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Question 1 of 12 · Digital Product Management Foundations
A B2B SaaS company measures the success of its onboarding redesign by counting the number of new features shipped in a quarter. Product leadership wants to shift the team toward outcome-orientation. Which change BEST reflects this shift?
This replaces an output metric (features shipped) with an outcome metric (user activation), directly measuring the value delivered to customers rather than the volume of work produced.
Question 2 of 12 · Customer Discovery, JTBD, and Design Thinking
A product team has vague signals that users are frustrated with the onboarding flow but no clear understanding of why. Which discovery approach BEST fits this stage of uncertainty?
When the problem itself is undefined, qualitative semi-structured interviews (the empathize stage of Design Thinking) are the right method to surface the underlying jobs, motivations, and pain points before any solution or metric can be meaningfully defined.
Question 3 of 12 · Lean, MVP, and Experimentation
A team believes their riskiest assumption is that busy parents will pay for a subscription meal-kit delivery service, before any software, logistics, or kitchen operations have been built. What is the cheapest valid test of this assumption?
A landing page (smoke test) MVP measures revealed preference — actual behavior like clicking 'Get Started' and entering payment details — at minimal build cost. It directly tests willingness to pay, which is the riskiest assumption, without building any real product.
Question 4 of 12 · Agile Delivery, Backlog, and Roadmapping
A product owner is choosing between two backlog items using WSJF. Feature A has a Cost of Delay of 40 and a job duration of 5 weeks. Feature B has a Cost of Delay of 30 and a job duration of 3 weeks. Which feature should be prioritized first, and why?
WSJF = Cost of Delay / Job Duration (job size). Feature A = 40/5 = 8; Feature B = 30/3 = 10. Even though Feature A has a higher absolute Cost of Delay, WSJF normalizes for the size of the job, and Feature B delivers more value per unit of time, so it should be sequenced first.
Question 5 of 12 · Product Analytics, Metrics, and Growth
A B2B SaaS company tracks day-30 retention cohorts and notices that users acquired through a paid search campaign retain at 15%, while users acquired through referrals retain at 55%. Weekly active users overall are flat. What should the product team do next?
Cohort analysis exists precisely to reveal that aggregate metrics like flat WAU can mask divergent behavior between acquisition sources; the correct next action is to reallocate spend toward the higher-quality channel and diagnose the root cause of paid-search churn (e.g., activation friction, poor fit) before scaling further.
Question 6 of 12 · Digital Product Design, Emerging Tech, and Responsible AI
A digital product team is building a public-facing web app for a government contract. Which accessibility standard should the PM require the design system to conform to?
WCAG 2.1 AA is the widely adopted baseline for public and government digital products, balancing comprehensive coverage with feasibility; modern Section 508 requirements incorporate WCAG 2.1 AA success criteria directly.
Question 7 of 12 · Digital Product Management Foundations
A digital product organization currently ships a physical-product-style annual release with a hard launch date. The team wants to move to an operating stance appropriate for a SaaS platform with continuous updates. Which stance BEST fits this digital product context?
Continuous delivery aligns with the ongoing nature of the digital product life cycle, reduces launch risk, and enables faster learning loops based on real usage data rather than a one-time launch event.
Question 8 of 12 · Customer Discovery, JTBD, and Design Thinking
In Jobs-To-Be-Done theory, a customer chooses a premium coffee brand partly because carrying the cup signals status to colleagues. Which dimension of the job is this best classified as?
The social job dimension captures how a customer wants to be perceived by others when using a product or service — in this case, signaling status to colleagues.
Question 9 of 12 · Lean, MVP, and Experimentation
A startup wants to test whether users want automated, personalized stock investment advice. Behind the scenes, an analyst manually researches and selects picks for each user, but the users interact with an app they believe is fully automated. What MVP type is this?
In a Wizard of Oz MVP, the customer believes the product is fully automated while humans perform the work manually behind the curtain — this tests demand for the automated experience without building the automation.
Question 10 of 12 · Agile Delivery, Backlog, and Roadmapping
A product team is building a roadmap for a digital product with high market uncertainty. Which roadmap format is MOST appropriate for communicating direction to stakeholders while preserving flexibility?
Now-Next-Later roadmaps organize work by confidence and desired outcomes (Now = actively being delivered with high confidence, Next = validated and upcoming, Later = exploratory ideas) rather than fixed dates, which communicates direction while allowing the plan to adapt as learning occurs — ideal for high-uncertainty digital products.
Question 11 of 12 · Product Analytics, Metrics, and Growth
A two-sided marketplace app connecting freelancers and clients is choosing a North Star metric. Which option best reflects the product's core value delivery and is most defensible for guiding cross-functional teams?
A North Star metric must capture the moment the product delivers its core value to both sides of the marketplace; completed paid transactions reflect actual value exchange, are outcome-oriented, and align product, growth, and revenue teams around a single leading indicator of business health.
Question 12 of 12 · Digital Product Design, Emerging Tech, and Responsible AI
A PM is defining acceptance criteria for a new AI-powered recommendation feature built on a machine learning model. Which acceptance criteria approach is MOST appropriate given the model's non-deterministic outputs?
Probabilistic ML systems must be evaluated statistically (e.g., precision, recall, satisfaction scores) against a representative dataset rather than a deterministic pass/fail, and paired with ongoing drift monitoring since real-world data shifts over time.
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It covers 6 domains: Digital Product Management Foundations (~14%), Customer Discovery, JTBD, and Design Thinking (~18%), Lean, MVP, and Experimentation (~18%), Agile Delivery, Backlog, and Roadmapping (~18%), Product Analytics, Metrics, and Growth (~17%), Digital Product Design, Emerging Tech, and Responsible AI (~15%). The full course has a dedicated chapter, lab and practice-test coverage for each.
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