Definition for a binary tree node.

class TreeNode:

def init(self, val=0, left=None, right=None):

self.val = val

self.left = left

self.right = right

def is_balanced(root: Optional[TreeNode]) -> bool:
def dfs(node):
if not node:
return 0

    left = dfs(node.left)
    if left == -1: return -1
    
    right = dfs(node.right)
    if right == -1: return -1
    
    if abs(left - right) > 1:
        return -1
        
    return 1 + max(left, right)
    
return dfs(root) != -1

def is_n_straight_hand(hand: list[int], group_size: int) -> bool:
if len(hand) % group_size != 0:
return False

count = Counter(hand)
min_heap = list(count.keys())
heapq.heapify(min_heap)

while min_heap:
    first = min_heap[0]
    
    for i in range(first, first + group_size):
        if count[i] == 0:
            return False
            
        count[i] -= 1
        
        if count[i] == 0:
            if i != min_heap[0]:
                return False
            heapq.heappop(min_heap)
            
return True

Aggregate sensor time-series data into 15-minute intervals using pandas resample, calculating mean, min, and max statistics while correctly handling partial windows at data boundaries.

SELECT
t.experiment_id,
t.temperature * p.pressure AS result
FROM {{ ref("temperatures") }} t
INNER JOIN {{ ref("pressures") }} p
ON t.experiment_id = p.experiment_id
ORDER BY t.experiment_id ASC

Fetch JSON data from a weather API endpoint, parse nested forecast structure to extract temperature values, and save results as a JSON list in Python.

SELECT
part,
assembly_step
FROM
parts_assembly
WHERE
finish_date IS NULL;

SELECT
measurement_day,
SUM(
CASE
WHEN measurement_value % 2 != 0 THEN measurement_value
ELSE 0
END
) AS odd_sum,
SUM(
CASE
WHEN measurement_value % 2 = 0 THEN measurement_value
ELSE 0
END
) AS even_sum
FROM
sensor_measurements
GROUP BY
measurement_day
ORDER BY
measurement_day;

def rotate(matrix: list[list[int]]) -> list[list[int]]:
n = len(matrix)

# Transpose
for i in range(n):
    for j in range(i + 1, n):
        matrix[i][j], matrix[j][i] = matrix[j][i], matrix[i][j]
        
# Reverse rows
for row in matrix:
    row.reverse()
    
return matrix

Writing an SQL Query to Categorize Loans Based on Default Rates

Objective

This interview question aims to evaluate your ability to write an SQL query that categorizes loans into different risk categories based on their default rates and then retrieves a list of these loans, ordered by defa...

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How to Find Customers with Above-Average Purchase Amounts Using SQL

When preparing for a database management or SQL interview, understanding how to handle specific data queries is crucial. One common interview question involves finding customers whose purchase amounts exceed the average within ...

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Master element attribute checking with Selenium. Learn basic attribute validation and existence testing for beginners....

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