InterviewCloud
Azure AI, data, MLOps, deployment, and enterprise interview scenarios.
Azure OpenAI wraps OpenAI models with Azure enterprise controls, networking, identity, and regional deployment options.
Strong answers mention Entra ID, private networking, Azure compliance posture, deployment names, quota management, content filtering, and integration with Azure AI Search.
Use Azure Machine Learning or containerized services with model registry, endpoint monitoring, and rollout strategy.
Cover model registry, managed online endpoints, ACR images, Application Insights, autoscaling, blue-green deployment, and data drift monitoring.
Use ingestion, storage, transformation, orchestration, quality checks, and serving layers.
Common services include Data Factory, ADLS, Databricks or Synapse, Event Hubs, Purview, and Power BI. Interviewers care about reliability, lineage, and cost.
Use Functions for event-driven serverless tasks and AKS for long-running, complex, or Kubernetes-native services.
Compare cold starts, runtime limits, scaling control, networking, operational overhead, and team Kubernetes maturity.
AI-102 and DP-203 are useful for AI/data roles; AZ-104 or AZ-305 helps architecture depth.
Use cert paths to guide topics, but build projects around Azure OpenAI, AI Search, ADLS, and deployment monitoring for interview signal.