AI-102 Practice Exams | AI-102 Azure AI Engineer

Master the Azure AI Engineer Associate (AI-102) Exam: Practical Skills & Real-World Application

Ready to confidently achieve the Microsoft Certified: Azure AI Engineer Associate (AI-102) certification? This comprehensive course goes beyond simple memorization, providing you with the practical skills and real-world scenarios needed to excel in your exam and advance your career as an Azure AI Engineer.

Why Choose This Course for AI-102 Exam Prep?

• Go Beyond Rote Learning: We focus on applying your knowledge to complex, scenario-based questions, mirroring the challenges you'll face in real-world AI solution development.
• Identify and Address Knowledge Gaps: Detailed explanations and valuable resource links help you understand why answers are correct, allowing you to pinpoint areas for focused study.
• Build Confidence & Reduce Exam Anxiety: Familiarize yourself with the exam format, question types, and pace to feel prepared and calm on test day. Our randomized question order ensures thorough understanding.
• Ideal Supplement to Your Studies: Perfect for reinforcing concepts after theoretical learning, tackling challenging scenarios, and validating your readiness for the DP-100 exam and beyond.
• Our Commitment to Effective Learning: We prioritize deep understanding over quick fixes, providing ethical learning tools and avoiding "real-time dumps" that hinder true skill development.

Target Audience: The Modern Azure AI Engineer

This course is designed for individuals who build, manage, and deploy AI solutions on Microsoft Azure. If your responsibilities include:

• AI solution development lifecycle: Requirements definition, design, development, deployment, integration, maintenance, performance tuning, and monitoring.
• Collaborating with solution architects, data scientists, data engineers, IoT specialists, and other developers.
• Utilizing languages like Python and C#.
• Leveraging REST APIs and SDKs for various AI applications (image processing, video processing, NLP, knowledge mining, generative AI).

You'll gain expertise in:

• Understanding the Azure AI portfolio and data storage options.
• Applying responsible AI principles.

Key Skills Covered (AI-102 Exam Domains):

• Plan and Manage an Azure AI Solution (20-25%):
  • Selecting the right Azure AI Foundry services.
  • Choosing services for generative AI, computer vision, natural language processing, speech, and information extraction.
  • Planning and deploying Azure AI Foundry services.
  • Implementing Responsible AI principles.
  • Creating and deploying Azure AI resources.
  • Selecting appropriate AI models.
  • Deploying models using various options.
  • Utilizing SDKs and APIs.
  • Managing endpoints and CI/CD pipelines.
  • Monitoring, securing, and managing costs of Azure AI Foundry Services.
• Implement Generative AI Solutions (15-20%):
  • Building generative AI solutions with Azure AI Foundry.
  • Preparing and deploying generative AI solutions.
  • Utilizing Azure AI Foundry Hub, Project, and resources.
  • Implementing prompt flow solutions and RAG patterns.
  • Evaluating and integrating Azure OpenAI with Foundry SDK.
  • Leveraging prompt templates and Azure OpenAI in Foundry Models.
  • Utilizing DALL-E for image generation.
  • Optimizing and operationalizing generative AI solutions.
• Implement an Agentic Solution (5-10%):
  • Creating custom agents and understanding their use cases.
  • Configuring necessary resources to build agents.
  • Implementing complex agents with Semantic Kernel and Autogen.
  • Developing complex workflows for multi-agent solutions.
  • Testing, optimizing, and deploying agents.
• Implement Computer Vision Solutions (10-15%):
  • Analyzing images and selecting features.
  • Detecting objects and generating tags.
  • Implementing image analysis features.
  • Extracting text from images using Azure AI Vision.
  • Converting handwritten text.
  • Choosing between image classification and object detection.
  • Training and evaluating custom vision models.
  • Publishing and consuming custom vision models.
• Implement Natural Language Processing Solutions (15-20%):
  • Analyzing and translating text.
  • Extracting key phrases and entities.
  • Determining sentiment and language in text.
  • Detecting Personally Identifiable Information (PII).
  • Processing and translating speech.
  • Implementing generative AI speaking capabilities.
  • Utilizing Azure AI Speech