AI-900 Microsoft Azure AI Fundamentals Exam - Key Points

Introduction

• Total: 160 Exam Questions and Answers
• Importance of subscribing to their YouTube channel
• PDF available for download on shapingpixels.com

Key Topics and Answers

1. Microsoft Transparency Principle

• Question: Ensure model meets the transparency principle.
• Answer: Enable explain best model.
• Importance: Transparency in regulated industries (e.g., Healthcare, Banking).
• Model Explainability: Helps understand feature importance.

2. Data Transformation Modules

• Question: Examples of data transformation modules in Azure ML designer.
• Answer: Split Data, Clean Missing Data.
• Function: Prepare data before a machine learning experiment.

3. Web Chat Bot Benefits

• Question: Business benefits of implementing a web chat bot.
• Answer: Reduce workload for customer service agents.
• Function: Automate responses to common queries.

4. Anomaly Detection Service

• Question: Identify issues from sensor data in real-time.
• Answer: Use Anomaly Detector.

5. Data Splitting for ML Models

• Question: How to split data for training and evaluation.
• Answer: Randomly split data into rows for training and rows for evaluation.

6. Predictive Algorithms

• Question: Algorithm for predicting fuel efficiency (continuous value).
• Answer: Regression algorithm.
• Other Algorithms: Classification for discrete values, clustering for patterns.

7. Confusion Matrix Analysis

• Question: Details for interpreting confusion matrix.
• Answers: Correctly predicted positives: 11, False negatives: 1, 33.

8. Chat Program from FAQs

• Question: Building chat program using FAQ documentation.
• Answer: Q&A Maker + Azure Bot Service.
• Alternative: Language Understanding (LUIS) if not just simple Q&A.

9. Anomaly Detection Examples

• Question: Statements about anomaly detection.
• Answers: Incorrectly stated examples corrected.
• Function: Detects deviations in patterns (e.g., fraud detection, network intrusions).

10. Handling Unusual/Missing Values

• Question: Principle for handling unusual or missing values.
• Answer: Reliability and safety.
• Emphasis: Ensuring AI systems perform as designed and respond safely.

11. User Utterance Term in Language Model

• Question: Term for user's spoken query or command.
• Answer: Utterance.
• Example: