Question 1

An enterprise wants to scale AI usage beyond pilots and has been advised to create an AI champions program. Leadership is debating what the program should look like.Which design best reflects how an effective AI champions program should operate?

Options :

A : Ask IT to nominate a few admins as “champions” whose main task is to approve access requests; do not give them time for coaching or peer engagement so they can stay focused on tickets.

B : Select only senior executives as champions so messages come from the top, and limit their involvement to recording occasional video messages about the importance of AI.

C : Outsource the champions program entirely to the vendor’s customer success team, who will run webinars but have no visibility into the organisation’s culture, policies, or specific use cases.

D : Recruit volunteer champions from across business units, give them training, time, and community support, and empower them to model usage, coach peers, and feed insights back to the central adoption and governance teams.

Answer: D

Question 2

Fabrikam is implementing a retrieval-augmented generation (RAG) pattern for a “Policy Advisor Copilot.” They want a search service that can:Index PDFs, knowledge articles, tickets, and emailsSupport full-text, semantic, and vector/hybrid searchExpose an API that their copilot calls to retrieve relevant chunks before generating answersFill in the blank:The Azure service that best provides this search and retrieval capability is __________.

Options :

A : Azure AI Vision

B : Azure AI Search

C : Azure AI Foundry

D : Azure AI Translator

Answer: B

Question 3

An AI transformation lead explains to executives:“Machine learning is most valuable when we have enough historical data and our problem involves patterns or predictions that are too complex or dynamic for a small set of hand-written rules.”Is this statement correct?

Options :

A : True

B : False

Answer: A

Question 4

Tailwind Retail wants a model to recommend promotions. They have:Transaction tables with product, price, time, and storeCustomer attributes (age band, loyalty tier, region)Free-text notes from call centre agents about complaints and preferencesWhich approach best uses data types to improve the model?

Options :

A : Treat all fields as raw text in one long string and train a language model without distinguishing between structured and unstructured data.

B : Use structured features from tables for the core model, and derive additional signals from free-text notes (for example, topics or sentiment) that can be added as engineered features.

C : Ignore structured data entirely and train only on free-text fields, because language models always outperform tabular models when enough text is available.

D : Drop all free-text fields since they are hard to model; relying on numeric and categorical features guarantees better performance in every scenario.

Answer: B

Question 5

Fabrikam Health is building a “Ward Rounds Copilot” for clinicians.RequirementsR1: Extract text from photos of whiteboard notes, patient labels, and printed ward lists taken on a tabletR2: Allow doctors to ask questions over clinical guidelines, local policies, and anonymised case summaries using natural-language and semantically relevant resultsR3: Provide a unified environment where the digital team can connect models and tools, configure the copilot, and manage lifecycle, security, and monitoringProposed solutionUse Azure AI Vision to perform OCR and image analysis on the photos from the wardUse Azure AI Search to index guidelines, policies, and case summaries and to provide semantic retrieval for the copilotUse Azure AI Foundry as the workspace where the team wires together models, tools (including Vision and Search), and lifecycle policies for the solutionDoes the proposed solution meet the requirements?

Options :

A : Yes

B : No

Answer: A