Monitoring and observability in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding the difference between monitoring and observability

Which statement best describes the difference between monitoring and observability in a software system?

A Monitoring collects predefined metrics and alerts on known issues, while observability enables understanding unknown issues by analyzing logs, metrics, and traces.

B Monitoring is only about logging errors, and observability is about fixing bugs automatically.

C Monitoring and observability are the same; both only collect logs for debugging.

D Observability is a tool, and monitoring is a process that runs inside it.

💡 Hint

Think about how each helps with known vs unknown problems.

Monitoring focuses on collecting specific metrics and alerting on known issues. Observability provides deeper insight by combining logs, metrics, and traces to understand unknown problems.

💻 Command Output

intermediate

Prometheus query output interpretation

Given the Prometheus query rate(http_requests_total[5m]), what does the output represent?

A The number of HTTP requests in the last 5 seconds.

B The total number of HTTP requests since the server started.

C The average number of HTTP requests per second over the last 5 minutes.

D The current number of active HTTP connections.

💡 Hint

Consider what rate() function calculates in Prometheus.

The rate() function calculates the per-second average increase of a counter over the specified time window, here 5 minutes.

❓ Configuration

advanced

Configuring alert rules in Prometheus

Which Prometheus alert rule configuration will correctly trigger an alert when CPU usage exceeds 80% for 5 minutes?

Prompt Engineering / GenAI

groups:
- name: cpu_alerts
  rules:
  - alert: HighCPUUsage
    expr: avg(rate(cpu_seconds_total[1m])) by (instance) > 0.8
    for: 5m
    labels:
      severity: critical
    annotations:
      summary: "CPU usage is above 80%"
      description: "Instance {{ $labels.instance }} CPU usage is above 80% for more than 5 minutes."

A

expr: avg_over_time(cpu_seconds_total[5m]) > 0.8
for: 1m

B

expr: sum(cpu_seconds_total) by (instance) > 80
for: 5m

C

expr: rate(cpu_seconds_total[5m]) > 0.8
for: 5m

D

expr: avg(rate(cpu_seconds_total[1m])) by (instance) > 0.8
for: 5m

💡 Hint

Look for the expression that calculates CPU usage rate averaged per instance over a short interval and checks if it exceeds 0.8 (80%).

Option D uses avg(rate(cpu_seconds_total[1m])) by (instance) which calculates the average CPU usage rate per instance over 1 minute, then alerts if above 0.8 for 5 minutes.

❓ Troubleshoot

advanced

Diagnosing missing logs in a distributed tracing system

You notice that some traces in your distributed tracing system are missing logs from certain services. What is the most likely cause?

A The logs are stored but the dashboard is not refreshing automatically.

B The services are not instrumented properly to send logs to the tracing system.

C The network is blocking all traffic except metrics data.

D The tracing system is down and cannot receive any data.

💡 Hint

Think about what is required for logs to appear in traces from each service.

If services are not instrumented correctly, they won't send logs to the tracing system, causing missing logs in traces.

🔀 Workflow

expert

Order the steps to implement observability in a new microservices project

Arrange the following steps in the correct order to implement observability effectively in a new microservices project.

A 2,1,3,4

B 1,2,3,4

C 2,3,1,4

D 3,2,1,4

💡 Hint

Start by defining goals, then instrument, then collect data, then alert.

First define KPIs and SLOs to know what to measure, then instrument services, set up collection platform, and finally create alerts.