Master your AI-102T00 interview with these comprehensive questions and answers designed to help you excel in developing AI solutions on Azure.
Preparing for the AI-102T00: Develop AI solutions in Azure interview requires a thorough understanding of both foundational and advanced concepts related to Azure AI services. Here, we provide the top 25 interview questions along with detailed answers to help you ace your interview.
The AI-102 certification, also called "Designing and Implementing an Azure AI Solution", is designed for AI Engineers responsible for building, managing, and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. It suits data scientists, developers, or engineers working on intelligent apps.
Azure Cognitive Services is a suite of APIs and SDKs that allow developers to easily integrate AI capabilities like:
Grouped into 4 categories:
Azure Bot Service provides tools to build conversational agents (bots) that can:
The AI-102 certification is designed for professionals who want to develop AI-powered applications using Microsoft Azure. It validates skills in integrating Azure Cognitive Services, Azure Machine Learning, and conversational AI solutions. The goal is to ensure candidates can build, manage, and deploy intelligent applications responsibly and efficiently in real-world business environments.
Azure Form Recognizer uses machine learning to extract key-value pairs, tables, and text from documents such as invoices, receipts, and forms. This significantly reduces manual data entry and processing errors. It supports both prebuilt and custom models, making it adaptable to various document types and industry-specific formats.
|
Feature |
Azure Cognitive Services |
Azure Machine Learning |
|
Purpose |
Prebuilt AI capabilities |
Build and train custom ML models |
|
Technical skill required |
Low (REST APIs, SDKs) |
High (Python, ML frameworks) |
|
Use Case |
Image recognition, speech, sentiment |
Predictive analytics, custom AI |
LUIS helps apps understand natural language input. It:
Azure LUIS enables applications to understand natural language commands from users by identifying intents and extracting relevant entities. It is particularly useful for chatbots and voice-based interfaces, helping systems interact more naturally with humans. Developers can train and publish models easily through the LUIS portal.
Azure Cognitive Services provides prebuilt AI models accessible through APIs, allowing developers to quickly add capabilities like vision, speech, and language understanding to applications. It reduces the need for deep AI expertise and shortens development time. These services are scalable, secure, and continuously improved by Microsoft.
Azure Bot Service simplifies the creation of intelligent bots by integrating tools like the Bot Framework SDK, Azure Functions, and LUIS. It allows bots to be deployed across channels like Microsoft Teams, Slack, and websites. The service ensures scalable, real-time user interactions while supporting multilingual and multi-modal inputs.
13. Explain the steps to use Computer Vision Read API.
Azure Translator offers real-time text translation between 90+ languages using:
|
Feature |
Text Analytics |
LUIS |
|
Goal |
Extract sentiment, key phrases |
Understand intent + extract entities |
|
Customization |
No |
Yes |
|
Use Case |
Feedback analysis |
Conversational interfaces |
Azure Speech provides:
It’s useful in transcription, accessibility tools, and voice assistants.
17. What is the role of Azure Cognitive Search in AI solutions?
Azure Cognitive Search enables developers to create intelligent search experiences using AI. It allows indexing of structured and unstructured content, with capabilities like full-text search, filtering, and ranking. When integrated with AI enrichment (skills like OCR or key phrase extraction), it transforms raw content into meaningful, searchable insights for end-users.
Speech-to-text converts spoken audio into written text and is useful for transcription, voice commands, or live captioning. Text-to-speech does the reverse, generating lifelike speech from written content, often used in accessibility tools and virtual assistants. Azure supports both with customizable voices and language options for natural user experiences.
Skillsets are pipelines that define:
20. What is the difference between Custom Vision and Computer Vision APIs?
|
Feature |
Custom Vision |
Computer Vision |
|
Training |
Custom model with own data |
Prebuilt models |
|
Use Case |
Brand logo detection |
OCR, image description |
|
Customizable |
Yes |
No |
Azure Machine Learning allows data scientists and developers to build, train, and deploy machine learning models at scale. It supports automation, MLOps, and integration with popular frameworks like TensorFlow and PyTorch. In AI-102 scenarios, it is mainly used when prebuilt Cognitive Services aren’t sufficient for specialized business needs.
Azure AI services offer multiple security layers, including authentication through Azure Active Directory, role-based access control (RBAC), and private endpoints. Developers can secure APIs using keys and restrict traffic using virtual networks or firewalls. Additionally, Azure ensures compliance with global standards like GDPR and ISO 27001.
AI enrichment is a feature in Azure Cognitive Search that uses cognitive skills to analyze and transform content during indexing. It extracts text from images, detects language, and pulls out entities or key phrases using Cognitive Services. This enriched metadata improves the quality and accuracy of the search experience.