Azure AI Interview Questions and Answers

Azure AI is Microsoft’s set of cloud services and tools for building AI solutions — including Azure AI services (Vision, Language, Speech, Translator, Document Intelligence), Azure OpenAI for generative AI, Azure AI Search for retrieval and Azure Machine Learning for custom models.

AI is the broad goal of machines performing tasks that need human-like intelligence. Machine learning is a subset where models learn patterns from data. Deep learning is a subset of ML that uses multi-layer neural networks, common in vision, speech and language models.

They are pre-built, API-based AI capabilities — such as image analysis, OCR, sentiment analysis, speech-to-text and translation — that you call through REST APIs or SDKs without training your own models.

Using a resource key and endpoint, or more securely with Microsoft Entra ID (for example managed identities and role-based access control). Keys should be stored in Azure Key Vault, not in code.

A logical container that holds related Azure resources so you can manage, secure and delete them together.

Microsoft’s principles are fairness, reliability and safety, privacy and security, inclusiveness, transparency and accountability.

To analyse images — tagging, captions, object detection and OCR to read printed or handwritten text.

Extracting text, key-value pairs, tables and fields from documents such as invoices, receipts and forms, using prebuilt or custom models.

Sentiment analysis, key phrase extraction, named entity recognition, language detection, summarisation, PII detection, conversational language understanding and question answering.

Speech-to-text, text-to-speech, speech translation and custom speech and voice models.

A service that detects harmful text and images — such as hate, violence, sexual and self-harm content — so apps can filter or flag them.

It gives access to OpenAI models (such as GPT models) through Azure, with Azure security, networking, regional deployment and content filtering. You deploy a model and call it by its deployment name.

The model is the underlying LLM. A deployment is your named instance of that model in your Azure OpenAI resource; your code calls the deployment name.

Tokens are pieces of text that models read and generate. They determine context length limits and cost, so prompts and responses should be kept efficient.

Temperature controls randomness — lower values give more focused answers. Max tokens limits the length of the model’s response.

RAG retrieves relevant content from your own data — for example with Azure AI Search — and adds it to the prompt so the model answers using your documents, which improves accuracy and reduces hallucinations.

Numerical vectors that represent the meaning of text. Similar texts have similar vectors, which enables semantic and vector search in RAG solutions.

Ground the model with RAG, write clear system prompts, ask it to answer only from the provided context, lower temperature and add evaluation and human review for important outputs.

Designing prompts — system messages, instructions, examples (few-shot) and output formats — so the model produces reliable, useful answers.

A platform to prepare data, train, track, deploy and monitor ML models, with workspaces, compute, AutoML, designer, pipelines and MLflow-based tracking.

Automated machine learning tries multiple algorithms and settings to find a good model for your data, saving manual experimentation time.

Real-time (online) endpoints return predictions instantly for single requests. Batch endpoints score large datasets asynchronously.

With metrics such as accuracy, precision, recall, F1-score, the confusion matrix and ROC-AUC, chosen based on the business cost of errors.

Applying DevOps practices to ML — versioning data and models, automated training and deployment pipelines (CI/CD), and monitoring for performance and data drift.

A change in the statistical properties of input data over time compared with training data, which can reduce model accuracy and signals that retraining may be needed.

Store and index the PDFs in Azure AI Search with embeddings, retrieve the most relevant chunks for each question, send them with a grounded system prompt to an Azure OpenAI deployment and return cited answers — with Content Safety and logging enabled.

Use Document Intelligence’s prebuilt invoice model (or a custom model), process files in batches from Blob Storage, validate low-confidence fields and save results to a database.

Choose the right pricing tier, set budgets and alerts, cache repeated results, keep prompts short, use smaller models where possible and delete unused resources.

Use Microsoft Entra ID and managed identities, store secrets in Key Vault, restrict network access with private endpoints, apply least-privilege RBAC and enable logging and monitoring.