Tracing and Observability Quiz
5 questions Pass: 70% +25 pts (50 with Pro)
Quiz covering Optimize Generative AI Systems
What you'll be asked
- What is the primary purpose of distributed tracing in a Generative AI application?
- When monitoring an agentic system, which metric is most critical for evaluating the quality of the LLM's reasoning process?
- Why is it important to capture 'metadata' (such as prompt versions and tool definitions) within your traces?
- In the context of agentic systems, what is the 'span' concept in tracing?
- You notice that your agentic system is frequently entering 'infinite loops' during complex tool-use tasks. Which observability pattern is most effective for diagnosing this behavior?
Answers and explanations come after you submit.
Course Content
- Introduction to Microsoft Foundry
- Introduction to Microsoft Foundry Quiz5q
- Choosing AI Models for Tasks
- Choosing AI Models for Tasks Quiz5q
- LLMs and Small Language Models
- LLMs and Small Language Models Quiz5q
- Multimodal Models Overview
- Multimodal Models Overview Quiz5q
- Foundry Tools and Services
- Foundry Tools and Services Quiz5q
- Retrieval and Indexing Methods
- Retrieval and Indexing Methods Quiz5q
- Deploy LLMs in Foundry
- Deploy LLMs in Foundry Quiz5q
- Implement RAG Patterns
- Implement RAG Patterns Quiz5q
- Tool-Augmented Workflows
- Tool-Augmented Workflows Quiz5q
- Multistep Reasoning Pipelines
- Multistep Reasoning Pipelines Quiz5q
- Evaluate Models and Apps
- Evaluate Models and Apps Quiz5q
- Foundry SDKs and Connectors
- Foundry SDKs and Connectors Quiz5q
- Define Agent Roles and Goals
- Define Agent Roles and Goals Quiz5q
- Agents with Retrieval and Function Calling
- Agents with Retrieval and Function Calling Quiz5q
- Integrate Agent Tools
- Integrate Agent Tools Quiz5q
- Multi-Agent Orchestration
- Multi-Agent Orchestration Quiz5q
- Autonomous Workflows with Safeguards
- Autonomous Workflows with Safeguards Quiz5q
- Agent Monitoring and Evaluation
- Agent Monitoring and Evaluation Quiz5q
- Prompt Engineering Techniques
- Prompt Engineering Techniques Quiz5q
- Model Parameters Tuning
- Model Parameters Tuning Quiz5q
- Chain of Thought and Self-Critique
- Chain of Thought and Self-Critique Quiz5q
- Tracing and Observability
- Tracing and Observability Quiz5q
- Hybrid LLM and Rules Engines
- Hybrid LLM and Rules Engines Quiz5q
- Azure Content Understanding
- Azure Content Understanding Quiz5q
- OCR and Layout Analysis
- OCR and Layout Analysis Quiz5q
- Field Extraction
- Field Extraction Quiz5q
- Structured and Markdown Outputs
- Structured and Markdown Outputs Quiz5q
- Extract from Images
- Extract from Images Quiz5q
- Extract from Audio and Video
- Extract from Audio and Video Quiz5q
- Content Understanding Analyzers
- Content Understanding Analyzers Quiz5q
- Form Processing Automation
- Form Processing Automation Quiz5q
- Invoice and Receipt Processing
- Invoice and Receipt Processing Quiz5q
- ID Document Processing
- ID Document Processing Quiz5q
- Custom Model Training
- Custom Model Training Quiz5q
- Batch Processing Documents
- Batch Processing Documents Quiz5q
- Error Handling in Extraction
- Error Handling in Extraction Quiz5q
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