Airbnb Interview Questions (13+ Questions)

We suspect that web service isn't receiving HTP requests and we need to confirm network traffic on port 80. We're tasked to capture network packets that are incoming and outgoing from port 80. We'll use TCP dump for this. Main command to solve this question will be TCP dump hyphen i, meaning interface any. We'll use all network interfaces that are on this host machine and C 10, meaning we'll capture only first 10 packets. And then we'll write this output into this file. And the last part is our filter. We'll use port 80. Alternative filters would be host and then IP address, or we can use port range and other filters that TCP dump supports. Next we need to read the speed cap file and save this under some file name. Finally, we need to read this CAT file, and then we need to run this script.

We have a log file under TMP app combined log with entries from multiple servers in mixed order. Here's the structure of the log file. We have date, time, host name, and then the log itself. We need to sort combined log file by timestamp meaning date, then time, and then by host name alphabetically and saved output to sorted log file. We'll use sort command. And to sort, we need to use the K flag. We will use K one comma one. It starts by column one and ends at column one, the sorting. Then we do second sorting two comma two, and then again K three comma three. We'll sort by date then by time and then by host name. We need to add the name of the target file that we would like to sort and the name of the file where we need to save the output.

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.

We will convert Excel files with multiple sheets to individual CSV files using Pandas. We have a directory that contains multiple Excel files with many sheets inside of it. We are required to convert every single sheet from Excel file into its own separate CSV file. The naming convention for each output is filename_sheetname.csv. We import Pandas library that we alias as PD, then OS library that interacts with the operating system. We use it to list all files in a directory and build a complete file path. And lastly, the Pathlib library that works with file paths. We define two path variables: excel_dir, the folder where our Excel files are stored, and output_dir, where we save the converted CSV files. We open the Excel file using the read_excel function, where as a first argument, we define the full path, and we set sheet name to None so that Pandas will read all sheets at once. We save the current sheet as a CSV file using to_csv function, and then we set index to false so that Pandas won't add an extra row number column to the output.

Spark is a big data framework that processes massive amounts of data across multiple machines. Instead of tables, Spark uses data frames. We are given only one file, orders.csv, with 5,000 records. Each order has a status, like completed, canceled, ongoing, and so on. Our job here is to find which status appears most frequently. There are two types of transformations, narrow and wide. In narrow transformation, each row on the left executor goes directly to the row on the right executor. It means that no data moves between executors, and each partition stays on the same machine and gets processed independently. But when it comes to wide transformations, rows from one executor can end up on a completely different executor. Here, data is able to move between machines, and this is called a shuffle. The only problem is that wide transformation requires more network traffic and more time. When we use group by, we do the wide transformation because it makes the rows with the same status move onto the same executor. Count finds the number of rows in each group, and ordering by count, ascending set to false, which means that everything is sorted in descending order.

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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