Unlock PHP Magic: How to Take Input from User in PHP Easily
9 Aug 2025
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Operate & Monitor (OM)
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.
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.
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.
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.
Explain the value of converting manual Runbooks (troubleshooting guides) into automated scripts (e.g., Ansible Playbooks). Provide an example of a manual task (e.g., checking log files) that is now automated by the runbook.
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 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.
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 monitoring strategy encompassing three layers: Application, Infrastructure, and Business Metrics. For each layer, specify one key metric (e.g., CPU utilization, 95th percentile latency, conversion rate) and the ideal tool to track it.
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.
Explain the concept of AIOps in the context of IT operations. Provide one example of how machine learning (ML) could be applied to log data to reduce 'alert fatigue' and identify root causes faster.
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