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Feature Flag Risk & Rollback Readiness Assessment

Measures engineering teams' risk tolerance, monitoring confidence, and rollback preparedness for feature-flagged deployments. Designed for engineers, SREs, and product managers managing progressive rollouts.

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Sample questions

A preview of what’s in the template. Every question is editable before you launch.

20 questions · ~9 min
Q01
Message

Welcome! This survey explores how engineering teams manage risk, monitoring, and rollback for changes behind feature flags or similar toggles. Your participation is voluntary, and you may stop at any time. There are no right or wrong answers—we're interested in your honest experience. Responses are confidential and will be reported in aggregate only. Estimated time: 10 minutes.

Q02
Multiple Choice

Does your team currently use feature flags or similar runtime toggles (e.g., LaunchDarkly, Unleash, homegrown systems)?

  • Yes
  • No
Q03
Opinion Scale

For typical changes protected by a feature flag or canary, how much rollout risk is your team comfortable accepting?

Scale: 1 – 7
Min:Very risk-averseMax:Very comfortable with risk
Q04
Opinion Scale

How confident are you that your team's monitoring and alerting would detect a problematic flagged change within approximately 10 minutes?

Scale: 1 – 7
Min:Not at all confidentMax:Extremely confident
Q05
Opinion Scale

How confident are you that your team can roll back or disable a problematic flagged change quickly?

Scale: 1 – 7
Min:Not at all confidentMax:Extremely confident
Q06
Long Text

Based on your responses in this survey, please share any additional thoughts or reflections about how your team manages risk, monitoring, or rollback for flagged changes.

Q07
Multiple Choice

What is your primary role?

  • Backend engineer
  • Frontend/Web engineer
  • Mobile engineer
  • DevOps/SRE
  • Data/ML engineer
  • QA/Testing
  • Product manager
  • Engineering manager
  • Other
  • Prefer not to say
Q08
Message

Thank you for completing this survey! Your insights will help improve feature flag risk management practices. All responses are confidential and will be reported in aggregate only.

Q09
Dropdown

For a typical flagged change, what percentage of active users experiencing a negative impact would trigger a rollback decision?

  • 0.1% of active users
  • 0.5%
  • 1%
  • 2%
  • 5%
  • More than 5%
  • Not sure
Q10
Opinion Scale

<p>How well does your team monitor <strong>error rates and exceptions</strong> for changes behind feature flags?</p>

Scale: 1 – 5
Min:Not monitored at allMax:Fully monitored with automated alerts
Q11
Dropdown

What is the typical time from the decision to roll back a flagged change to it being fully reverted or disabled?

  • Less than 1 minute
  • 1–5 minutes
  • 6–15 minutes
  • 16–30 minutes
  • More than 30 minutes
  • Not sure
Q12
AI Interview

Thank you for your survey responses. I'd like to ask a couple of follow-up questions to better understand your team's approach to risk, monitoring, and rollback for flagged changes.

Q13
Dropdown

How many years of professional experience do you have?

  • 0–1
  • 2–4
  • 5–9
  • 10–14
  • 15+
  • Prefer not to say
Q14
Opinion Scale

<p>How well does your team monitor <strong>latency and performance metrics</strong> for changes behind feature flags?</p>

Scale: 1 – 5
Min:Not monitored at allMax:Fully monitored with automated alerts
Q15
Dropdown

Approximately how many employees work at your company?

  • 1–10
  • 11–50
  • 51–200
  • 201–1,000
  • 1,001–5,000
  • 5,001–10,000
  • 10,001+
  • Prefer not to say
Q16
Opinion Scale

<p>How well does your team monitor <strong>business/product metrics</strong> (e.g., conversion rates, revenue) for changes behind feature flags?</p>

Scale: 1 – 5
Min:Not monitored at allMax:Fully monitored with automated alerts
Q17
Dropdown

What is your company's primary industry?

  • Software/SaaS
  • E-commerce
  • Fintech/Financial services
  • Media/Entertainment
  • Healthcare
  • Telecom
  • Gaming
  • Other
  • Prefer not to say
Q18
Opinion Scale

<p>How well does your team monitor <strong>user-facing logs and anomalies</strong> for changes behind feature flags?</p>

Scale: 1 – 5
Min:Not monitored at allMax:Fully monitored with automated alerts
Q19
Dropdown

In which region are you primarily based?

  • North America
  • Europe
  • Asia-Pacific
  • Latin America
  • Middle East
  • Africa
  • Prefer not to say
Q20
Long Text

What, if any, gaps exist in your team's monitoring or alerting for flagged changes?

What’s included

  • AI follow-ups

    Adaptive probes on open-ended answers that pull out detail a static form would miss.

  • Attention checks

    Built-in safeguards against rushed answers and low-quality respondents.

  • AI-drafted copy

    Wording, ordering, and branching written by the AI — tuned to your research goal.

  • Auto report

    Themes, quotes, and a plain-English summary write themselves once responses come in.

How it compares

We reviewed the closest templates from other survey tools. Here’s what they do well — and where this template goes further.

Why this template

  • Purpose-built for feature-flagged deployments, with dedicated scales on rollout risk tolerance, rollback confidence, and typical decision-to-rollback time — not generic risk-management language.
  • Breaks monitoring confidence into four concrete signal types (error rates/exceptions, latency/performance, business metrics, user-facing logs/anomalies) plus an open-text question on monitoring gaps.
  • Includes an AI follow-up interview that adaptively probes each respondent's answers, something no static form builder can do.
  • Segments respondents by role, experience, company size, industry, and region, then auto-generates a report so engineering leaders can benchmark rollout risk posture across teams.

Typeform

Product Readiness Assessment by Smartbug Media

A fielding-ready Typeform template for gauging whether a product is ready to launch, built by agency partner Smartbug Media. It's readiness-assessment-adjacent but oriented toward go-to-market/product-launch criteria rather than engineering concerns like feature flags, canary rollouts, or rollback time. Good for a quick, polished readiness check, not a deep operational risk audit.

What it does well

  • Typeform's signature clean, conversational question flow likely drives higher completion rates than a plain form
  • Built as a plug-and-play template so teams can deploy it immediately without survey-design work
  • Backed by an agency (Smartbug Media) suggesting it reflects a proven GTM readiness framework

Where it falls short

  • Static question set with no adaptive AI follow-up — it can't probe an individual's answer about monitoring or rollback gaps in real time
  • Not tailored to engineering-specific concepts like feature flag rollout percentage, canary monitoring, or rollback SLAs
  • No published methodology on scoring logic or prompts, and no automated per-response quality scoring

Ready to launch?

Open this template in the editor. Every part is yours to change before the first respondent sees it.

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