AI Incident Response Platforms for On-Call

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-tools-AI-incident-response-platforms-creators

1. Which metric does AI specifically help reduce in incident response platforms?

   1. User Experience Score (UES)
   2. Total Cost of Ownership (TCO)
   3. Mean Time To Resolve (MTTR)
   4. Mean Time Between Failures (MTBF)

2. What can AI automatically generate from incident chat logs and system data?

   1. Incident summaries
   2. Severity classifications
   3. Security patches
   4. Budget forecasts

3. What does 'similar-incident recall' refer to in AI IR platforms?

   1. The rate at which incidents decrease over time
   2. The process of alerting all team members simultaneously
   3. The time it takes for the platform to send alert notifications
   4. The system's ability to find past incidents that match current symptoms

4. A student suggests using AI to determine the severity level of a completely new type of incident. Why might this be problematic?

   1. AI always overestimates severity to be safe
   2. Novel incidents are automatically low severity
   3. AI severity decisions require manual approval anyway
   4. AI cannot reliably assess severity for novel incident types it has never seen before

5. What is the primary risk if a hallucinated detail enters an AI-generated postmortem?

   1. The inaccurate detail can propagate through documentation for years
   2. Legal liability is immediately assigned
   3. The incident will be marked as resolved
   4. The platform will automatically trigger a rollback

6. Why must humans perform an edit-pass before publishing AI-generated postmortems?

   1. To catch and correct any AI hallucinations before they become permanent
   2. To comply with marketing requirements
   3. To verify the platform's uptime during the incident
   4. To increase the word count of the document

7. What are 'policy guardrails' in the context of AI-drafted customer communications?

   1. Rate limits on how many messages can be sent
   2. Constraints that prevent AI from generating content that violates company policy
   3. Physical barriers around data centers
   4. Approval workflows requiring multiple sign-offs

8. Which of the following is a capability that AI performs WELL in incident response platforms?

   1. Suggesting similar past incidents for runbook reuse
   2. Deciding severity for completely new incident types
   3. Replacing human judgment in all high-stakes communications
   4. Automatically deploying fixes without human approval

9. A team evaluates an AI IR platform. They score it on AI summary quality, similar-incident recall, customer-comms safety, integration, and cost. What evaluation method are they using?

   1. A competitive bidding evaluation
   2. A pass-fail certification process
   3. A customer satisfaction survey
   4. A five-criteria scoring framework with 1-5 ratings per criterion

10. Which platform was NOT mentioned as an example of an AI-enabled incident response tool in the lesson?

    1. Datadog
    2. PagerDuty AI
    3. incident.io
    4. Rootly AI

11. What does 'runbook reuse' mean in the context of AI incident response?

    1. Applying proven resolution procedures from past incidents to new similar incidents
    2. Using the same on-call rotation schedule repeatedly
    3. Reusing the same incident ticket number across multiple events
    4. Sharing incident documentation with competitor companies

12. When evaluating AI customer-comms safety, what is being assessed?

    1. Whether AI-generated messages comply with policy and avoid harmful content
    2. The number of customers who receive updates
    3. The speed at which customer notifications are delivered
    4. The cost of sending customer communications

13. What is a postmortem in incident response terminology?

    1. A summary sent to customers after an incident
    2. A document analyzing what went wrong, why, and how to prevent recurrence
    3. A financial report of incident costs
    4. A funeral ceremony for failed servers

14. Why is 'integration with our tools' an important evaluation criterion for AI IR platforms?

    1. Integration is required by law for all software purchases
    2. Integration determines the platform's color scheme
    3. The AI must connect with existing systems like monitoring, logging, and communication tools
    4. AI cannot function without integrating social media accounts

15. What is the main reason AI-drafted customer communications still require human review?

    1. To ensure accuracy and appropriate tone before external release
    2. To meet regulatory requirements for handwritten signatures
    3. To allow the AI to learn from the edits
    4. To increase the cost of the communications process