INTERVIEW QUESTION
Tell me about a time you dealt with ambiguity: how to answer
Name what you couldn't answer at the start, what you assumed so you could move, and how you tested it.
Most answers to this question start the same way. The requirements were unclear, so you set up meetings with the stakeholders, got clarity, and then built the thing.
Interviewers hear that answer a lot, and it tells them very little. An interviewer who asks this often calls “I talked to stakeholders to get clarity” a cookie-cutter answer. The meetings aren't the problem. What's missing is what you did while things were still unclear.
A strong answer names three things: the question you couldn't answer at the start, the assumption you made so you could move, and how you tested it. This guide shows how to find those in your own story.
What counts as ambiguity at work
Many candidates aren't sure what the word means here, and some distrust it. In job ads, “comfort with ambiguity” can read as a warning that the scope of the job will keep changing, as one r/recruitinghell thread puts it bluntly.
In an interview it means acting without the information you'd like, and without anyone setting out the steps. An engineering interviewer tests it with an ill-defined problem, watching whether candidates find the need, plan and design a way through. Google's 2019 How We Hire video named it a hiring trait, though its current hiring pages don't use the phrase.
Everyday situations count: a ticket with no acceptance criteria, “find out why sign-ups dropped”, a brief that says “make onboarding better”, a decision due before the data arrives. If you're early in your career, a group project with a vague brief or a new job with no handover can work.
If nothing comes to mind, ask yourself when you last started something before you were sure it was right. Most people have several: the first draft sent with open questions, the estimate given before the details were known. Those are ambiguity stories, even if nobody called them that.
The expected scale grows with seniority. A reply in the same thread notes that the more senior you are, the more ambiguity you're expected to handle.
Match the story to the level you're applying for. Whatever its size, it tends to go wrong in the same place.
Why asking for clarity isn't the whole answer
Asking questions is a good first step. The trouble starts when the story stops there, as if the ambiguity disappeared once someone replied.
Often it doesn't. The same project management interviewer explains that asking around can make things more ambiguous. A reply in that thread suggests the opposite habit: draft a plan from your assumptions, then share it so people can correct it.
That's the behavior interviewers want to hear about. You made a reasonable guess, said it out loud and checked it quickly. A product interviewer listens for risk reduced through prototypes, small releases with clear goals, and learning fast.
Sometimes the person who asked can't answer either. Then go to the people closest to the work. An analyst on r/BusinessIntelligence suggests talking to the requester's own staff, building a rough prototype in whatever tool is quickest, and letting the feedback shape the next version. Each step is a test of a guess.
Clarifying questions still count when they're specific, especially for analysts. Asking which metric, over what period and how it's defined isn't stalling, as an analyst on r/analytics points out. The difference is that you name those questions in your answer, which is where the structure starts.
How to tell it
Interviewers use these questions to see how you make decisions in general. A manager on r/managers asks about deciding without enough information because it shows a candidate's whole process. So the answer should make your reasoning easy to follow.
It follows the STAR order, with the unknown at the start and the test in the middle.
The question you couldn't answer. “It was unclear” isn't enough. Say exactly what: who the report was for, whether the system would hold the load, which customers the feature was meant to serve. If the decision couldn't wait, say why.
What you assumed so you could start. Say why it was a reasonable guess, and whether it was easy to undo. Amazon's leadership principles note that many decisions are reversible and don't need extensive study. Saying your choice could be reversed shows you weighed the risk.
How you tested it, and what you found. A prototype, a draft sent to the requester, a small release, a quick query. Include what you got wrong. A former Amazon product leader asks what you learned regardless of whether the call was right.
One follow-up worth practicing out loud is “What did you have to assume in order to get started?” Kora asks follow-ups like that: you pick your role, answer behavioral questions out loud, get a follow-up when your answer leaves out an important detail, and get feedback after each answer.
Go back to the answer from the start of this page. “The requirements were unclear” becomes “nobody could tell me whether the export was for finance or for customers, so I assumed finance, built one sample, and sent it to both on day two.”
The meetings are still there, but now they test something. The examples below show the same shift in six roles.
Example answers
In each made-up stronger answer, the question nobody could answer comes in the first two sentences, followed by the assumption and the test. Check whether your answer names all three.
Software engineer
Question: “Tell me about a time you were given an unclear assignment. How did you work out what to do?”
Weaker
“I was given a ticket to make report exports faster, with no details. I set up a meeting with the product manager and the team lead to get clarity on the requirements.
Once we were aligned, I implemented the changes and exports got faster.”
A meeting clears up the ambiguity, so there's no assumption, no test and nothing for the interviewer to probe.
Stronger
“I got a ticket to make report exports faster before a large customer onboarded. Nobody could tell me which exports were slow, or how fast was fast enough.
I assumed the problem was account size, since this customer had about ten times our usual data. I loaded a copy of a similar-sized account and timed our five most-used reports. Two took over a minute to export.
I posted those numbers with a proposal: fix those two, aim for under ten seconds, and leave the rest. The product manager agreed in the thread that day.
Streaming the rows instead of building the whole file in memory brought both under eight seconds, and the customer onboarded without complaints about exports.”
It names what nobody knew, the guess that let work start, and the quick test that turned a vague ticket into a clear target.
Follow-ups to expect: What did you have to assume in order to get started? Who did you ask, or what did you read, to work out what was needed?
Product manager
Question: “Tell me about a difficult decision you made without all the information you wanted.”
Weaker
“We didn't have much data on whether users wanted a team plan, but I trusted my gut and we built it.
It turned out well, and I learned that sometimes you just have to go for it.”
“Trusted my gut” hides what was missing and how the risk was managed.
Stronger
“We had to decide whether to build a team plan before a competitor launched one. What I couldn't answer was whether teams would pay per seat or wanted a flat price, and we had no pricing data.
I assumed per seat, because team size kept coming up on sales calls. Instead of building billing first, we added a team plan option to the pricing page that led to a waitlist with two prices.
In three weeks, 140 accounts joined, and four in five picked per seat, so that's what we built.
The flat-price group turned out to be mostly agencies, which I hadn't expected. We added an agency tier the following quarter.”
The unknown is specific, the test is small and easy to undo, and the answer admits something the candidate didn't predict.
Follow-ups to expect: What information was missing when you had to decide? Why couldn't the decision wait for that information?
Product designer
Question: “Tell me about a time you were given a vague brief.”
Weaker
“My manager asked me to make our returns process better. It was really vague, so I did a lot of research, talked to users and stakeholders, and came up with a new design.
Everyone loved it.”
“A lot of research” doesn't say what the designer didn't know or what they assumed.
Stronger
“The brief was to make returns easier in our shopping app. The question I couldn't answer was where people gave up, because our analytics only recorded started and completed returns.
I assumed it was the label-printing step, since most support emails mentioned printers. Before redesigning anything, I asked an engineer to track each step for a week.
My guess was only half right. Printing lost some people, but more quit at the step asking why they were returning the item, which I hadn't looked at.
I took both findings to the product manager, and we agreed to start with the reason step. The redesign cut drop-off there by about a third, and we added a QR code option for printing the next month.”
The messy middle stays in: the first assumption was only half right, and the test changed the plan.
Follow-ups to expect: What did you have to assume in order to get started? What goal or scope did you settle on in the end?
Data analyst
Question: “Tell me about a time a request you were given was unclear.”
Weaker
“A stakeholder asked me why engagement had dropped. The request was vague, so I asked a lot of clarifying questions.
Then I pulled the data and found the answer.”
It doesn't say which questions were asked, what was assumed, or what the answer was.
Stronger
“Our head of customer success asked why engagement was down. I couldn't tell which measure he meant, weekly active users or sessions per user, or what he was comparing against.
He was traveling, so I assumed weekly active users against the same month last year, because his team's goals used that. I put the definition at the top of a one-page draft and sent it that morning.
He replied that he meant sessions per user, compared with last quarter. Because the definition was written down, the change took an hour rather than a rewrite.
Most of the drop came from two large customers who had moved their teams onto our mobile app, where sessions weren't tracked yet. I flagged that real usage was probably flat.”
The assumption is written where the requester can correct it, which turns a vague question into one that can be answered.
Follow-ups to expect: What was the question about the assignment that you couldn't answer at the start? What did you have to assume in order to get started?
Program manager
Question: “Tell me about a time the requirements changed after you had started. What did you do?”
Weaker
“Partway through moving our build pipelines to a new CI tool, the security team added a requirement. I was flexible, adapted the plan and kept everyone informed with regular updates.
We delivered successfully.”
“Flexible” and “adapted” don't say what changed, what it cost or who agreed to it.
Stronger
“Our security team added a requirement to the CI migration I was running: every release build had to be signed, and the 1 March cutover date couldn't move.
What nobody could tell me was whether the new tool's signing worked with our mobile app builds. I assumed it did, since the vendor's documentation listed both platforms, and kept the plan on that basis.
Rather than wait for cutover, I asked the mobile lead to run one signed test build that week. It failed on Android, because the tool needed a different key format.
The steering group agreed to split the cutover. Web and backend moved on 1 March, and mobile followed two weeks later, once the keys were reissued.”
A new requirement becomes a specific question, the assumption is tested early by the person who knows, and the new plan is agreed.
Follow-ups to expect: What happened to the work you had already built? How did you agree the changed plan with the person who asked for the work? How do you stop scope growing without anyone deciding it should?
Consultant
Question: “Tell me about a recommendation you made without all the data you wanted.”
Weaker
“The client didn't have good data, so we used industry benchmarks and made a recommendation based on best practice.
The client was happy with our work.”
Benchmarks stand in for judgment. There's no hypothesis to test and no way it could be shown to be wrong.
Stronger
“An insurance client asked why fewer customers were renewing their home policies. Their survey of lapsed customers wouldn't be back for three weeks, and the steering meeting was in ten days.
My hypothesis was that price, not service, was the main cause, because premiums had risen sharply at renewal that year. I told my manager what would change my view: renewals falling just as much among customers whose premium hadn't gone up.
I pulled renewal data for customers whose premium had stayed flat. Their renewal rate had barely moved.
We presented renewal pricing as the main finding, labeled provisional until the survey arrived. Three weeks later, the survey backed it up.”
It's hypothesis-led, says up front what would disprove it, and runs a quick test the data could have failed.
Follow-ups to expect: What information was missing when you had to decide? How did you decide to go ahead without it?
Hypothetical product cases and past-behavior questions
Searching for ambiguity interview questions brings up two different formats. A widely upvoted post, from a hiring manager on r/ProductManagement, is about hypothetical product cases: a made-up scenario, such as your CEO deciding to take on a competitor, with almost nothing else to go on.
In that format, the advice is to slow down, ask questions before proposing anything, and work toward a small first version. It's a product-sense exercise, scored on how you reason in the room, and this guide doesn't cover it.
Questions that start with “Tell me about a time” are past-behavior questions and need a real story. Those stories tend to go wrong in a few predictable ways.
What makes the answer go wrong
Most weak answers skip the time when things were still unclear. These are the common versions:
- Clarity as the whole story. You asked, someone answered, and the work began. Nothing shows what you did before the answer arrived.
- Waiting for a full plan. The same project management interviewer says a candidate who needs everything planned and organized is likely to feel overwhelmed when the answers only add to the ambiguity.
- Working it out alone. A product manager who interviews engineers wants someone who will work out an unclear initiative together with product, not someone who expects product to settle everything first.
- A path that's too tidy. A neat line from vague brief to perfect result sounds rehearsed. A reply on r/UXDesign says the messy middle is what makes a story interesting.
Other ways it gets asked
The same story usually fits several wordings, with a change in what you lead with.
“Tell me about a decision you made without all the information you wanted.” Lead with what was missing and why the decision couldn't wait. The product manager and consultant examples above answer this one.
“Tell me about a time the requirements changed after you'd started” or “…adapting to a major change.” The change becomes the question you couldn't answer. Say what happened to work already done, what you kept and threw away, and who agreed to the new scope.
“How comfortable are you with ambiguity?” or “How do you handle ambiguity?” Answer in a sentence, then give the story: “Fairly comfortable, as long as I can test my guesses early. On my last project…”
“How do you deal with changing priorities?” If the work stayed clear but there was more of it, or its order kept shifting, that's a prioritization question. There the answer turns on what you dropped and who you told, not on what you assumed.
