SAFe-Agilist Adapting and Thriving with SAFe Practice Question
An ART's Product Manager reports that customer usage data shows a recently released feature is being adopted far less than predicted. The feature was delivered on time and met its acceptance criteria. The RTE wants the ART to respond to this feedback in the next PI. Which action best aligns with SAFe's approach to adapting based on market feedback?
⚠ Common exam trap
The trap here is equating meeting acceptance criteria with delivering value, when low adoption means the underlying hypothesis still needs to be tested and the backlog re-prioritized.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Bring the usage data and the underlying hypothesis to the next PI Planning as input for re-prioritizing the backlog and defining new experiments.
SAFe treats features as hypotheses about value, and real usage data is the evidence that confirms or refutes them. Bringing that data and the original hypothesis into PI Planning allows the ART to re-prioritize work and design small experiments that test how to improve adoption. This closes the feedback loop between delivery and outcome, which is how the ART adapts to market response rather than simply producing output.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Declare the feature complete since it met its acceptance criteria and move the teams to the next planned feature.
Why it's wrong here
Acceptance criteria confirm the feature was built correctly, not that it delivers the intended outcome. Ignoring low adoption wastes the opportunity to learn and adapt. SAFe distinguishes output from outcome, and the ART's purpose is to deliver value, not just completed features. Moving on without responding to usage data contradicts the principle of applying systems thinking and empirical feedback.
- ✓
Bring the usage data and the underlying hypothesis to the next PI Planning as input for re-prioritizing the backlog and defining new experiments.
Why this is correct
PI Planning is where the ART aligns on the highest-value work for the next PI. Feeding real usage data and the original hypothesis into planning lets teams re-prioritize features and define small experiments to test what will actually drive adoption. This closes the loop between delivery and market feedback, which is central to SAFe's empirical approach to adapting strategy and solutions.
- ✗
Direct the teams to rebuild the feature with additional functionality to increase adoption.
Why it's wrong here
Adding functionality without understanding why adoption is low risks investing more in a solution that may not address the real need. SAFe encourages validating assumptions with the smallest experiment before committing additional capacity. Rebuilding the feature assumes the problem is insufficient functionality, which the usage data does not establish, and it delays learning while consuming the ART's limited capacity.
- ✗
Ask the Business Owners to lower the feature's planned business value in the next PI so the ART's metrics look accurate.
Why it's wrong here
Adjusting business value after the fact to match outcomes hides the learning and misrepresents the ART's performance. Business value is an estimate used for prioritization and alignment, not a score to be retroactively corrected. This action would suppress the feedback signal the ART needs to adapt and would undermine the transparency that Inspect and Adapt depends on.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Scaled Agile exam blueprint
This SAFe-Agilist practice question is part of Courseiva's free Scaled Agile certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the SAFe-Agilist exam.