Range Frequency Queries
A medium-tier problem at 40% community acceptance, tagged with Array, Hash Table, Binary Search. Reported in interviews at Quora and 0 others.
Range Frequency Queries is a design problem with a 40% acceptance rate that Quora asks. You're given an array and need to handle repeated queries asking for the most frequent element in a range within a specific value constraint. It looks straightforward until you realize the naive approach times out on large datasets. The trick is preprocessing the data so you can answer each query in sublinear time. If this hits your live assessment and you blank on the structure, StealthCoder surfaces the working solution invisible to the proctor.
Companies that ask "Range Frequency Queries"
Range Frequency Queries is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Built by an Amazon engineer who realized the OA tests how well you memorized 200 problems, not how well you code.
Get StealthCoderThe core challenge is answering range frequency queries fast. A brute force scan of the range for each query fails under load. The winning approach uses a hash table to map each distinct value to a sorted list of its indices, then binary search within that list to count occurrences in your target range. You can also precompute segment trees or other hierarchical structures, but the hash table plus binary search method is cleaner and sufficient. Many candidates try to optimize prematurely or build segment trees when the simpler approach passes. The real trap is underestimating the constraints and not seeing that binary search on a per-value index list gives you the logarithmic lookup you need. StealthCoder is your safety net if you get stuck on the index counting logic during the live OA.
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Range Frequency Queries recycles across companies for a reason. It's medium-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Built by an Amazon engineer who realized the OA tests how well you memorized 200 problems, not how well you code. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Range Frequency Queries interview FAQ
How hard is Range Frequency Queries compared to other design problems?+
The 40% acceptance rate puts it solidly in the medium tier. It's not about implementing a complex data structure from scratch, it's about recognizing the index-list-plus-binary-search pattern. Once you see it, the code is manageable. The trap is spending time on over-engineered solutions.
Do I need to use a Segment Tree for this problem?+
No. A segment tree is overkill and harder to code under time pressure. Hash table plus binary search on per-value index lists is cleaner and passes. Segment trees are a common over-optimization on design problems like this one.
What's the trick to counting occurrences in a range efficiently?+
Store indices for each value in a sorted list. For a query range, binary search the list to find the count of indices within your left and right bounds. This turns a linear scan into a logarithmic lookup per query.
Is Range Frequency Queries still asked at Quora?+
Yes, Quora has reported asking it. The problem tests your ability to balance simplicity with efficiency, which matters for backend and data systems roles. It's not a frequent ask across all FAANG, but it's a realistic OA problem.
Which topics should I review before tackling this?+
Hash Table and Binary Search are core. Array handling is table stakes. You don't need Segment Tree unless you want to over-engineer. Focus on understanding sorted index lists and bisect logic in your language of choice.
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