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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
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Answer:
Explanation:
Explanation:
The correct selections are Yes, No, Yes. Prompt engineering is the practice of designing clear and effective inputs that guide a generative AI model toward relevant, accurate, and useful output. Guidelines, constraints, role instructions, tone requirements, and output-format rules are all common prompt-engineering techniques, so statement A is true. Statement B is false because examples are normally considered examples or few-shot demonstrations, not the instruction itself. The instruction tells the model what task to perform, while examples demonstrate the preferred pattern or output style. Statement C is true because context supplies the background information, source material, business scenario, audience, or constraints the model should use when generating the answer. Strong prompts usually combine instruction, context, constraints, and examples.
NEW QUESTION # 95
Which practice best demonstrates operational governance when implementing AI solutions?
Answer: B
Explanation:
Monitoring AI outputs for risk and compliance issues helps organizations identify bias, harmful content, privacy violations, and regulatory risks in real time. This ongoing oversight ensures AI systems remain aligned with governance policies, legal requirements, and responsible AI standards throughout their operational lifecycle.
Reference:
https://learn.microsoft.com/en-us/training/modules/embrace-responsible-ai-principles-practices/3- identify-guiding-principles-responsible-ai
NEW QUESTION # 96
- 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:
Answer Area
* Azure Vision in Foundry Tools can extract and analyze key phrases from PDF files. Answer: No
* Azure Vision in Foundry Tools can generate images based on natural language descriptions. Answer:
No
* Azure Document Intelligence in Foundry Tools can be used to automate the processing of invoices and credit notes. Answer: Yes
* No - Azure Vision in Foundry Tools focuses on computer vision tasks such as image analysis and OCR (reading text from images and documents). While it can extract text from scanned PDFs via OCR, key phrase extraction is a natural language processing capability provided by Azure Language in Foundry Tools , not Azure Vision. Key phrase extraction analyzes text to identify main concepts, which is a different service family than vision.
* No - Azure Vision can analyze existing images (for example, generate captions/descriptions of an image), but generating new images from a text prompt is a generative model capability (for example, DALL E through Azure OpenAI/Azure AI Foundry model endpoints), not an Azure Vision feature.
Vision describes what it "sees"; it doesn't synthesize new images from natural language.
* Yes - Azure Document Intelligence in Foundry Tools is designed for intelligent document processing
, including automating extraction of structured fields from financial documents. Microsoft provides prebuilt models for invoices and supports custom extraction for similar document types, which makes it suitable for automating workflows involving invoices and credit-note style documents (field extraction, validation, routing).
NEW QUESTION # 97
An organisation is developing an AI system that will recommend candidates for internal promotions. During testing, the system produces accurate recommendations for most departments but consistently underperforms for the engineering department, where the workforce is more demographically diverse than other departments.
Which responsible AI standards are most directly relevant, and what action should the organisation take?
Answer: B
Explanation:
Eight responsible AI standards: fairness, reliability, safety, privacy, security, inclusiveness, transparency, and accountability. In this scenario, three standards are most directly relevant.
Fairness requires that the AI system treats all people equitably, meaning it must not disadvantage candidates from the more diverse engineering department. Reliability requires that the system performs consistently across all departments, not just most of them. Inclusiveness requires that the system benefits everyone, including diverse populations. The correct action addresses all three: audit training data for representation gaps that could cause bias, test across demographic groups, and ensure equitable performance before deployment.
NEW QUESTION # 98
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