Airbnb Interview Questions (13+ Questions)

Learn how to capture and analyze HTTP network traffic on port 80 using Bash commands and packet analysis tools. This guide shows how to verify web server connectivity by examining TCP handshakes and packet flow, helping diagnose HTTP communication issues and network configuration problems in production environments.

Learn how to sort and organize multi-server log files by timestamp and hostname using Linux Bash commands. This guide covers combining out-of-order logs from distributed systems, applying multi-key sorting for chronological analysis, and generating organized log reports, essential for incident investigation and distributed system troubleshooting.

Kubernetes Pod Security Context Hardening: secure-app Deployment prod namespace. Remediate insecure container configurations by enforcing non-root user execution, read-only filesystems, and strict capability dropping. Implement least-privilege principles to prevent privilege escalation and reduce the attack surface. Essential for CKS preparation, container compliance, and adhering to Kubernetes Pod Security Standards (PSS).

Kubernetes NetworkPolicy Fix: analytics collector→processor Port 9090 Connectivity. Diagnose and resolve connection timeouts between collector deployment (app=collector) and processor deployment (app=processor) on port 9090 in analytics namespace. Deploy egress NetworkPolicy allowing precise inter-deployment traffic while maintaining deny-all default isolation. Perfect for microservices communication, zero-trust networking, service mesh replacement, compliance security, lateral movement prevention, and namespace isolation.

Read multiple Excel files from a directory, extract each sheet, and save them as separate CSV files with a structured naming convention using Python pandas.

Group orders by status and find which status has the highest count.

WITH calculated AS (
SELECT
e.employee_id,
e.name,
e.position,
CASE
WHEN p.hours_worked <= 40
THEN p.hours_worked * p.hourly_rate
ELSE
(40 * p.hourly_rate) + ((p.hours_worked - 40) * p.hourly_rate * 1.5)
END AS pay
FROM {{ ref("employees") }} e
INNER JOIN {{ ref("payroll") }} p
ON e.employee_id = p.employee_id
)
SELECT * FROM calculated

WITH normalized_subs AS (
SELECT
user_id,
start_date,
COALESCE(end_date, '9999-12-31'::DATE) AS end_date
FROM {{ ref("subscriptions") }}
),
active_watches AS (
SELECT
w.user_id,
w.watch_duration
FROM {{ ref("watch_history") }} w
INNER JOIN normalized_subs s
ON w.user_id = s.user_id
WHERE w.watch_date >= s.start_date
AND w.watch_date <= s.end_date
)
SELECT
user_id,
SUM(watch_duration) AS total_watch_time
FROM active_watches
GROUP BY user_id

WITH extracted AS (
SELECT
store_id,
product_name,
category,
units_sold,
description,
COALESCE(
TRY_CAST(
REGEXP_SUBSTR(description, '\[(\d+)% off\]', 1, 1, 'e', 1)
AS FLOAT
) / 100,
0
) AS discount
FROM {{ ref("products") }}
)
SELECT * FROM extracted

Objective: Determining Unique Daily User Logins with SQL

When faced with the requirement to determine the number of unique users who logged in each day from a set of login events, you can achieve this with a well-structured SQL query. Here's a comprehensive explanation of how you can approach t...

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def erase_overlap_intervals(intervals: list[list[int]]) -> int:
intervals.sort(key=lambda x: x[1])

removals = 0
prev_end = float("-inf")

for start, end in intervals:
    if start >= prev_end:
        prev_end = end
    else:
        removals += 1
        
return removals

Crafting the Perfect SQL Query for Categorizing and Analyzing Delayed Flights by Airline

Objective

Develop an SQL query to categorize delayed flights based on delay duration and determine both the frequency and percentage share of each delay category for each airline.

Query Explanati...

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Master dropdown testing with Selenium. Learn option enumeration, selection validation, and dropdown interaction techniques....

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