Monitoring Tools and Observability Questions

Covers hands on familiarity with modern monitoring and observability platforms and the practices for instrumenting and operating production systems. Candidates should be able to describe one or more tools such as Prometheus, Grafana, Datadog, CloudWatch, and explain how to write queries, design dashboards, and configure alerts. Include understanding of metrics collection, time series databases, log aggregation, distributed tracing, and common query languages used by these platforms. Also cover integrating monitoring with incident management systems such as PagerDuty and Opsgenie, defining service level indicators and objectives, setting alerting thresholds to reduce noise, and using dashboards and alerts to troubleshoot performance and availability issues.

EasyTechnical

0 practiced

As a Solutions Architect, explain the differences between metrics, logs, and distributed traces in the context of observability. For each data type provide: (a) a concise definition, (b) two real-world example use-cases (one for detection, one for debugging), and (c) one storage/query trade-off you would communicate to a non‑technical stakeholder.

EasyTechnical

0 practiced

A client asks you to estimate monthly monitoring costs. List the quantitative inputs you need to produce a reliable estimate (e.g., number of hosts, metrics per host, custom metric cardinality, logs events/sec, average span size and spans per request, retention windows) and explain briefly how each factor drives ingestion and storage costs.

HardSystem Design

0 practiced

Design an observability architecture for a regulated healthcare customer: telemetry must remain in-region, logs retained for 7 years, access to telemetry is strictly controlled, and query performance must be acceptable for audits. Propose components, storage-tiering, encryption, access controls, and approaches to enable aggregated cross-region reporting without moving raw telemetry data across borders.

HardTechnical

0 practiced

A client wants to detect novel performance regressions automatically (unknown patterns). Propose an approach using open-source tools: discuss feature selection from metrics, statistical vs ML models (e.g., ARIMA, isolation forest, LSTM), training/labeling strategy, alert integration, and how you'd limit false positives operationally.

MediumTechnical

0 practiced

Design a log retention and storage-tiering plan to meet a 90-day compliance requirement while minimizing cost. Include hot/warm/cold tiers, indexing vs raw retention, compression and parsing trade-offs, and example filters to significantly reduce ingested log volume without losing forensic capability.

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