Q01
메시지
모든 템플릿
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.
샘플 질문
템플릿에 포함된 내용을 미리 확인해 보세요. 모든 질문은 설문 공개 전에 자유롭게 수정할 수 있습니다.
질문 20개 · 약 9분
Q02
객관식
Does your team currently use feature flags or similar runtime toggles (e.g., LaunchDarkly, Unleash, homegrown systems)?
- Yes
- No
Q03
의견 척도
For typical changes protected by a feature flag or canary, how much rollout risk is your team comfortable accepting?
척도: 1 – 7
최소:Very risk-averse최대:Very comfortable with risk
Q04
의견 척도
How confident are you that your team's monitoring and alerting would detect a problematic flagged change within approximately 10 minutes?
척도: 1 – 7
최소:Not at all confident최대:Extremely confident
Q05
의견 척도
How confident are you that your team can roll back or disable a problematic flagged change quickly?
척도: 1 – 7
최소:Not at all confident최대:Extremely confident
Q06
장문형
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
객관식
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
메시지
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
드롭다운
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
의견 척도
<p>How well does your team monitor <strong>error rates and exceptions</strong> for changes behind feature flags?</p>
척도: 1 – 5
최소:Not monitored at all최대:Fully monitored with automated alerts
Q11
드롭다운
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 인터뷰
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
드롭다운
How many years of professional experience do you have?
- 0–1
- 2–4
- 5–9
- 10–14
- 15+
- Prefer not to say
Q14
의견 척도
<p>How well does your team monitor <strong>latency and performance metrics</strong> for changes behind feature flags?</p>
척도: 1 – 5
최소:Not monitored at all최대:Fully monitored with automated alerts
Q15
드롭다운
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
의견 척도
<p>How well does your team monitor <strong>business/product metrics</strong> (e.g., conversion rates, revenue) for changes behind feature flags?</p>
척도: 1 – 5
최소:Not monitored at all최대:Fully monitored with automated alerts
Q17
드롭다운
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
의견 척도
<p>How well does your team monitor <strong>user-facing logs and anomalies</strong> for changes behind feature flags?</p>
척도: 1 – 5
최소:Not monitored at all최대:Fully monitored with automated alerts
Q19
드롭다운
In which region are you primarily based?
- North America
- Europe
- Asia-Pacific
- Latin America
- Middle East
- Africa
- Prefer not to say
Q20
장문형
What, if any, gaps exist in your team's monitoring or alerting for flagged changes?
포함된 기능
AI 후속 질문
정형화된 설문이 놓치는 세부 내용을, 주관식 답변에 맞춰 AI가 심층 질문으로 끌어냅니다.
주의력 확인 장치
성의 없는 답변과 저품질 응답자를 걸러내는 내장 안전장치입니다.
AI가 작성한 문안
문구, 질문 순서, 분기 로직까지 AI가 연구 목표에 맞춰 작성합니다.
자동 리포트
응답이 모이면 주요 주제, 인용문, 이해하기 쉬운 요약이 자동으로 작성됩니다.
다른 서비스와 비교
다른 설문 도구의 가장 유사한 템플릿을 검토했습니다. 그 도구들이 잘하는 점과, 이 템플릿이 한발 더 나아가는 지점을 정리했습니다.
이 템플릿을 선택하는 이유
- 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 MediaA 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.
잘하는 점
- 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
아쉬운 점
- 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
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이 템플릿을 편집기에서 열어 보세요. 첫 응답자가 보기 전에 모든 부분을 원하는 대로 바꿀 수 있습니다.