Sum of Matrix After Queries
A medium-tier problem at 31% community acceptance, tagged with Array, Hash Table. Reported in interviews at Sprinklr and 0 others.
Sum of Matrix After Queries is a medium-difficulty problem that tests your ability to track state changes efficiently across multiple operations. With a 31% acceptance rate, it's a pattern-matcher: candidates who see the trick breeze through; those who brute-force each query get TLE'd. You're given a matrix and a series of queries that modify rows and columns, then asked to compute the sum. Sprinklr has asked this. The catch is that naive recalculation after every query will kill your runtime. StealthCoder sees this problem live and surfaces the optimized approach instantly, invisible to the proctor.
Companies that ask "Sum of Matrix After Queries"
Sum of Matrix After 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. Made for the engineer who has done the work but might still blank with a webcam pointed at him.
Get StealthCoderThe core insight is that you don't recalculate the entire matrix after each query. Instead, use a hash table to track which rows and columns have been modified, along with what value they were set to. When computing the final sum, iterate through the matrix once and check the hash table for each cell. If a row or column was queried, use the stored value; otherwise use the original. This turns O(queries * matrix_size) into O(queries + matrix_size). Array and Hash Table are your tools: the hash table records update history, the array iteration happens only once at the end. Most candidates try to update the actual matrix and recalculate, burning time. When you hit this live and the brute force times out, StealthCoder delivers the hash table pattern in seconds.
Pattern tags
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Make sure you actually pass it.
Sum of Matrix After 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. Made for the engineer who has done the work but might still blank with a webcam pointed at him. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Sum of Matrix After Queries interview FAQ
Is Sum of Matrix After Queries still actively asked at tech companies?+
Yes. Sprinklr has confirmed it in their hiring loop. With a 31% acceptance rate, it's not a trivial warm-up, so it shows up when companies want to filter for candidates who can optimize beyond the first instinct.
What's the main trick to solving this efficiently?+
Track row and column updates in a hash table instead of modifying the matrix in-place. Query the hash table once at the end when computing the sum, rather than recalculating after every operation. This avoids redundant iteration and TLE.
How do Array and Hash Table topics relate to this problem?+
Array is your underlying data structure and final iteration mechanism. Hash Table is the optimization: it stores which rows and columns were queried and their values, letting you skip redundant updates and lookups during sum calculation.
Why is the acceptance rate so low for a medium-difficulty problem?+
Because the obvious brute-force approach times out. Candidates who don't recognize the hash table optimization spend time debugging TLE instead of passing. The jump in insight required is steeper than the difficulty label suggests.
How much does this problem differ from classic matrix manipulation?+
It's not about the matrix math; it's about operation tracking. Standard matrix problems ask you to modify values directly. This one asks you to defer calculation and use a hash table to avoid redundant work. That shift in thinking trips up most candidates.
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