I created this repository to help you prepare for the AI-900 exam. Unlike other exams, this one is rather simple as long as you understand the content covered in the official learning paths. Working through practice questions helped me pass this exam on the first try. Verify my certification

Official Website: Microsoft Azure AI Fundamentals

Certification (after passing this exam): Microsoft Certified: Azure AI Fundamentals

Disclaimer: These practice questions are very similar to the actual exam questions in style and skill level; yet are only indicative and by no means a comprehensive list of questions. Questions have not been transcribed from the real exam, which is against exam policy.

1. Object Detection
2. Semantic Segmentation
3. Image Analysis
4. Optical Character Recognition

1. Object Detection
2. Semantic Segmentation
3. Image Analysis
4. Optical Character Recognition

1. Q&A Maker
2. Semantic Segmentation
3. Azure Speech Maker
4. Azure Chat Functions

1. Compute Instances
2. Compute Balancers
3. Compute Clusters
4. Inference Clusters

1. Classification
2. K-means
3. Clustering
4. Neural Network

1. Classification
2. K-means
3. Clustering
4. Neural Network

1. Computer Vision and Computer Cluster
2. Computer Key and Computer Endpoint
3. Computer Vision and Computer Services
4. Computer Insights and Computer Services

1. Stars and Places
2. Marker and People
3. Background and Landmarks
4. Celebrities and Landmarks

1. Precision
2. Recall
3. Mean Average Precision
4. F1 Score

1. JPEG
2. AI
3. PNG
4. BMP

1. Regions
2. Areas
3. Lines
4. Words

1. Neural Network
2. SVM
3. Cognitive Text
4. Neural Machine Translation

1. List
2. RegEx
3. Filter
4. Machine-Learned

1. Classification
2. Regression
3. Normalization
4. Clustering

1. Decision Forest
2. K-means
3. K-nearest Neighbor
4. Anomaly Detection

1. Reinforcement Learning
2. Principal Component Analysis
3. K-means
4. Classification Algorithm

1. Excel
2. CSV
3. TSV
4. XML

1. Update values to use a common scale
2. Create a smaller set of discrete ranges
3. Update missing values with other values
4. Match data against the question

1. Precision
2. Accuracy
3. Recall
4. F1 Score

1. Precision
2. Accuracy
3. Recall
4. F1 Score

1. Precision
2. Accuracy
3. Recall
4. F1 Score

1. Ordinal
2. Linear
3. SVM
4. Poisson

1. Ordinal
2. Linear
3. SVM
4. Poisson

1. Research, testing, deployment
2. Transformation, Visualisation, Modeling
3. Workflow, Modeling, Deployment
4. ETL, ELT, Testing

1. Used to normalize data
2. Defines higher level features of models
3. Are provided as inputs
4. Cannot be directly learned from data using model training processes

1. Mean Absolute Error
2. Average Distance to Cluster Center
3. Average Distance to Other Center
4. Number of Points

1. 3D
2. Speech
3. Vision
4. Language

1. Image Stabalization
2. Tagging Images
3. Motion Detection
4. Video Thumbnail

1. Inclusiveness
2. Transparency
3. Accountability
4. Fairness