Monitoring and observability in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is monitoring in DevOps?

Monitoring is the process of continuously checking the health and performance of systems using tools that collect and display data like CPU usage, memory, and errors.

intermediate

Define observability and how it differs from monitoring.

Observability is the ability to understand the internal state of a system by analyzing its outputs like logs, metrics, and traces. Unlike monitoring, it helps find unknown problems, not just known issues.

beginner

Name the three main types of data used in observability.

The three main types are:
1. Metrics - numbers showing system performance
2. Logs - detailed event records
3. Traces - records of requests moving through services

beginner

Why is alerting important in monitoring?

Alerting notifies the team immediately when something goes wrong, so they can fix issues before users notice problems.

beginner

Give an example of a popular monitoring tool and one observability tool.

Monitoring tool example: Prometheus
Observability tool example: Jaeger (for tracing) or Grafana (for visualization)

Which data type in observability shows the path of a request through multiple services?

A Alerts

B Logs

C Metrics

D Traces

Traces record the journey of a request across different parts of a system.

What is the main goal of monitoring?

A To fix bugs in code

B To continuously check system health and performance

C To deploy new software versions

D To write documentation

Monitoring focuses on tracking system health and performance metrics.

Which of these is NOT a typical observability data type?

A Backups

B Metrics

C Traces

D Logs

Backups are not part of observability data; they are copies of data for recovery.

Alerting helps teams by:

A Automatically fixing issues

B Backing up data

C Notifying when problems occur

D Writing code

Alerting sends notifications so teams can respond quickly to issues.

Which tool is commonly used for monitoring metrics?

A Prometheus

B Jaeger

C Git

D Docker

Prometheus is a popular tool for collecting and querying metrics.

Explain in your own words the difference between monitoring and observability.

Think about how monitoring is like watching a dashboard, while observability is like investigating a mystery.

List and describe the three main types of data used in observability.

Imagine metrics as speedometers, logs as diaries, and traces as travel maps.

Practice

1. What is the main purpose of monitoring in a software system?

easy

A. To check if the system is working right now

B. To predict future system failures

C. To change system configurations automatically

D. To write new features for the system

Solution

1. Step 1: Understand monitoring's role

   Monitoring is about checking the current state of the system to see if it is working properly.

2. Step 2: Compare options to definition

   Only To check if the system is working right now matches this purpose. Other options describe different activities like prediction, automation, or development.

3. Final Answer:

   To check if the system is working right now -> Option A

4. Quick Check:

   Monitoring = check current system state [OK]

Hint: Monitoring = check system now, not future or changes [OK]

Common Mistakes:

• Confusing monitoring with observability
• Thinking monitoring predicts future issues
• Assuming monitoring changes system behavior

2. Which of the following is a correct example of a monitoring tool?

easy

A. Visual Studio Code

B. Prometheus

C. Dockerfile

D. GitHub

Solution

1. Step 1: Identify monitoring tools

   Prometheus is a popular open-source monitoring tool used to collect and query metrics.

2. Step 2: Check other options

   GitHub is for code hosting, Dockerfile is for container setup, and Visual Studio Code is a code editor, none are monitoring tools.

3. Final Answer:

   Prometheus -> Option B

4. Quick Check:

   Prometheus = monitoring tool [OK]

Hint: Prometheus is a classic monitoring tool name [OK]

Common Mistakes:

• Confusing code tools with monitoring tools
• Thinking Dockerfile is a monitoring tool
• Mixing development tools with monitoring

3. Given this Prometheus query: up{job="api-server"} == 1, what does it show?

medium

A. The total number of api-server jobs

B. All api-server jobs that are down

C. All api-server jobs that are currently up (running)

D. The CPU usage of api-server jobs

Solution

1. Step 1: Understand the query meaning

   The metric up is 1 when a target is up (running), 0 if down. The filter {job="api-server"} selects only api-server jobs.

2. Step 2: Interpret the comparison

   The query checks where up == 1, so it shows api-server jobs currently running.

3. Final Answer:

   All api-server jobs that are currently up (running) -> Option C

4. Quick Check:

   up == 1 means running targets [OK]

Hint: up == 1 means service is running [OK]

Common Mistakes:

• Thinking up == 1 means down
• Confusing metric with count
• Assuming it shows CPU usage

4. You see this error in your monitoring setup: error parsing query: unexpected token. What is the most likely cause?

medium

A. Server hardware failure

B. Network failure between server and client

C. Monitoring tool is not installed

D. Syntax error in the query expression

Solution

1. Step 1: Analyze the error message

   The message says "error parsing query" and "unexpected token", which means the query syntax is wrong.

2. Step 2: Rule out other causes

   Network failure, missing tool, or hardware failure would cause different errors, not parsing errors.

3. Final Answer:

   Syntax error in the query expression -> Option D

4. Quick Check:

   Parsing error = syntax mistake [OK]

Hint: Parsing errors mean syntax mistakes in queries [OK]

Common Mistakes:

• Assuming network or hardware issues cause parsing errors
• Ignoring the error message details
• Thinking the tool is missing

5. You want to improve observability by adding tracing to your microservices. Which approach best helps you understand why requests fail inside your system?

hard

A. Use distributed tracing to follow requests across services

B. Add more CPU and memory to servers

C. Increase the frequency of monitoring alerts

D. Write more unit tests for each service

Solution

1. Step 1: Understand observability and tracing

   Observability helps explain why things happen. Distributed tracing tracks requests across services to find where failures occur.

2. Step 2: Evaluate options for observability

   Adding resources or alerts or tests does not directly show why requests fail inside the system.

3. Final Answer:

   Use distributed tracing to follow requests across services -> Option A

4. Quick Check:

   Tracing = understand request flow and failures [OK]

Hint: Tracing shows request path and failure reasons [OK]

Common Mistakes:

• Confusing monitoring alerts with observability
• Thinking hardware upgrades improve observability
• Assuming tests replace tracing