Troubleshoot service connectivity by aligning selectors with pod labels. Fix api-svc selector mismatch that prevents endpoint population, restore DNS resolution for api-svc.shop.svc.cluster.local, and enable service accessibility. Essential for service discovery, internal cluster networking, DNS resolution, load balancing, and ensuring pods are properly discovered by their services.

Troubleshoot and fix Kubernetes RBAC and ServiceAccount configuration issues preventing pod API access. Diagnose authentication failures, enable token mounting, assign correct ServiceAccount, and fix RoleBinding namespace settings to restore API connectivity. Essential for pod-to-API communication, service discovery, internal monitoring, and applications that need to interact with the Kubernetes control plane.

Parse a list of URLs using Python regex to extract domain names including subdomains, handling various protocols, ports, and URL formats.

def check_inclusion(s1: str, s2: str) -> bool:
if len(s1) > len(s2):
return False

s1_count = [0] * 26
window_count = [0] * 26

for i in range(len(s1)):
    s1_count[ord(s1[i]) - ord('a')] += 1
    window_count[ord(s2[i]) - ord('a')] += 1
    
if s1_count == window_count:
    return True
    
l = 0
for r in range(len(s1), len(s2)):
    window_count[ord(s2[r]) - ord('a')] += 1
    window_count[ord(s2[l]) - ord('a')] -= 1
    l += 1
    
    if s1_count == window_count:
        return True
        
return False

Objective

The task is to write a SQL query to derive insightful metrics for a business analysis, focusing on the performance of different regions. You are required to calculate the total number of orders, the average order value, and total revenue from the customers and orders tables, group...

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Load a sales dataset with missing values, identify column data types, and replace nulls conditionally using pandas - numeric columns with 0 and text columns with "Unknown".

Read a Parquet file with multiple columns using pandas, select only the required columns for analysis, and write the subset to a new Parquet file.

Overview:

In SQL interviews, a common question tests your ability to calculate financial metrics from transaction data. One such problem involves calculating yearly revenue, previous year's revenue, and the percentage growth in revenue on a year-over-year basis. This type of question assesses your skills in SQL aggregation, date functions, and joins.

Objective:

Develop an SQL query that computes the following metrics from the financials table:

1. Year.
2. Yearly Revenue for each year, rounded to two decimal places.
3. Previous Year's Revenue: If there is no data for the previous year, this value should be NULL.
4. Percentage Growth in Revenue Year-Over-Year: This should be calculated as ((current year's revenue - previous year's revenue) / previous year's revenue) * 100, rounded to two decimal places. If there is no previous year's revenue, this value should be NULL.

Sample SQL Query:

WITH yearly_revenue AS (
    SELECT
        EXTRACT(YEAR FROM transaction_date) AS year,
        ROUND(SUM(amount), 2) AS yearly_revenue
    FROM
        financials
    GROUP BY
        EXTRACT(YEAR FROM transaction_date)
)
SELECT
    yr1.year,
    yr1.yearly_revenue,
    yr2.yearly_revenue AS previous_revenue,
    ROUND(((yr1.yearly_revenue - yr2.yearly_revenue) / yr2.yearly_revenue) * 100, 2) AS growth_percentage
FROM
    yearly_revenue yr1
    LEFT JOIN yearly_revenue yr2 ON yr1.year = yr2.year + 1
ORDER BY
    yr1.year ASC;

Explanation of the SQL Logic:

1. The yearly_revenue common table expression (CTE) calculates the yearly revenue for each year.
2. We then join the yearly_revenue CTE with itself to get the previous year's revenue.
3. The outer query selects the year, current year's revenue, previous year's revenue, and calculates the growth percentage.
4. Results are displayed with the yearly data ordered in ascending order.

This query ensures that your results are accurate and easily interpretable, aligning with best practices for presenting financial data. Moreover, it demonstrates your proficiency in handling complex SQL tasks, which is essential for database management roles.

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_same_tree(p: Optional[TreeNode], q: Optional[TreeNode]) -> bool:
if not p and not q:
return True

if not p or not q or p.val != q.val:
    return False
    
return is_same_tree(p.left, q.left) and is_same_tree(p.right, q.right)