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