latency results.
Box 3: Azure Vision in Foundry Tools
Generate descriptive text for uploaded images.
Azure Vision in Foundry Tools (part of Azure AI Services within the Foundry ecosystem) can be used to analyze uploaded images and automatically generate descriptive, human-readable text.
This capability is part of the Image Analysis feature, which generates English-language captions describing the content of an image.
Key aspects of this functionality include:
*-> Descriptive Captions: The service generates complete sentences based on objects identified in the image, providing multiple options ordered by a confidence score.
*-> Image Tagging: It can generate a list of words (tags) identifying objects, beings, scenery, or actions.
Reference:
https://azure.microsoft.com/en-us/products/ai-foundry/tools/document-intelligence
https://azure.microsoft.com/en-us/products/ai-foundry/tools/vision
NEW QUESTION # 42
You are exploring how Microsoft 365 Copilot uses Microsoft Graph to deliver AI-powered experiences.
Which information in Microsoft Graph can Copilot use by default?
Answer: B
NEW QUESTION # 43
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