Azure AI 900 Exam: Understand AI Workloads in Azure

1. Key AI Workloads on Azure

Azure supports a variety of AI workloads through its services. These are the primary categories to focus on:

a. Machine Learning Workloads

Tasks: Training, deploying, and managing ML models.
Azure Services:
Azure Machine Learning (Azure ML)
Azure Databricks
Cognitive Services Custom Vision

Use Cases:
Predictive analytics
Forecasting
Image classification
NLP-based sentiment analysis

b. Conversational AI

Tasks: Building bots and conversational interfaces.
Azure Services:
Azure Bot Service
Language Studio
Speech-to-Text and Text-to-Speech

Use Cases:
Chatbots for customer support
Voice-enabled virtual assistants

c. Cognitive Services

Tasks: Using pre-built AI APIs to integrate intelligence into apps.
Azure Services:
Vision (Computer Vision, Custom Vision, Face API)
Language (Text Analytics, Translator)
Speech (Speech Recognition, Synthesis)
Decision (Personalizer, Anomaly Detector)

Use Cases:
OCR and document analysis
Sentiment analysis
Speech-enabled applications

d. AI at the Edge

Tasks: Running AI models on edge devices for real-time insights.
Azure Services:
Azure IoT Edge
Azure Percept

Use Cases:
Industrial IoT
Smart cameras
Real-time object detection

2. Core Considerations for AI Solutions

a. Data Preparation

Key Concepts:
Data collection, cleaning, and preprocessing.
Ensuring balanced datasets to avoid bias.
Secure and compliant storage with Azure Blob or Azure Data Lake.

Best Practices:
Use Azure Data Factory for ETL processes.
Leverage Azure Synapse Analytics for data integration.

b. Model Selection and Training

Key Concepts:
Understanding supervised, unsupervised, and reinforcement learning.
Using pre-built models (e.g., Cognitive Services) versus custom ML models.

Best Practices:
Train models in Azure ML with AutoML or custom pipelines.
Monitor performance metrics like accuracy, recall, precision.

c. Deployment and Integration

Key Concepts:
Deploying models as REST APIs using Azure ML or Azure Functions.
Ensuring scalability with Azure Kubernetes Service (AKS).

Best Practices:
Use Azure API Management for secure API endpoints.
Optimize for latency using edge deployments with Azure IoT Edge.

d. Security and Compliance

Key Concepts:
Ensuring data protection and model security.
Compliance with standards like GDPR, HIPAA, or ISO.

Best Practices:
Use Azure Key Vault for managing sensitive keys and credentials.
Leverage Azure Security Center for threat detection.

e. Monitoring and Maintenance
Key Concepts:
Tracking model performance post-deployment.
Handling model drift and retraining.

Best Practices:
Set up alerts with Azure Monitor.
Use Azure ML’s model management capabilities for versioning and retraining.

3. Certification-Specific Tips

*Understand Azure AI Services: * Study documentation for Cognitive Services, Azure ML, and Azure Bot Service.
Hands-on Experience: Practice building, deploying, and scaling AI solutions in Azure.

Ethical AI: Learn Azure's Responsible AI principles, including fairness, transparency, and accountability.
Case Studies and Scenarios: Review real-world applications of AI on Azure to connect theory with practice.

Sample Exams: Familiarize yourself with question patterns and time management.

Prepare and Practice For Exam

By focusing on these AI workloads and considerations, you'll be well-prepared to design and manage AI solutions on Azure while meeting the certification's requirements.