Question 1

Contoso Manufacturing’s transformation office is preparing a board pack on “Supplier Resilience.”ScenarioThey need to:Pull together a narrative that combines web-based supply chain news, internal risk assessments, and key supplier meeting notesFor a shortlist of high-risk suppliers, deep-dive into attached Excel files (spend, on-time delivery, defect rates) to explain the main drivers and trends by region and categoryThey want to use Researcher and Analyst in Microsoft 365 Copilot.Which approach best aligns with how Researcher and Analyst are intended to be used?

Options :

A : Use Analyst for both the narrative and the data deep dive to minimise training on multiple agents

B : Use Researcher to analyse the Excel files and Analyst to write the cross-source narrative for the board pack

C : Use Researcher to create the supplier resilience narrative, then use Analyst to explore and visualise the Excel datasets

D : Use only standard Copilot Chat, because Researcher and Analyst are mainly for technical users

Answer: C

Question 2

Contoso’s leadership wants to use generative AI to boost everyday productivity: drafting emails, summarizing meetings, and improving documents in Word, Excel, PowerPoint, Outlook, and Teams.They have no urgent requirement to integrate external line-of-business systems yet, but want to show clear value in 3–6 months without a large engineering project.Which approach should they take first?

Options :

A : Build a custom AI app on Azure OpenAI and roll it out separately from Microsoft 365

B : Roll out Microsoft 365 Copilot out of the box, then plan extensibility in a later phase

C : Immediately build custom agents and connectors before enabling any Copilot features

D : Block Copilot and wait until all business systems are modeled as custom agents

Answer: B

Question 3

Fabrikam has rolled out an Azure AI vision solution that consistently processes about 8 million images every month for quality inspection. Volumes are stable and expected to grow slowly. Finance wants predictable monthly spend and discounts compared to pure pay-as-you-go.Which subscription/pricing approach best aligns with this requirement?

Options :

A : Keep everything on pay-as-you-go even though usage is highly predictable

B : Use commitment tiers or provisioned throughput plans for the relevant Azure AI services

C : Move all workloads to spot VMs so processing stops when capacity is reclaimed

D : Disable all cost alerts and just reconcile spend at year-end

Answer: B

Question 4

Tailwind Traders has field engineers who scribble notes on whiteboards or paper checklists onsite. They want a fast way, from their phones, to pull those notes into Microsoft 365 Copilot Chat so Copilot can summarise them or turn them into emails or work items.To bring physical content into Copilot Chat from a phone, the engineers should rely on the mobile app’s ______ capability.

Options :

A : Live GPS tracking

B : Device compliance checks

C : Scan and text extraction (Lens/OCR)

D : Offline-only local chatbot mode

Answer: C

Question 5

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

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