Monitoring and alerting in production in LangChain - Practice Problems & Coding Challenges

Challenge - 5 Problems

💻 Command Output

intermediate

What is the output of this Prometheus query?

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

LangChain

rate(http_requests_total[5m])

A The current number of HTTP requests being processed.

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

C The maximum number of HTTP requests in any 5-minute window.

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

💡 Hint

Think about what the 'rate' function calculates over a time range.

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

🧠 Conceptual

intermediate

Which alerting condition triggers a PagerDuty notification?

You have an alert rule: IF cpu_usage > 90% FOR 10m THEN alert. Which option best describes when PagerDuty will be notified?

A PagerDuty is notified immediately when CPU usage exceeds 90%.

B PagerDuty is notified only if CPU usage stays above 90% continuously for 10 minutes.

C PagerDuty is notified if CPU usage exceeds 90% at any point during 10 minutes, even briefly.

D PagerDuty is notified after CPU usage drops below 90% following a spike.

💡 Hint

Consider the meaning of the 'FOR 10m' clause in alert rules.

The 'FOR 10m' means the condition must be true continuously for 10 minutes before triggering the alert and notification.

❓ Troubleshoot

advanced

Why does this Grafana alert never fire?

You created a Grafana alert with this query: memory_usage[5m] > 80. The alert never fires even when memory usage is high. What is the likely cause?

LangChain

memory_usage[5m] > 80

A The <code>memory_usage[5m]</code> selector returns a range vector, which cannot be directly compared to a scalar.

B The alert condition compares a vector to a scalar without proper aggregation.

C The query uses a wrong function; it should be <code>avg(memory_usage) > 80</code>.

D The threshold 80 is too high; memory usage never reaches that value.

💡 Hint

Check the data types returned by Prometheus functions and how comparisons work.

memory_usage[5m] returns a range vector, but alert conditions require instant vectors for comparison to scalars.

🔀 Workflow

advanced

Order the steps to set up a basic alerting pipeline

Arrange these steps in the correct order to set up monitoring and alerting for a web service.

A 1,2,4,3

B 4,1,2,3

C 1,4,2,3

D 4,2,1,3

💡 Hint

Think about what must be ready before alert rules and notifications.

First, metrics must be exposed by the service (1), then the monitoring system must collect them (4), then alert rules are defined (2), and finally notifications are set up (3).

✅ Best Practice

expert

Which practice improves alert reliability and reduces noise?

You notice many alerts firing for brief spikes in CPU usage. Which practice best reduces false alerts without missing real issues?

A Use alert 'for' duration to require condition to persist before firing.

B Set alert thresholds very high to avoid triggering on spikes.

C Disable alerts during peak traffic hours to reduce noise.

D Send alerts to multiple channels to ensure visibility.

💡 Hint

Consider how to avoid alerts on short-lived spikes.

Using an alert 'for' duration means the condition must be true continuously for a set time, reducing noise from brief spikes.