AI102: Designing and Implementing a Microsoft Azure AI Solution

Overview

• Certification: Azure AI Engineer Associate upon passing the exam.
• Duration: 2 hours total, with 20 minutes for survey, leaving 1 hour 40 minutes of actual exam time.
• Total Questions: 42, typically straightforward in nature.
• Advice: Take your time; there's ample opportunity to answer each question thoroughly.

Exam Preparation

Resources

• Self-Paced Learning: Recommended to complete all modules on the AI102 page.
• Skills Measured: Review the detailed skills guide to prepare effectively.
• Hands-On Labs: Engage with labs provided, even if not directly required in the exam.

Exam Structure

• No hands-on coding during the exam; may involve code comprehension.
• Understand the context of whether to use C# or Python as languages for examples.
• Logical thinking is essential; questions are designed to be straightforward.

Key Concepts in AI Engineering

Essential Skills

• Programming: Familiarity with C# or Python.
• API Interaction: Comfortable with RESTful APIs, understanding JSON structure.
• DevOps: Knowledge of source control and CI/CD practices.

Responsible AI

• Fairness: Ensure no bias in AI systems.
• Reliability and Safety: Crucial for applications like autonomous vehicles or medical diagnoses.
• Privacy: Protect personal data and ensure models are secure.
• Inclusiveness: Systems should be accessible to everyone.
• Transparency and Accountability: Clear understanding of how AI works and responsible usage.

Understanding Artificial Intelligence

• Definition: AI simulates human-like capabilities across various tasks.
• Components:
  • Visual Perception: Recognizing images and interpreting content.
  • Language Processing: Understanding and generating human language.
  • Decision Making: Making predictions based on data patterns.
• Machine Learning: A foundational element of AI built on data and algorithms.

Azure AI Services

Core Services

1. Azure Machine Learning: Framework for building, training, and deploying models.
2. Cognitive Services: Range of tools for adding AI capabilities to applications.
   • Visual Analysis: Image and video analysis capabilities.
   • Language Understanding: Tools for natural language processing and sentiment analysis.
   • Speech Services: Converting speech to text and vice versa.
   • Anomaly Detection: Identifying outliers in datasets.
   • Content Moderation: Ensuring appropriate content in images and texts.

Pre-Built Solutions

• Forms Recognizer: Extracts information from forms using OCR.
• Metrics Advisor: Monitors data for anomalies in time series.
• Video Analyzer: Analyzes video content for various insights.
• Cognitive Search: Powerful search capabilities across indexed data.

Exam Strategy

• Time Management: Don’t rush; you have plenty of time.
• Mark Questions: Mark uncertain questions to revisit them later.
• Review Answers: Some questions may provide hints for others.
• Stay Calm: Focus on understanding each question; stress can impact performance.

Closing Advice

• Familiarize yourself with all services, their functionalities, and practical applications.
• Understand the relationships between services and how they can be combined to solve problems.
• Utilize the resources available on the Azure platform for self-study.