Scrum PSK I Practice Test Questions, Scrum PSK I Exam dumps
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PSK I: Improving Scrum Flow with Kanban
Professional Scrum with Kanban I (PSK I) is Scrum.org’s certification for practitioners who want to improve the flow of work while preserving the Scrum framework. The credential does not ask candidates to replace Scrum with a separate Kanban process. Instead, it focuses on applying Kanban practices—visualizing workflow, limiting work in process, managing active items, and using flow metrics—to help a Scrum Team deliver value more predictably.
Current Scrum.org course material describes PSK as particularly useful for experienced Scrum practitioners who already understand Scrum and want better ways to see and manage flow. The PSK I assessment is an online, 60-minute test with 45 questions and an 85 percent passing score. Those numbers are useful for planning, but exam success depends far more on understanding how Kanban complements empiricism than on memorizing metric definitions.
Candidates should resist a common false choice: Scrum or Kanban. In this credential, the practical question is how flow practices can strengthen transparency, inspection, and adaptation inside Scrum without changing Scrum accountabilities, events, or artifacts.
Kanban adds flow visibility to Scrum
A Scrum Team can complete all required events and still have poor flow. Work may start too early, remain blocked for days, bounce between specialists, or accumulate near the end of the Sprint. Kanban practices make those patterns visible by defining workflow states and policies so the team can see how work actually moves rather than how the process is supposed to move.
The Scrum artifacts still provide transparency around product work and commitments. A Kanban board or workflow definition adds another lens: the state of active work, queues, bottlenecks, and aging. The two forms of transparency reinforce each other when they serve the same product goal.
Workflow definitions should match reality
A useful Definition of Workflow identifies meaningful states, entry and exit criteria, policies, and how WIP is controlled. It should reflect how value moves through the team rather than mirror job titles or create a decorative board. Too few states can hide where work waits; too many states create maintenance overhead without improving decisions.
Candidates should be able to reason about policy clarity. If people disagree about what “in review” means or whether an item can enter testing with known defects, the board may show movement without creating reliable transparency. Explicit policies reduce these ambiguities and give the team something concrete to inspect and improve.
WIP limits expose capacity constraints
Limiting work in process encourages the team to finish existing work before pulling more. This is not a utilization rule designed to keep every individual busy. In fact, good flow may require someone to help a blocked colleague rather than start a new item in their own specialty. The system is optimized for value moving through the workflow, not for maximizing local busyness.
When a WIP limit is reached, the constraint creates a conversation. Why is work accumulating here? Is a review skill scarce? Are items too large? Is an external dependency slow? Are quality problems generating rework? The limit is useful because it reveals the problem early enough to act.
Cycle time and throughput answer different questions
Cycle time describes how long work takes to move through a defined part of the workflow. Throughput describes how many work items are completed in a period. Work item age shows how long an active item has been in progress. WIP shows how much work is currently inside the system. These measures are related, but each supports a different decision.
Candidates should avoid using flow metrics as performance targets for individuals. If people are rewarded for maximizing throughput without regard to value or quality, they may split work unnaturally or avoid complex items. Metrics are most useful when teams use them to understand the system, forecast probabilistically, and test whether an improvement changes the flow.
Service level expectations support probabilistic forecasting
A service level expectation expresses a forecast about how long a work item is likely to take based on historical performance. It is not a guarantee for every item. The distinction matters because knowledge work contains variability. A team can improve forecasting by examining its actual cycle-time distribution rather than substituting a single optimistic estimate for evidence.
This probabilistic view fits Scrum’s empirical foundation. A forecast can be inspected as more data becomes available, and unusual aging can trigger earlier attention. The team learns from its own system rather than assuming that a plan made before the work began is automatically more reliable than observed performance.
Flow metrics can make Scrum events more useful
Kanban information can enrich Sprint Planning by showing historical throughput and WIP patterns, the Daily Scrum by highlighting aging and blocked work, Sprint Review by connecting delivery patterns to outcomes, and the retrospective by providing evidence about process constraints. The events do not become metric meetings; the metrics help participants inspect reality.
Practitioners who hold Professional Scrum Master I knowledge should recognize the continuity. PSK I does not introduce a new accountability that owns the board or a new event that replaces the Daily Scrum. It adds practices that can make the existing framework more transparent and flow-aware.
Kanban should strengthen the Sprint rather than undermine it
Some teams misunderstand flow as a reason to ignore the Sprint Goal or continuously swap priorities. Scrum still creates a short planning horizon and a goal that gives coherence to the Sprint. Kanban helps the team manage how work moves within that commitment and can reveal when too much parallel work threatens the goal.
Similarly, continuous delivery capability does not eliminate the value of a Sprint cadence. A team may release whenever value is ready while still using the Sprint for planning, inspection, and adaptation. Candidates should separate release timing from the purpose of the Scrum events.
PSK I preparation should use real flow data
The strongest preparation uses examples. Draw a workflow, define WIP limits, identify blocked work, calculate simple flow metrics, and ask how the information changes a Daily Scrum or retrospective conversation. Examine a scatterplot or aging view and decide which items deserve attention. Consider what happens when policies are unclear or when a WIP limit is repeatedly exceeded.
The Scrum certification ecosystem includes deeper Scrum Master and Product Owner paths, but PSK I has a distinctive purpose: improving the flow of value without weakening Scrum. Candidates who understand that relationship can reason through unfamiliar scenarios instead of treating Kanban terminology as an independent memorization list.
Little’s Law is useful conceptually because it connects WIP, throughput, and cycle time in a stable system. Candidates do not need to turn every scenario into a mathematical exercise, but they should understand the implication: if throughput is roughly stable and WIP grows, cycle time tends to grow as well. This provides another reason to control work in process instead of assuming that starting more items will make the team finish more quickly.
Blocked work deserves explicit treatment. A board that leaves blocked items in an ordinary state can hide the age and impact of the problem. Teams can visualize blockers, record why they are blocked, and inspect recurring causes. The important outcome is not a perfect board design; it is earlier attention and organizational learning. Repeated blockers tied to the same dependency may justify a structural change rather than repeated escalation of individual items.
Forecasting improves when the team uses a meaningful reference class. Historical cycle times from tiny maintenance tasks may not predict a large integration change. Teams should compare similar work where possible and understand when a new type of item carries more uncertainty. Probabilistic forecasts are strongest when the data reflects the system the team expects to use, not when a number is copied from an unrelated period simply because it is available.
PSK I study should therefore combine the Scrum Guide with the Kanban Guide for Scrum Teams and hands-on interpretation of flow data. If a candidate can explain why a WIP limit changes behavior, how an aging item should influence the Daily Scrum, and why a service-level expectation is a forecast rather than a deadline, the assessment becomes a test of reasoning rather than vocabulary.
Teams should also understand that reducing cycle time is not automatically the objective for every item. Some work legitimately requires deeper discovery, safety review, or technical validation. Kanban helps by making those paths explicit and comparing like with like rather than pressuring every item into the same speed target. Candidates should look for balanced decisions: reduce unnecessary waiting and handoffs, but do not remove controls that protect quality or value. Good flow is the smooth movement of the right work through an appropriate system, not simply the smallest possible number on a chart.
This is also why teams should inspect their workflow policies periodically. A WIP limit, state definition, or service-level expectation that was useful six months ago may no longer fit the team’s skills, product, or architecture. Kanban practices are empirical. The team should preserve a policy because evidence shows it helps flow, not because the board has always been configured that way.
A final check for PSK I is whether the chosen Kanban practice increases transparency and supports better decisions inside Scrum. If a board, metric, limit, or policy becomes an end in itself, the team has lost the empirical purpose. Flow practices are useful because they reveal reality early enough for people to inspect it and adapt.
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