Microsoft Certified: Azure AI Engineer Associate Advanced Practice Exam: Hard Questions 2025

You manage an Azure OpenAI-based customer support assistant used by multiple internal applications. You must: (1) ensure no public network access to the model endpoint, (2) enforce least-privilege for different apps (some can only call embeddings, others can call chat completions), and (3) centralize policy and routing so you can swap model deployments without changing client code. Which architecture best meets the requirements?

A retail company built an image processing pipeline using Azure AI Vision. Images are captured in stores and uploaded to Blob Storage. A Function triggers and calls the Vision API to classify images. During peak hours, many requests fail with transient 429 responses and processing latency spikes. You must minimize dropped images, preserve ordering per store, and avoid overloading the Vision endpoint. Which design change is best?

You built a real-time safety monitoring solution that uses Azure AI Vision to detect hard-hat compliance from CCTV frames. The false-negative rate is acceptable overall but spikes at night due to low-light conditions and motion blur. You must reduce false negatives without significantly increasing latency or sending larger images over the network. Which approach is most appropriate?

You are designing a multilingual customer feedback analytics system using Azure AI Language. Requirements: (1) extract key phrases and sentiments in the source language, (2) support domain-specific terminology (product names), and (3) ensure consistent results across deployments and environments. Which solution best meets these needs?

A compliance team uses Azure AI Language to detect personally identifiable information (PII) in chat logs. They observe two issues: (1) high false positives for strings that look like IDs but are product SKUs, and (2) missed detections for local identifiers used in a specific country. You must improve accuracy while preserving auditability and minimizing manual rules. What should you do?

You built a RAG chatbot over internal policy documents. Users complain that answers are sometimes grounded in the wrong policy version. Documents have multiple revisions, and older revisions must be retained for legal reasons but should not be used unless the user explicitly asks for historical policies. Which change best addresses the issue?

Your solution uses Azure AI Search for knowledge mining across PDFs and Office documents stored in Blob Storage. A new data source contains many scanned PDFs with mixed orientations and handwritten notes. Search relevance is poor because the text extraction output is incomplete and noisy. You must improve extraction quality and normalize content for indexing while keeping the pipeline maintainable. What is the best approach?

You are extracting structured fields from invoices using Azure AI Document Intelligence. A new supplier uses a layout that frequently changes and includes optional sections. The model sometimes maps fields incorrectly (e.g., shipping address into billing address). You need higher accuracy and a strategy that remains resilient to layout drift. What should you do?

You must deploy a generative AI assistant that uses internal documents and must prevent data exfiltration. Requirements: (1) block responses that contain secrets (API keys, connection strings), (2) reduce jailbreak success, (3) log safety signals for audits, and (4) keep latency low. Which combination best satisfies the requirements?

Your team is troubleshooting a RAG application using Azure AI Search + Azure OpenAI. The assistant sometimes hallucinates citations that do not exist in the retrieved documents, even though you include retrieved chunks in the prompt. You must reduce fabricated citations while keeping answer quality high. What is the best change?