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NEW QUESTION # 64
Your company uses a non-reasoning generative AI model to create textual content. You discover that the model's responses are inconsistent and do NOT meet expectations. You need to improve the prompts. What should you do? More than one answer choice may achieve the goal. Select the BEST answer.
Answer: C,D
Explanation:
When a non-reasoning generative AI model produces inconsistent outputs, the most reliable improvement is to make the prompt more specific, constrained, and demonstrative of what "good" looks like.
A is correct because adding high-quality examples is a form of few-shot prompting. Examples act like
"training wheels" at inference time: they show the model the desired structure, tone, level of detail, formatting rules, and boundaries. This reduces ambiguity and variance, especially for tasks like marketing copy, summaries, policy text, or customer replies. The more your examples resemble real target outputs (including edge cases), the more consistent the model's completions become.
B is correct because adding context, relevant source material, and explicit expectations narrows the model's degrees of freedom. Including the intended audience, purpose, constraints (length, voice, banned claims), and trusted reference content (approved facts, product specs, policy excerpts) helps the model stay aligned and reduces hallucinations and off-brand language. This is also where you specify acceptance criteria such as
"must include 3 bullet points," "use UK English," or "cite only provided text." C is not best: technical jargon can confuse or bias output if it's not aligned to the task; clarity beats jargon. D is not best: a single concise requirement is usually under-specified and often increases variability.
NEW QUESTION # 65
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
A text-to-image generator (like DALL-E or Midjourney) takes textual descriptions and synthesizes them into visual imagery. It does not perform language translation workflows; that task requires natural language processing (NLP) or large language models (LLMs).
Box 2: No
Predictive analytics models rely on historical data and machine learning algorithms to forecast future trends, behaviors, or numerical outcomes (such as click-through rates or future sales).
They do not create or synthesize brand-new creative artifacts like marketing text or ad copies; that is the role of Generative AI.
Box 3: Yes
Generative AI chatbots (like those built using custom LLMs or Copilot Studio) excel at maintaining dynamic, human-like context. They can handle highly personalized conversations based on user profile inputs and dynamically synthesize relevant product recommendations.
NEW QUESTION # 66
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 # 67
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 barrier to AI adoption can include data limitations and lock of AI readiness and skills.
Those are two of the most significant hurdles organizations face today. In fact, a 2025 PEX Report found that 52% of professionals cite data quality and availability as their primary challenge, closely followed by a lack of internal expertise at 49%.
Box 2: Yes
Yes - Organizations often struggle with AI adoptions because they prioritize technology selection before defining clear business use cases.
It is essentially the "hammer looking for a nail" problem; organizations often get dazzled by the
"hammer" (AI) and start swinging before they even know what they are trying to build.
Starting with technology rather than business value is a primary reason why 88% to 95% of AI pilots fail to deliver measurable results. This "technology-first" trap often leads to "random acts of AI"-expensive experiments that succeed in a lab but fail to solve any real operational bottlenecks.
Box 3: Yes
Yes - A lack of cross-functional collaboration is a common barrier to AI adoption.
That is spot on-it's often the "human" silos, not the hardware, that stall AI progress. While technical hurdles like data quality are significant, research consistently identifies a lack of cross- functional collaboration as a primary "hidden" barrier to successful AI adoption.
Reference:
https://www.ibm.com/think/insights/ai-adoption-challenges
https://www.rapidops.com/blog/why-ai-fails/
https://www.gartner.com/peer-community/post/preferred-tactics-building-effective-collaboration- cross-functional-teams-involved-ai-governance-risk-management-e-g-joint
NEW QUESTION # 68
Your company is deploying Microsoft 365 Copilot. The deployment must provide users with access to the Researcher agent to search across data in Microsoft SharePoint.
You need to recommend a licensing plan for the solution.
What should you recommend?
Answer: D
Explanation:
To use the Researcher agent for searching across Microsoft SharePoint data, you need the Microsoft 365 Copilot add-on license.
The Researcher agent is a first-party reasoning agent included in the core experience for users with a paid Copilot license. It is designed for multi-step, in-depth research grounded in your Microsoft Graph data, including SharePoint, OneDrive, emails, and Teams.
Required Licensing Components
To deploy this capability, your organization must have:
1. A Qualifying Base License:
Enterprise: Microsoft 365 E3 or E5, or Office 365 E3 or E5.
Business: Microsoft 365 Business Standard or Business Premium.
*-> 2. Microsoft 365 Copilot Add-on:
This paid license (typically ~$30/user/month) unlocks the Researcher agent and the ability to search internal tenant data like SharePoint.
Reference:
https://learn.microsoft.com/en-us/copilot/microsoft-365/faq-researcher
NEW QUESTION # 69
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