Approach: Clarify scale and constraints, then design a RESTful API with regional endpoints and geo-replication using Azure Blob Storage and Cosmos DB.

Considerations: Balance consistency models by use case. Handle network failures with retry logic. Ensure cost-efficiency across regions while maintaining performance SLAs.

Key Points: Use Traffic Manager for geo-routing, ETag versioning for concurrency, blob tiering for cost optimization, and design for 99.9% uptime with cross-region failover.

Approach: Use a hash map to count frequencies, then build a min-heap of size k to track k largest frequencies. Returns elements in O(n log k) time.

Considerations: Trade space vs time efficiency. Consider bucket-sort for O(n) time when k approaches array size. Validate edge cases: k=0, empty array, k larger than unique elements.

Key Points: Optimal: O(n log k) time, O(n) space. Use min-heap to maintain k elements, pop smallest when exceeding k. Handle ties consistently. Verify with array=[1,1,1,2,2,3], k=2.

Approach: Implement distributed tracing with Application Insights to identify bottlenecks. Isolate whether issue is network I/O, database queries, or CPU-bound operations using metrics and logs.

Considerations: Distinguish systemic issues (connection pooling, cascading failures) from transient spikes. Balance observability cost against operational overhead. Prioritize service instrumentation by traffic volume.

Key Points: Use Azure Monitor, Application Insights, correlation IDs for request tracing. Common culprits: N+1 queries, missing indexes, inefficient serialization, connection pool exhaustion. Implement circuit breakers to prevent cascades.