Apple Data Analyst Interview Questions

“I was asked whether a new settings screen confused people. The easy way was to record every tap, which we did not allow. Instead I used counts that were already collected: how often the screen was opened per device, without identity, and the share of support contacts mentioning it. I also ran a small usability session with volunteers. The counts showed the screen was opened many times in a row, which fit the sessions, where people went back and forth to find one option. I said clearly that the counts alone could not prove confusion. The team moved the option. The outcome was your result.”

“A team planned to remove a rarely used option from a media app because usage looked low. My analysis showed that the small group who used it did so almost daily and were among the longest-standing customers. I checked the result with two different data sources first. Then, instead of saying the decision was wrong, I brought the team a one-page view: how often those people used it, and what they might lose. I offered two options: keep it, or move it to a less prominent place. They chose to move it. After the change, your result.”

“A good search means the person found what they wanted quickly. So I would look at how often a search ends with a tap high in the results and no further search, and whether the app opened was still used a week later, since a download that is deleted the next day was not a good match. Failure signals matter as much: the same words typed again with small changes, or the person leaving empty-handed. I would split all of this by query type, such as an exact app name against a general need, and by language, because quality often differs a lot between them.”

“I would define the outcome as still subscribed some weeks after the trial ends. Then I would compare trial users in groups by what they did early: how many days they listened, whether they saved songs or made a playlist, and whether they listened on more than one device. Working with grouped counts, not individual records, still shows patterns, such as people who build a playlist in the first week staying far more often. I would say clearly that this is a link, not a cause. The next step would be a product test that makes playlist creation easier and measures the change.”

“The trick is that the latest version changes during the period. So I would first build a small table of versions with their release dates, and for each day find the newest version released on or before it. Then I GROUP BY model and day, summing all active devices and, with a CASE inside the sum, only those on that day's latest version. Dividing the two gives the share. Plotting it per model shows how fast each one adopts the update. Before trusting it, I would check for days where the counts drop suddenly, which usually means a missing data load rather than fewer devices.”

“First I would agree the main outcome with the team, for example the share of visitors who add the product to their bag, and a few guardrails such as returns and page load time. Visitors are assigned to the old or new layout at random and stay in their group across visits. Before starting, I estimate how many visitors are needed to see a real difference, so we do not stop early because the numbers look good on a Tuesday. When the test ends, I read the main outcome once, check the guardrails, and split by device to make sure the new layout did not help on desktop while hurting on phones.”

“I would first separate two things: more people talking about battery, and batteries actually draining faster. Support contacts and public discussion measure the first. For the second, I would use the anonymous battery statistics that devices already report, comparing devices on the new version with those still on the old one, model by model, over the same days. If measured use rose only on one model, that points to a real cause there. If it did not rise at all while complaints did, the update may have made a change people noticed, such as a new indicator. I would report both parts plainly.”

“I have worked in places where the answer to every question was to collect more about each person. Apple makes devices people carry everywhere and services that hold their photos and music, and it chooses to know little about them. I find that constraint interesting: it forces better questions and better methods, such as grouped counts, small studies and honest uncertainty. In my last role I answered a design question with counts and a handful of usability sessions instead of tracking, and the answer held. I want to get better at that kind of careful, limited analysis.”