Observability in Cloud (Logs, Metrics, Tracing) Questions and Answers for Viva

Answer-1: Observability is the ability to measure a system's internal states by collecting and analyzing logs, metrics, and traces.

Answer-2: It helps detect, diagnose, and resolve issues quickly in complex, dynamic cloud systems.

Answer-3: Logs, metrics, and tracing.

Answer-4: Logs are time-stamped, unstructured or structured records of discrete events occurring in a system.

Answer-5: Metrics are numerical measurements that represent system performance or resource usage over time.

Answer-6: Tracing captures the flow of requests across distributed systems to diagnose latency and bottlenecks.

Answer-7: Logs record discrete events with details, while metrics provide aggregated numerical data.

Answer-8: Tracking the entire lifecycle of a request as it travels through multiple services or microservices.

Answer-9: Prometheus, Grafana, ELK Stack, Jaeger, Zipkin, Datadog, AWS CloudWatch.

Answer-10: They provide trends and thresholds that can trigger alerts before failures occur.

Answer-11: Logs provide detailed context and error messages for identifying root causes of issues.

Answer-12: By showing the exact path and timing of requests across services to find bottlenecks.

Answer-13: Logs formatted in a consistent structure like JSON for easier querying and analysis.

Answer-14: A database optimized to store and query metrics data indexed by time.

Answer-15: Cloud-native focuses on dynamic, distributed, and ephemeral resources with real-time insights.

Answer-16: Critical metrics that measure application health, like latency, error rates, and throughput.

Answer-17: They uniquely identify requests across services to link related logs and traces.

Answer-18: Metrics like CPU or memory usage trigger scaling policies based on load.

Answer-19: Collecting logs from multiple sources into a centralized system for analysis.

Answer-20: To reduce overhead by collecting only a subset of traces while still gaining insights.

Answer-21: By detecting anomalies, unauthorized access, and unusual patterns through logs and metrics.

Answer-22: An open-source initiative to standardize collection of telemetry data like logs, metrics, and traces.

Answer-23: Short-lived functions produce ephemeral logs and metrics, requiring specialized collection tools.

Answer-24: They visualize logs, metrics, and traces for quick status overview and anomaly detection.

Answer-25: A condition on metrics or logs that triggers notifications when thresholds are crossed.

Answer-26: It enables faster feedback loops, continuous monitoring, and better incident response.

Answer-27: CPU usage, memory usage, request latency, error rates, throughput.

Answer-28: Monitoring tracks known metrics to check health, while observability provides insight to understand unknown issues.

Answer-29: By encrypting logs at rest and in transit, and managing access controls.

Answer-30: They measure response times to identify slow or failing components.

Answer-31: They add context to metrics to enable filtering and aggregation by dimensions like service or region.

Answer-32: Recording notable occurrences in a system that may affect operation or performance.

Answer-33: Using log aggregation, compression, retention policies, and indexing.

Answer-34: A single unit of work within a trace representing an operation or request segment.

Answer-35: By linking logs with matching timestamps or correlation IDs to metric anomalies.

Answer-36: Automatically identifying abnormal patterns in metrics or logs that deviate from normal behavior.

Answer-37: Simplified analysis, improved searchability, and unified alerting.

Answer-38: By providing historical data trends to forecast future resource needs.

Answer-39: It allows immediate detection and response to issues as they occur.

Answer-40: By providing built-in telemetry, integration, and scalability.

Answer-41: Black-box observes external behavior, white-box collects internal system data.

Answer-42: Using agents or sidecars to capture telemetry before resource termination.

Answer-43: It increases storage and query complexity due to many unique label combinations.

Answer-44: By revealing slow components and inefficient call paths.

Answer-45: They balance storage costs with compliance and troubleshooting needs.

Answer-46: By monitoring and alerting on performance targets defined in SLAs.

Answer-47: It controls the percentage of requests traced to balance insight and overhead.

Answer-48: Handling large data volumes, ensuring low overhead, and correlating disparate data sources.

Answer-49: For anomaly detection, root cause analysis, and predictive monitoring.

Answer-50: More automation, AI-driven insights, unified telemetry standards, and deeper integration with DevOps tools.