So you are preparing for the Azure AI-102 exam and you want to test yourself with realistic questions.. If you searched for Azure AI-102 practice questions, you're in the right place Not the easy definition questions, but the scenario-based ones that actually show up on the exam. Here are 20 questions that cover the most important service selection and architecture decisions you need to master.
| Exam Domain | Questions Covered | Key Services Tested |
|---|---|---|
| Plan and manage an Azure AI solution | Q3, Q8, Q12, Q13 | Azure AI Content Safety, multi-service resource, managed identity |
| Implement generative AI solutions | Q2, Q6, Q14, Q16, Q20 | Azure OpenAI, RAG, max tokens, DALL-E, vector search |
| Implement an agentic solution | Q9 | Agent testing, escalation, tool use |
| Implement knowledge mining | Q7, Q10, Q11 | Azure AI Search semantic ranking, custom skills, knowledge store |
| Implement NLP and language | Q5, Q15 | PII detection, sentiment analysis |
| Implement computer vision and speech | Q1, Q4 | Document Intelligence, Speech-to-text |
Questions
Question 1
A company needs to extract line items from scanned invoices and map them to a database. Which Azure AI service is the best choice?
A) Azure AI Vision B) Azure AI Search C) Azure AI Document Intelligence D) Azure AI Language
Answer: C Azure AI Document Intelligence is purpose-built for extracting structured fields from documents like invoices, receipts, and forms. Azure AI Vision provides OCR but does not offer the same document field extraction workflow. Azure AI Search is for retrieval, not extraction.
Question 2
A developer wants to build a chatbot that answers questions using the company's internal documentation. The documents are stored in Azure Blob Storage. Which architecture pattern should they use?
A) Fine-tune a model on the company documents B) Use RAG with Azure AI Search as the retrieval layer C) Store documents in Azure AI Language for query D) Use Azure AI Speech to read documents aloud
Answer: B RAG (Retrieval-Augmented Generation) with Azure AI Search is the standard pattern for grounding model responses on enterprise documents. Fine-tuning is expensive and does not handle document updates well. Azure AI Language does not store documents.
Question 3
A solution must support both text and image inputs to classify content safety. Which service handles both modalities?
A) Azure AI Language B) Azure AI Vision C) Azure AI Content Safety D) Azure AI Search
Answer: C Azure AI Content Safety detects harmful content in both text and images. Azure AI Language handles text only. Azure AI Vision handles images but does not focus on content safety classification.
Question 4
A team needs to implement speech-to-text for call center recordings, then translate the transcript to French. Which two services are required?
A) Azure AI Speech and Azure AI Translator B) Azure AI Language and Azure AI Speech C) Azure AI Vision and Azure AI Translator D) Azure AI Search and Azure AI Language
Answer: A Azure AI Speech handles speech-to-text. Azure AI Translator handles text translation. Azure AI Language does speech-to-text, and Azure AI Vision is for images.
Question 5
An application must detect personally identifiable information (PII) in customer support tickets and redact phone numbers and email addresses. Which Azure AI Language feature should be used?
A) Key phrase extraction B) Sentiment analysis C) Named entity recognition D) PII detection
Answer: D PII detection specifically identifies sensitive personal information like phone numbers, email addresses, and social security numbers. Named entity recognition identifies entities but does not focus on PII redaction.
Question 6
A developer wants to deploy an Azure OpenAI model and control the maximum response length to reduce costs. Which parameter should they configure?
A) Temperature B) Top P C) Max tokens D) Frequency penalty
Answer: C Max tokens limits the length of the generated response, directly controlling cost and verbosity. Temperature controls randomness, Top P controls token selection diversity, and frequency penalty reduces repetition.
Question 7
A search solution must return results based on semantic meaning, not just keyword matching. Which Azure AI Search feature enables this?
A) Filter expressions B) Semantic ranking C) Faceted navigation D) Autocomplete
Answer: B Semantic ranking uses language understanding to rank search results by meaning, not just keyword overlap. It is one of the most tested Azure AI Search features on the AI-102 exam.
Question 8
A company wants to use a single endpoint and key to access multiple Azure AI services. Which resource type should they create?
A) Single-service Azure AI services resource B) Multi-service Azure AI services resource C) Azure OpenAI resource D) Azure AI Search resource
Answer: B A multi-service Azure AI services resource provides one endpoint and key for multiple services (Language, Vision, Speech, Translator). This is explicitly tested on the exam.
Question 9
A team is building an autonomous support agent that must escalate to a human when it cannot resolve a question. Which testing approach is most aligned with AI-102 objectives?
A) Bounding box accuracy metrics B) Task completion, tool use, safety, and escalation testing C) Image classification accuracy D) Speech recognition word error rate
Answer: B Agent testing should cover task completion, tool use, safety, and escalation paths. Bounding box metrics are for object detection, not agent workflows.
Question 10
A developer needs to enrich documents in an Azure AI Search index with custom risk scores from an external API. Which skill type should they add to the skillset?
A) Custom Vision skill B) Entity recognition skill C) Custom Web API skill D) OCR skill
Answer: C A Custom Web API skill allows Azure AI Search enrichment pipelines to call external APIs for custom enrichment logic. This is a standard pattern tested on the exam.
Question 11
A solution must persist enriched search pipeline output to Azure Storage for downstream analytics. Which Azure AI Search feature enables this?
A) SSML configuration B) Semantic ranking C) Knowledge Store projections D) Indexer schedules
Answer: C Knowledge Store projections persist enriched search pipeline outputs to Azure Storage tables and blobs. This is a key feature for knowledge mining scenarios.
Question 12
A team wants to implement least-privilege access to Azure AI services without using API keys. Which authentication approach should they use?
A) API keys in application configuration B) Managed identity with RBAC C) Shared access signatures D) Basic authentication
Answer: B Managed identity with RBAC eliminates the need for hardcoded keys and follows the principle of least privilege. This is the security pattern Microsoft recommends and tests heavily.
Question 13
A solution must restrict Azure AI services to private network access only. Which networking feature should be configured?
A) Service endpoints B) Private endpoints C) Network security groups D) Application gateway
Answer: B Private endpoints restrict Azure AI services to private network access, eliminating public internet exposure. This is the standard pattern tested for network security scenarios.
Question 14
A developer needs to generate images from text descriptions using Azure OpenAI. Which model family should they use?
A) GPT-4 B) Whisper C) DALL-E D) Embeddings
Answer: C DALL-E is the model family for image generation from text prompts. GPT-4 generates text, Whisper handles speech-to-text, and Embeddings are for vector representations.
Question 15
A solution must analyze customer reviews and determine whether each review is positive, negative, or neutral. Which Azure AI Language feature should be used?
A) PII detection B) Key phrase extraction C) Sentiment analysis D) Language detection
Answer: C Sentiment analysis determines the emotional tone of text. It is one of the most commonly tested Azure AI Language features on the exam.
Question 16
A team needs to build a vector search index over embedded document chunks for a RAG solution. Which index field type is required?
A) Edm.String B) Collection(Edm.Single) with vector search configuration C. Edm.DateTimeOffset D) Edm.Int32
Answer: B Vector fields use Collection(Edm.Single) with vector search configuration to enable similarity search over embedded content. This is a core concept for generative AI and RAG scenarios.
Question 17
A company wants to monitor Azure AI service usage, latency, and error rates. Which Azure service should they use?
A) Azure AI Search B) Azure Monitor C) Azure AI Content Safety D) Azure Key Vault
Answer: B Azure Monitor collects metrics, logs, and alerts for Azure AI services. It is the standard monitoring solution tested across all AI-102 domains.
Question 18
A solution must translate documents (not just text) from English to Japanese while preserving formatting. Which service should be used?
A) Azure AI Language B) Azure AI Translator (document translation) C) Azure AI Speech D) Azure AI Search
Answer: B Azure AI Translator supports document translation, preserving the original file format. Azure AI Language handles text analysis, not document translation.
Question 19
A team needs to implement CI/CD for an Azure AI application that includes model deployment and prompt flow updates. Which toolchain is most appropriate?
A) Azure DevOps or GitHub Actions with Azure ML / Foundry deployment tasks B) Manual deployment through the Azure portal C) Azure AI Search indexer schedules D) Azure Monitor alert rules
Answer: A Azure DevOps or GitHub Actions with deployment tasks is the standard CI/CD approach for Azure AI applications. The exam tests your ability to automate deployment, not do it manually.
Question 20
A developer wants to ground a generative AI model on enterprise data without fine-tuning. Which approach is most cost-effective and maintainable?
A) Fine-tune the model on all enterprise documents B) Use RAG with Azure AI Search to retrieve relevant context at query time C) Store all documents in the model's prompt D) Train a custom model from scratch
Answer: B RAG with Azure AI Search is the most cost-effective and maintainable approach. It retrieves relevant context at query time without modifying the model, and documents can be updated without retraining.
FAQ
How many questions are on the AI-102 exam?
The AI-102 exam typically has 40 to 60 questions and lasts 120 minutes. Question types include multiple choice, drag-and-drop, case studies, and scenario-based questions.
What is the passing score for AI-102?
Microsoft does not publish a fixed passing score, but it is generally around 700 out of 1000.
How much does the AI-102 exam cost?
The exam costs $165 USD. Retakes are the same price.
How long should I study for AI-102?
Most candidates need 8 to 12 weeks of focused study, especially if they are new to Azure AI services. Hands-on practice with the Azure portal is essential.
Is AI-102 harder than AI-900?
Yes. AI-900 is a foundational exam that tests basic AI concepts. AI-102 is an associate-level exam that tests your ability to design, build, and deploy real Azure AI solutions.
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