D.E. Shaw Interview Experience | SMTS | oct 2024 | Reject
Question Details
Current Position: SDE-II Current Company: Banking MNC Experience: Python Developer with 4.5 years of experience College: Tier-2 Online Assessment HackerRank Programming Questions: 3 questions focusing on Data Structures and Algorithms (DSA). Topics include dynamic programming, graphs,...
Full Details
Current Position: SDE-II
Current Company: Banking MNC
Experience: Python Developer with 4.5 years of experience
College: Tier-2
Online Assessment
HackerRank
Programming Questions: 3 questions focusing on Data Structures and Algorithms (DSA). Topics include dynamic programming, graphs, and tree DP.
**
Round 1 Data Structures and Algorithms (1 hour 15 min)**
- DSA Problem 1: Find all the articulation points in a graph.
-
DSA Problem 2: Partition an array into two subsets such that the absolute difference between the sums of the subsets is minimized.
-
problem 3:
class MyInt(int):
def __new__(cls, value):
print(f"Creating MyInt({value})")
**return** super().__new__(cls, value)
def __eq__(self, other):
print("Checking equality")
if isinstance(other, MyInt):
**return** super().__eq__(other) and self.real == other.real
**return** False
def __hash__(self):
print("Calculating hash")
**return** super().__hash__()
a = MyInt(10)
b = MyInt(10)
print(a == b)
print(hash(a) == hash(b))
- Explain the purpose of each method in MyInt.
- What will be the output of the code above?
- Modify the MyInt class to make
a == b
return False
and hash(a) == hash(b)return True
without modifying any part of the code that follows the class definition.
**
Round 2 Data Structures and Concepts**
-
DSA Problem 1: What is Best-First Search? Questions on its properties and applications.
-
DSA Problem 2: What are the key properties of a (2,4) tree, and how do these properties impact the efficiency of search and insertion operations?
I mentioned that I had forgotten what a (2,4) tree was, and the interviewer briefly explained (in under 2 minutes) that a (2,4) tree is a type of self-balancing tree where each internal node has between 2 to 4 children, ensuring logarithmic search, insertion, and deletion times.
Follow-up Question: Write a function that inserts a key into a (2,4) tree, performing node splits and necessary rebalancing.
def insert_into_24_tree(tree: \'Node\', key: int) -> \'Node\':
"""
Inserts a key into a (2,4) tree and returns the new root node.
**Parameters**:
tree (Node): The root node of the (2,4) tree.
key (int): The key to insert.
**Returns**:
Node: The root node of the updated (2,4) tree.
"""
Discussion:
- How does a (2,4) tree handle balancing differently compared to other self-balancing trees like AVL or Red-Black trees?
- What would be the impact on performance if a (2,4) tree had larger nodes (e.g., a (3,5) tree)? How would this affect memory usage?
- Can you describe how deletion works in a (2,4) tree and what challenges arise compared to insertion?
**
Round 3 Technical Round**
* Algorithmic Proof: Prove the correctness of your implementation of a merge sort that minimizes the number of inversions. Discuss the invariant maintained at each recursive level of the algorithm.
* Python Application: Write Python code to manage a priority queue efficiently, implementing insertions and extractions in O(log n) time.
- Discussion:
\t1. How would you handle thread safety when accessing a shared priority queue in Python, considering the Global Interpreter Lock (GIL)?
\t1. How does reference counting in CPython impact multi-threaded programs, and what alternatives would you suggest for high concurrency?
\t1. Discuss the difference between eager and lazy evaluation in Python.
**
Round 4 Managerial and Technical**
* Behavioral Question: Describe a time when you had to manage multiple conflicting deadlines in a high-stakes project. How did you ensure successful delivery?
* Brain Teaser: magine you\'re in a room with 100 doors, each numbered from 1 to 100. You toggle the state of doors following a pattern: first toggle every door, then every second door, every third door, and so on, until the 100th door. After completing the toggling process, how many doors will remain open?
* Scenario-based Question: You\'re leading a cross-functional team that is facing resistance to a new technology stack being introduced. How would you manage the transition and align team members with different perspectives?
**
Round 5 Techno-Managerial**
* Market Volatility: Explain how you would leverage implied volatility in a financial trading strategy to hedge against market risks. How do you model market sentiment when making risk management decisions?
* You\'re building an API that supports both RSQL and GraphQL queries for a financial system. The API needs to handle nested filters, custom exception handling, and maintain query efficiency. How would you manage errors from deeply nested queries or malformed RSQL filters while providing clear error messages to users? Address edge cases like circular dependencies and unexpected inputs.
* Behavioral Question: When did you demonstrate academic excellence in your work? Why do you believe academic excellence and ethics are essential, not just for an organization but for your own personal and professional growth?
About This Question
This is a reported interview question from a d.e. shaw interview for a swe role (intern level) during the oa round reported in 2024.
It covers the following topics: Arrays, Graph, Sql, Binary Tree, Os, Sorting, Concurrency, Dynamic Programming, Queue, Heap, Behavioral, Recursion, Stack .
Topics
More D.E. Shaw Interview Questions
About D.E. Shaw Interview Reports
This question was reported by a candidate who interviewed at D.E. Shaw. LeakCode aggregates interview reports from 10+ sources, including 1Point3Acres, Glassdoor, LeetCode Discuss, Blind, Reddit, Indeed, and Nowcoder. Each report is translated where necessary, deduplicated against existing entries, and tagged by company, role, round type, and reporting date.
Use this question as one calibration data point, not a memorization target. Companies typically rotate their question pools every 2-4 months; the exact wording of a 2024 question may differ from what you encounter today. The underlying pattern, difficulty level, and follow-up depth at D.E. Shaw are the higher-signal extractions to take from this report.
For broader preparation context, the D.E. Shaw interview process typically includes a recruiter screen, one or two technical phone screens, and a 4-5 round on-site loop covering coding, system design (at L4+ levels), and behavioral. Reports tagged on LeakCode show the round-by-round distribution and typical difficulty calibration. To browse questions filtered by round type and seniority, use the company hub linked above.
How To Practice This Type of Question
Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.
Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in D.E. Shaw reports consistently are the ones worth investing in; one-off niche problems are not.
During Your D.E. Shaw Round
Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.
The single most predictive failure mode in D.E. Shaw reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.