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Operate & Monitor (OM)
Distinguish between White-Box Monitoring (internal metrics) and Black-Box Monitoring (external behavior). Provide one example of a key metric derived from each type of monitoring for a web service.
For a high-traffic e-commerce checkout service, define appropriate Service Level Indicators (SLIs) for latency and error rate. Based on these, propose a 3-tier Service Level Objective (SLO) alerting system (Warning, Critical, Pager Duty) with specific thresholds.
Explain the concept and necessity of Distributed Tracing in a microservices architecture. Specify the data fields required in a trace span (e.g., span ID, service name) and how tracing helps a DevOps engineer debug cross-service latency issues.
Outline a plan for implementing Synthetic Monitoring (active monitoring) for a critical API endpoint. Detail the required parameters (e.g., request frequency, expected response code/body) and the purpose of using synthetic checks over passive monitoring.
Design a comprehensive Health Check Endpoint (/healthz) for an application that is part of an autoscaling group. List 5 key internal and external checks (e.g., database connectivity, external API status) that the endpoint must report to the load balancer/autoscaler.
Distinguish between Syntactic Monitoring (e.g., PING checks) and Semantic Monitoring (e.g., transaction testing). Provide an example of a failure that would be missed by a syntactic check but caught by a semantic check.
Define the relationship between SLI (Indicator), SLO (Objective), and SLA (Agreement) for a software service. Provide a specific example of each for a customer-facing login service, and explain the consequence of breaching the SLA.
Explain why monitoring cost/billing is a critical operations metric in a serverless environment (e.g., AWS Lambda, Google Cloud Functions). Identify 3 metrics (e.g., invocation count, memory usage, execution duration) that directly impact cost and should be alerted on.
Design a strategy for organizing monitoring dashboards. Propose 3 distinct types of dashboards (e.g., Executive/Business, Troubleshooting/Deep Dive, Team-Specific) and list the primary audience and goal for each type.
Describe the process of configuring a Metrics Scraper (e.g., Prometheus) to collect data from a new microservice. Detail the specific format (e.g., Prometheus Exposition Format) that the application must expose at a dedicated endpoint (/metrics) for successful data ingestion.
Explain the necessity of Log Sampling in a high-volume microservices environment (e.g., millions of events per second). Detail a strategy where error logs are always retained, but debug logs are sampled at a rate of 1:100 to reduce ingestion costs.
Design an automated process using a log analysis tool (e.g., Splunk) to automatically identify and categorize the Top 5 Recurring Error Messages from the past 24 hours. Specify how this data should be presented to the development team for daily review.
Design a log aggregation strategy that prioritizes the delivery of critical error logs over debug logs to the central logging platform. Specify the component (e.g., log forwarder, queue) responsible for this prioritization.
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