Courseiva

AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations

What is 'supply chain optimisation' as an AI workload?

⚠ Common exam trap

A common mix-up: candidates confuse adjacent AI workloads (e.g., contract analysis, document processing, or HR analytics) with the core logistics-focused definition of supply chain optimisation, which specifically involves demand forecasting, route planning, and inventory control.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Using AI for demand forecasting, route optimisation, and inventory management across the supply chain

Supply chain optimisation as an AI workload involves using machine learning models to analyse historical data and real-time variables for demand forecasting, route optimisation, and inventory management. This reduces costs, improves delivery times, and minimises waste by dynamically adjusting to changes in supply and demand.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Using AI to write optimised supplier contracts with better negotiation terms

    Why it's wrong here

    Using AI to draft supplier contracts and negotiate terms is a natural language generation and commercial/legal task, not supply chain optimisation. Contract language may reflect negotiation strategy and compliance requirements, but it does not model product demand, route efficiency, or inventory replenishment. Supply chain optimisation focuses on operational logistics decisions, whereas contract writing remains a procurement and legal activity.

  • Using AI for demand forecasting, route optimisation, and inventory management across the supply chain

    Why this is correct

    Supply chain optimisation uses AI to forecast demand from historical sales and external signals, compute efficient delivery routes with real-time traffic and constraint data, and set optimal inventory policies such as safety stock and reorder points. These predictive and prescriptive analytics reduce transportation costs, avoid stockouts, and improve service levels. This is the workload that directly optimises the flow of goods and materials.

  • Automating supplier onboarding by extracting information from registration documents

    Why it's wrong here

    Automating supplier onboarding by extracting text from registration documents is a document intelligence workload, commonly implemented with OCR and form understanding in Azure AI Document Intelligence. It digitises structured data from forms but does not apply predictive analytics to logistics, demand, or stock levels. Supply chain optimisation is about forecasting and decision-making across the physical supply chain, not digitising supplier paperwork.

  • Monitoring supply chain staff performance using AI-powered productivity tracking

    Why it's wrong here

    AI-powered productivity tracking for monitoring supply chain staff is an HR analytics workload, not supply chain optimisation. It typically uses computer vision or activity monitoring to assess individual employee performance, whereas supply chain optimisation applies predictive models to the physical flow of goods, warehouse operations, and logistics decisions. Because it targets workforce efficiency rather than demand, routing, or inventory, it does not match the workload described.

About these practice questions

One of 985 original AI-900 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →

How Courseiva writes practice questions · Editorial policy

JA

Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.