Jira adds one-click links to six AI coding agents
Jira now deep-links to six AI coding agents including Claude Code and Cursor, auto-filling work item context via Atlassian MCP to eliminate copy-paste. This removes the prompt-prep tax on every ticket for teams using Jira. Find it in the Development panel now.
Bitbucket beta adds live deployment status to PR lists
Bitbucket beta adds a Deployment column to PR lists showing live environment status per commit, cutting context switches to pipeline UIs during incidents. Existing Pipelines users get it automatically; others enable it by adding a deployment step to…
How would you frame technical debt for your manager's business case?
This tests translating debt into business risk and cost of delay. A strong answer quantifies velocity drag, proposes phased remediation via WSJF or capacity allocation, and offers roadmap trade-offs.
What is throughput and how does it differ from velocity?
Tests whether you know throughput is count-based and velocity estimate-based. Define throughput as items finished per sprint regardless of size, contrast velocity's point sum, pick throughput for forecasting and keep velocity for calibration.
How do you evolve a team from dependent to self-managing?
Tests situational leadership across Tuckman's stages. Answer: direct in Forming, facilitate conflict in Storming, observe in Norming, system-coach in Performing, and retire each stance as trust grows.
How would you advocate for decentralizing a deployment approval dependency?
WHAT IT TESTS: Separating strategic and local decisions while using data. OUTLINE: Propose a pilot with guardrails; track lead time, defect rate, rollbacks; define escalation paths. RED FLAG: Demanding autonomy without metrics or dismissing enterprise risk.
Manager wants to attend your Sprint Retrospective. Risk and response?
This tests your grasp of psychological safety in retrospectives. A strong answer cites the observer effect, proposes an alternative forum first, and sets ground rules if attendance is required.
What options exist when a story is too large for one sprint?
This tests vertical-slicing discipline versus architectural decomposition. A strong answer covers splitting by user value, checking INVEST criteria, and avoiding task-like layers. A red flag is proposing horizontal database or UI splits that defer feedback.
How should the team handle an oversized user story in Sprint Planning?
WHAT IT TESTS: Protecting the Sprint Goal when a story is too big. ANSWER OUTLINE: Split it vertically with the Product Owner, swarm the top slice, and renegotiate scope rather than overcommitting. RED FLAG: Proposing overtime or horizontal splits.
How do you handle NFRs in the backlog and make them visible?
WHAT IT TESTS: Making NFRs visible and actionable in Scrum. ANSWER OUTLINE: Write NFRs as measurable backlog items with acceptance criteria; embed in Definition of Done; decompose into tasks; automate validation.
How do blockers and impediments differ, and when do you escalate?
It tests whether you separate immediate task stops from chronic drag. Blockers are red-light stops for swarming; impediments are velocity drains surfaced in retrospectives and escalated with data.
How does an engineering manager's role change moving to agile autonomous teams?
Tests understanding of shifting from command-and-control to coaching and enabling teams. Strong answers cite three domains: team coaching, value investment, and environment shaping, plus servant leadership.
What technical and process challenges appear when forming cross-functional product teams?
Tests whether you see cross-functional integration as dissolving handoffs, not renaming teams. Strong answers mention testing in CI/CD, collective estimation, and social friction. Weak answers treat it as a staffing reshuffle that keeps siloed workflows.
How does management evolve in scaled agile versus traditional program management?
This tests whether you see scaled agile shifting management from command to enablement. A strong answer contrasts LeSS manager-as-teacher supporting self-managing teams against traditional program-management command structures.
How would you probabilistically forecast 40 stories using throughput data?
Tests probabilistic forecasting literacy using historical throughput. Good answers gather 8–12 periods of throughput, run Monte Carlo resampling, and present percentile delivery curves (e.g., 50th/85th/95th).
From an engineer's perspective, when does Cycle Time begin and end?
Tests if you set Cycle Time boundaries to expose wait states past coding. Strong answer: starts at In Progress, ends at Done or production, includes review/test, excludes backlog queues, and distinguishes from Lead Time. Red flag: starting at ticket creation.
Explain Little's Law and its practical application in Kanban
This tests your grasp of the WIP-throughput-lead time relationship in stable flow systems. State Lead Time = WIP / Throughput and show lowering WIP cuts lead time if throughput is flat. Beware claiming more WIP raises throughput without increasing lead time.
Explain backlog refinement: purpose, participants, and outcomes
Tests if you treat refinement as team-wide prep, not a solo PO task. Strong answers cite the full team and stakeholders, with outcomes being ready stories and estimates. Red flag: saying only the PO and Scrum Master attend or that it replaces sprint planning.
Two senior developers clash on implementation, derailing sprint planning. Your role?
WHAT IT TESTS: Protecting Scrum events while channeling conflict into productive tension. ANSWER OUTLINE: Park or timebox the debate, reframe positions into shared interests with structured dialogue, and drive to a decision or spike.
How would you apply Conway's Law to design team structures for microservices?
WHAT IT TESTS: Using org structure as an intentional architecture lever. ANSWER OUTLINE: Map bounded contexts to cross-functional teams; use APIs as contracts; split by decoupling boundary.