Master the SQL patterns Apple asks across Data Analyst, BI, and Analytics Engineer roles. Practice App Store metrics, Services revenue, and product analytics with real datasets and AI tutoring.
14 challenges
Apple patterns
Real datasets
Product & Services
AI tutor
Step-by-step hints
What this page is: we have no dated public source for how Apple runs its SQL round, so this page does not state one — no duration, no platform, no stage list. What follows is general SQL interview practice on the kind of data Apple works with. What is ours: the questions are SQL Quest challenges and the topic emphasis is our editorial judgement, not a measured breakdown of Apple's interview. Pages with dated sources say so in this spot.
The 14 challenges tagged Apple in the SQL Quest bank, with every raw challenge tag resolved to the 9 canonical skills. Each share is the portion of those 14 challenges that exercise the skill — a challenge exercises several, so the shares do not sum to 100%. This is the composition of the practice set on this page, not a measurement of Apple’s interview.
These are SQL Quest challenges chosen because their SQL matches the work — Querying Basics, Aggregation & Grouping, Window Functions. They are not questions Apple has asked, and this page does not claim to know its questions. All of the six play free; each card opens the challenge itself.
General guidance about analytics SQL work — not sourced from Apple and not a description of its process. We have no dated, citable source for how Apple runs its SQL round, so this page states none.
Skillmap
Ten questions, no signup. You get a readiness score weighted to the SQL this page covers, your Skillmap across joins, window functions, aggregation and the rest, and the weakest skill to practise first.
Drill the skills the Apple set leans on, one at a time: GROUP BY exercises · Window function practice · Date function practice · CTE practice · JOIN practice — or browse every SQL practice question.
Every question in the Apple set, one page each with the schema and a hint: Quarterly Hiring Cohort Report · Monthly Order Trends · Monthly Order Volume in 2024 · Multi-Month Active Customers · Month-over-Month Customer Growth · Year-over-Year Growth · First and Last Order per Customer · Cumulative Revenue Share (Pareto) · Moving Average with Dynamic Window · Customer Lifetime Value · Revenue Share by Category (Window %) · Sliding Window Max Revenue · Customer Retention Cohort · Year-over-Year Movie Rating Trends.
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Interviewing at more than one company? The same patterns carry: Meta · Google · Amazon · Netflix — or the full company-by-company interview guide.