Bloomberg Interview Questions (10+ Questions)

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def find_order(num_courses: int, prerequisites: list[list[int]]) -> list[int]:
adj = {i: [] for i in range(num_courses)}
indegree = {i: 0 for i in range(num_courses)}

for crs, pre in prerequisites:
    adj[pre].append(crs)
    indegree[crs] += 1
    
q = deque()
for i in range(num_courses):
    if indegree[i] == 0:
        q.append(i)
        
res = []
while q:
    curr = q.popleft()
    res.append(curr)
    
    for neighbor in adj[curr]:
        indegree[neighbor] -= 1
        if indegree[neighbor] == 0:
            q.append(neighbor)
            
if len(res) == num_courses:
    return res
return []

Objective

In this SQL-based interview question, candidates are asked to write a query that retrieves the top three orders with the highest order values for each customer. The result should be accurately sorted by customer name in ascending order and then by order value in descending order.

##...

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Practice a hard Snowflake SQL interview question tagged Bloomberg that combines window functions, joins, and ranking. Given a mountain climbing registry, you must join two tables and use ROW_NUMBER or a similar window function to find the most recent climber per mountain. This question tests your ability to partition data, rank by date, and filter to the top result within each group.

Practice multi-level data aggregation in PySpark. Learn how to combine standard groupBy aggregations with Window functions to compute metrics across different categorical levels simultaneously.

Comprehensive Guide to Writing an SQL Query for Marketing Campaign Performance Report

Introduction:
If you're preparing for a data-oriented interview, you might encounter a scenario where you need to showcase your SQL skills by generating a detailed marketing campaign performance report. Th...

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def combination_sum(candidates: list[int], target: int) -> list[list[int]]:
res = []

def dfs(i, current_comb, total):
    if total == target:
        res.append(current_comb.copy())
        return
    if i >= len(candidates) or total > target:
        return
        
    current_comb.append(candidates[i])
    dfs(i, current_comb, total + candidates[i])
    
    current_comb.pop()
    dfs(i + 1, current_comb, total)
    
dfs(0, [], 0)
return res

def alien_order(words: list[str]) -> str:
adj = {c: set() for w in words for c in w}
in_degree = {c: 0 for w in words for c in w}

for i in range(len(words) - 1):
    w1, w2 = words[i], words[i + 1]
    min_len = min(len(w1), len(w2))
    
    if len(w1) > len(w2) and w1[:min_len] == w2[:min_len]:
        return ""
        
    for j in range(min_len):
        if w1[j] != w2[j]:
            if w2[j] not in adj[w1[j]]:
                adj[w1[j]].add(w2[j])
                in_degree[w2[j]] += 1
            break
            
q = deque([c for c in in_degree if in_degree[c] == 0])
res = []

while q:
    curr = q.popleft()
    res.append(curr)
    
    for neighbor in adj[curr]:
        in_degree[neighbor] -= 1
        if in_degree[neighbor] == 0:
            q.append(neighbor)
            
if len(res) != len(in_degree):
    return ""
    
return "".join(res)

Objective

In this comprehensive SQL interview question, you are challenged to write an SQL query that calculates the percentage change in product prices over time.

Task

Your task is to write an SQL query that:

1. Calculates the percentage change in product prices over time.
2. Uses a co...

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Master drag and drop automation with Selenium ActionChains. Learn element interaction, position validation, and swap verification testing....

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