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
Template Overview
19
Questions
AI-Powered
Smart Analysis
Ready-to-Use
Launch in Minutes
This professionally designed survey template helps you gather valuable insights with intelligent question flow and automated analysis.
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?
Rows
Not monitored
Lightly monitored
Adequate
Well-instrumented
Automated 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: 11 (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: [Object Object]Mode: Fast
Q19
Chat Message
Thanks for your time—your insights are appreciated!
Frequently Asked Questions
What is QuestionPunk?
QuestionPunk is a lightweight survey platform for live AI interviews you control. It's fast, flexible, and scalable—adapting every question in real time, moderating responses across languages, letting you steer prompts, models, and flows, and even generating surveys from a simple prompt. Get interview-grade insight with survey-level speed across qual and quant.
How do I create my first survey?
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Can I export survey results?
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