As soon as it comes to the NVIDIA interview, a lot of people feel that the volume, the difficulty is indeed on the large side, but the preparation is in place fully I'm not sure if you're going to be able to get through this, but I'm sure you can. Just successfully helped a student to end the NVIDIA SDE VO interview, organized the process and questions to the students who want to submit a reference ~
NVIDIA SDE Interview Process
- Electric/video interview: initial screening + Coding + job description.
- Tech side: examining algorithms, system design, Coding, C++ language, etc.
- On-site side: BQ+ project deep dive.
Review Of Interview Questions
Phone Screen
BQ
- In your employment experience, what suggestions have you proposed for your supervisor?
- Do you have any coding experience with assembly code?
- What is your most challenging project experience?
This participant's pre-test simulated BQ answers were very general, not supported by data, and did not highlight individual roles and contributions when describing his personal project challenges. Later, with our help, he used the STAR model to systematically reconstruct his answers and also applied this approach in the interview to fully explain the process of solving the high concurrency challenge and reducing latency by 50% through distributed architecture.
Coding
The question is given an array nums containing n integers, determine whether it can become a non-decreasing array by modifying at most one element. Where a non-decreasing array is defined as having nums[i] ≤ nums[i + 1] holds for all subscripts i in the array that satisfy 0 ≤ i ≤ n - 2 (with subscripts starting at 0).
This participant started by just traversing the array, and when he found that nums[i] > nums[i + 1] he directly modified nums[i] to nums[i + 1] and counted the number in theProgramhelp When prompted, he added that when encountering a violation, first check i > 0 and nums[i + 1] < nums[i - 1] then modify nums[i + 1] to nums[i], otherwise modify nums[i] to nums[i + 1] and strictly limit the number of modifications to no more than once to ensure that the fixes are effective, which won the interviewer's approval.
Tech Round
Topic 1: is L1 sparse solution property vs L2 smooth solution property, explain the difference from the gradient update formula.
The participant initially answered "Both L1 and L2 add penalties to the gradient update, but L1 has a larger penalty resulting in a smaller parameter", without clearly explaining the source of sparsity. At Programhelp's prompting, he added that "from the gradient formula, the gradient of L1 contains sign(w), and when |w| is small, the update can make it go to zero directly; while the gradient of L2 is a linear term λw, and the parameter can only asymptotically converge to zero, so L1 produces a sparse solution, and L2 produces a smooth solution."
Topic 2: Special Array Determination (Parity Alternation) Violent solution: traverse to check parity of neighboring elements, time complexity On).
A similar type of question was simulated for this participant before the exam, so he was traversing the array with a brute force solution, checking whether each neighboring element has a different parity (i.e., nums[i] % 2 ! = nums[i+1] % 2), if all meet then return true, otherwise false, time complexity O(n).
Follow-up: How to maintain parity with a line tree if the array changes dynamically?
Idea: To support dynamic updates, use a line tree to store the parity alternation state of each interval: each node records whether the interval is alternating or not, and the parity of the left and right endpoints. Check for alternation at child node junctions when merging, and recursively adjust when updating so that both queries and modifications are O(log n).
Topic 3: Subarray Special Queries (Prefixing and Optimization)
The participant of this question also directly and violently enumerates all the subarrays and checks the parity alternation one by one. With the hints from Programhelp backend, he changed his mindset and used prefix and optimization to maintain an array of prefixed parity states and record the pattern of parity change when arriving at each position, so as to determine whether any subarray is special or not in O(1) time, and improve the query efficiency to O(n).
System Design
Title: designing a distributed training system for large models with trillions of parameters.
The trainee answered "use data parallelism to replicate the model to multiple GPUs", and with Programhelp's reminder, he used a hybrid parallelism strategy, combining pipeline parallelism to divide model layers, tensor parallelism to split large weights, and using the ZeRO optimizer to eliminate memory redundancy, and communication optimization to achieve efficient training.
On-site Round
BQ
- Individual career planning
- What are the biggest current technical bottlenecks in the department? What efforts can you make?
System Design
Topic: Designing a recommendation interface that supports 100,000 QPS per second
Idea: Combine Redis preload hotspot data and Guava local cache to reduce latency, and decouple feature computation and model inference through Kafka asynchronous to ensure system scalability to support 100,000 QPS.
About Programhelp
If you want to pass a difficult interview like NVIDIA, welcome to talk to us~ We can provide you with professional interview assistance, OA writing to ensure the pass rate, remote interview assistance from North American CS experts, voice reminders, interviews, etc. We can customize the whole process from resume optimization to contract negotiation, and help you get the Offer of your choice!
