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NEW QUESTION # 29
- 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
* Prompt engineering changes how a generative AI model was trained. Answer: No
* Prompt engineering focuses on designing clear, concise, and context-rich instructions. Answer: Yes
* Effective prompt engineering involves maximizing the number of tokens used in each request. Answer:
No
* No - Prompt engineering does not modify model weights or retrain the model. It is an inference-time technique : you steer outputs by improving the instructions and context you send to the model.
Changing how a model was trained would involve pretraining, fine-tuning, or other training methods- separate from prompt engineering.
* Yes - The primary goal of prompt engineering is to reduce ambiguity and variability by providing clear instructions , the right context , and explicit output constraints . This often includes specifying role and task, providing necessary facts or grounding text, defining format (bullets, JSON, headings), and adding examples (few-shot) when helpful. Well-constructed prompts improve consistency, relevance, and usefulness of outputs.
* No - Good prompt engineering does not mean "use as many tokens as possible." In fact, using unnecessary tokens can increase cost and may degrade quality by adding noise. Effective prompts are typically as short as possible but as long as necessary : enough context to achieve accuracy and alignment, but not bloated. Token discipline matters because most pricing is token-based and long contexts can dilute attention over what's most important.
NEW QUESTION # 30
Your company stores hundreds of internal business reports.
You need to recommend a generative AI solution that uses an agent to answer questions based on the content in the reports.
What should you include in the recommendation?
Answer: A
Explanation:
The correct answer is B. Retrieval Augmented Generation (RAG). The requirement is to answer questions based on the content of internal business reports. RAG is the correct architecture because it retrieves relevant information from trusted internal documents at runtime and supplies that information as grounding context to the generative AI model. This allows the agent to generate answers that are more accurate, current, and relevant to the organization's own content without retraining the model. Azure Machine Learning is mainly used for training and deploying predictive models. A Generative Adversarial Network is typically associated with synthetic content generation, especially images, and is not the right fit for document-grounded Q & A.
Natural language processing is too broad; RAG is the specific recommended pattern.
NEW QUESTION # 31
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 # 32
Your company is evaluating the use of Microsoft Copilot Studio to support business process automation and employee self-service.
Which two capabilities are directly supported in Copilot Studio? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: B,C
Explanation:
Customizing agent behavior and responses - A core function of Copilot Studio. You define topics, instructions, conversational flows, tone, and responses to shape exactly how the agent behaves.
Building agents that connect to business data and automate user interactions - The central purpose of Copilot Studio: a low-code platform for building custom agents that connect to data sources (via connectors), trigger Power Automate flows, and automate self-service interactions for employees.
NEW QUESTION # 33
Your company sells hiking and camping gear online.
You need a generative AI solution that can interact with customers, ask questions about their needs, and escalate to a human agent if necessary.
What solution should you use?
Answer: B
Explanation:
Generative AI chatbots function as a primary point of contact in modern customer service, using natural language processing (NLP) and large language models (LLMs) to engage in human-like dialogue rather than following rigid, pre-programmed scripts.
Reference:
https://cobbai.com/blog/ai-chatbot-for-customer-service
NEW QUESTION # 34
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