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Feature Flags Risk, Monitoring & Rollback Survey Template

Assess risk tolerance, monitoring coverage, and rollback readiness for feature flags. Ideal for engineers and PMs. Launch in minutes; actionable insights fast.

What's Included

AI-Powered Questions

Intelligent follow-up questions based on responses

Automated Analysis

Real-time sentiment and insight detection

Smart Distribution

Target the right audience automatically

Detailed Reports

Comprehensive insights and recommendations

Sample Survey Items

Q1
Multiple Choice
Does your team currently use feature flags or similar runtime toggles?
  • Yes
  • No
Q2
Opinion Scale
Overall, how much rollout risk is your team comfortable taking on for typical changes protected by a safety mechanism (e.g., feature flag, canary)?
Range: 1 10
Min: Very risk-averseMid: BalancedMax: Comfortable with risk
Q3
Dropdown
For a typical flagged change, what impact level would trigger a rollback?
  • 0.1% of active users
  • 0.5%
  • 1%
  • 2%
  • 5%
  • More than 5%
  • Not sure
Q4
Opinion Scale
How confident are you that monitoring/alerting would detect a problematic flagged change within about 10 minutes?
Range: 1 10
Min: Not confidentMid: Moderately confidentMax: Very confident
Q5
Matrix
How well are the following signals monitored for changes behind flags/toggles?
RowsNot monitoredLightly monitoredAdequateWell-instrumentedAutomated detection
Error rates (e.g., 5xx, exceptions)
Latency/response time
Resource usage (CPU, memory)
User behavior (clicks, funnels)
Business KPIs (conversions, revenue)
Feature-level logs/trace attributes
Q6
Long Text
If confidence is low, what are the main monitoring or alerting gaps to address?
Max 600 chars
Q7
Multiple Choice
Attention check: For quality control, please select “No” for this question.
  • Yes
  • No
Q8
Rating
How confident are you that you can roll back or disable a problematic change quickly?
Scale: 10 (star)
Min: Not confidentMax: Fully confident
Q9
Dropdown
Typical time from deciding to roll back to the change being fully reverted or disabled:
  • <1 minute
  • 1–5 minutes
  • 6–15 minutes
  • 16–30 minutes
  • More than 30 minutes
  • Not sure
Q10
Long Text
Anything else about how your team thinks about risk, monitoring, or rollback for flagged changes?
Max 600 chars
Q11
Multiple Choice
What is your primary role?
  • Backend engineer
  • Frontend/Web engineer
  • Mobile engineer
  • DevOps/SRE
  • Data/ML
  • QA/Testing
  • Product manager
  • Engineering manager
  • Other
  • Prefer not to say
Q12
Dropdown
Years of professional experience:
  • 0–1
  • 2–4
  • 5–9
  • 10–14
  • 15+
  • Prefer not to say
Q13
Dropdown
Company size (employees):
  • 1–10
  • 11–50
  • 51–200
  • 201–1,000
  • 1,001–5,000
  • 5,001–10,000
  • 10,001+
  • Prefer not to say
Q14
Dropdown
Primary industry:
  • Software/SaaS
  • E-commerce
  • Fintech/Financial services
  • Media/Entertainment
  • Healthcare
  • Telecom
  • Gaming
  • Other
  • Prefer not to say
Q15
Dropdown
Region:
  • North America
  • Europe
  • APAC
  • LATAM
  • Middle East
  • Africa
  • Prefer not to say
Q16
Chat Message
Welcome! This short survey asks about how your team handles risk, monitoring, and rollback for changes behind flags/toggles. Please answer based on your current practice.
Q17
Long Text
Optional: Any additional context or examples you’d like to share about your team’s practices?
Max 600 chars
Q18
AI Interview
AI Interview: 2 Follow-up Questions on risk, monitoring, and rollback
AI InterviewLength: 2Personality: Expert InterviewerMode: Fast
Q19
Chat Message
Thanks for your time—your insights are appreciated!

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