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Monitoring and Improving AI Solutions Flashcards
Microsoft Azure AI Engineer Associate AI-102 Flashcards
Study our Monitoring and Improving AI Solutions flashcards for the Microsoft Azure AI Engineer Associate AI-102 exam with 30+ flashcards. View as flashcards, a searchable table, or as a fun matching game.
| 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 |
About the Flashcards
Flashcards for the Microsoft Azure AI Engineer Associate exam guide you through monitoring and optimizing AI workloads on Microsoft Azure. Examine how Azure Monitor, Application Insights, Metrics, and Dashboards gather telemetry, trigger alerts, and surface insights, enabling real-time tracking of usage, latency, and resource utilization. Learn to choose logs or metrics appropriately and configure autoscaling so services meet shifting demand without waste.
The deck also reinforces anomaly detection, model drift, KPIs, and root-cause analysis to keep predictions accurate and dependable. Practice feedback loops, predictive analytics, scalability testing, and container monitoring while exploring queue lengths, dependency tracking, and load testing. Compact cards strengthen the terminology and best practices you need for exam day success.
Topics covered in this flashcard deck:
- Azure monitoring tools
- Telemetry & metrics
- Anomaly & drift detection
- Performance KPIs
- Scaling & load testing
- Root cause analysis