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Monitoring and Improving AI Solutions Flashcards
Microsoft Azure AI Engineer Associate AI-102 Flashcards
| Front | Back |
| How can API response times for AI solutions be improved | By optimizing code, infrastructure, or decreasing model complexity |
| How can data visualization improve AI monitoring | By making complex trends and patterns easily understandable |
| How can dependency tracking aid AI solution monitoring | By identifying issues with external systems or services that impact performance |
| How can feedback loops contribute to the improvement of AI solutions | By leveraging user input for retraining and refining models |
| How can monitoring resource utilization improve AI performance | By identifying inefficient use of CPU, GPU, or memory to optimize infrastructure |
| How can predictive analytics improve AI monitoring strategies | By forecasting trends and preventing issues before they occur |
| How can you track the usage of an AI solution in Azure | By using Azure Application Insights |
| How can you use Azure Machine Learning to improve your AI solution | By retraining machine learning models with new data to optimize performance |
| What are Alerts in Azure Monitor used for | To notify or take action when specific thresholds are met |
| What does the term "root cause analysis" mean in the context of troubleshooting AI | The process of identifying the fundamental issue causing a system failure or inefficient performance |
| What is the benefit of monitoring model versioning | To track changes and ensure deployment of the most effective model |
| What is the benefit of using real-time analytics for AI monitoring | To quickly identify and address emerging issues |
| What is the function of Azure Advisor for AI solutions | To provide recommendations on optimizing resources and performance |
| What is the importance of anomaly detection in AI monitoring | To identify unexpected behaviors that may indicate issues or areas of improvement |
| What is the importance of monitoring data input quality | To ensure accurate predictions and prevent garbage-in, garbage-out issues |
| What is the purpose of autoscaling in AI solutions | To dynamically adjust resources based on workload demand |
| What is the purpose of Azure Monitor | To collect, analyze, and act on telemetry data from cloud and on-premises environments |
| What is the purpose of load testing in AI solution monitoring | To evaluate system stability and performance under high demand scenarios |
| What is the role of data sampling in improving AI monitoring efficiency | To reduce the volume of telemetry data without losing critical insights |
| What is the role of logs in monitoring AI performance | To provide detailed information for troubleshooting and performance evaluation |
| What is the role of synthetic transactions in monitoring | To proactively test application performance by simulating user actions |
| What is the significance of pending queue lengths in AI monitoring | It helps identify bottlenecks in data processing and request handling |
| What is the significance of scalability testing in AI solutions | To ensure the solution can handle increased workloads without performance degradation |
| What is the value of tracking user behavior in AI solutions | To identify patterns and improve user-centric design and model performance |
| What role does container monitoring play in AI deployments | To ensure containerized applications are running efficiently and securely |
| When should you use Azure Metrics instead of logs | When you need near real-time data for analyzing performance trends |
| Which Azure tool helps visualize data for AI solution monitoring | Azure Dashboards |
| Why are Key Performance Indicators (KPIs) critical in AI monitoring | They measure performance against objectives and help prioritize improvements |
| Why is model drift monitoring important | To detect when a model's predictions no longer align with real-world data |
| Why should you monitor latency in AI inference | To detect delays that may affect user experience and application responsiveness |
This deck emphasizes the tools and techniques for monitoring, analyzing, and improving the performance of AI solutions deployed in Azure.