Databricks Interview Questions (11+ Questions)

Learn how to diagnose and resolve disk space exhaustion issues on mounted volumes using Linux Bash commands. This guide covers checking filesystem usage, identifying largest files, freeing storage space, and verifying recovery, essential for troubleshooting storage capacity problems, preventing service failures, and maintaining application availability.

Configure Linux network namespaces and bridges for isolated container networking. Learn to create separate network segments with veth pairs, interconnect namespaces using Linux bridges, enable inter-namespace communication, and verify connectivity. This guide covers network namespace isolation, virtual ethernet configuration, bridge setup, IP forwarding, and routing between isolated network stacks. Essential for container networking troubleshooting, microservices development, understanding Docker/Kubernetes networking, and implementing custom network topologies in production environments.

def two_sum(numbers: list[int], target: int) -> list[int]:
l, r = 0, len(numbers) - 1

while l < r:
    cur_sum = numbers[l] + numbers[r]

    if cur_sum > target:
        r -= 1
    elif cur_sum < target:
        l += 1
    else:
        return [l + 1, r + 1]
        
return []

Implement a secure, automated credential-rotation flow using Secrets Manager, KMS, Lambda, SSM, SNS, and CloudWatch Logs with least-privilege IAM.

We will analyze sales dataset dimensions and calculate total revenue. Pandas is a library that was specifically designed for data analysis and manipulation. We are given one CSV file that is called Sales Data. Our job is, first of all, to analyze its size, classify it as small, medium, or large based on total amount of sales, and then calculate total revenue. Everything should be saved as JSON report. We need to read it into a data frame. Data frame is simply a table in memory with rows and columns. For rows and columns, we will implement the len function that simply returns the number of items in any list or collection. In order to find the total number of sales, we need to multiply rows by columns. Small one is considered the one with less than 10,000 sales, then medium in range between 10,000 and 99,999, and large, everything that exceeds 100,000 sales. In order to find the revenue, we need to multiply the quantity by price. We will use the sum function to add up all revenue amounts. Everything is wrapped inside of round function.

Spark is a big data framework that processes massive amounts of data across multiple computers at the same time. Instead of tables like in SQL, Spark uses data frames. We are given two files, orders.csv with 5,000 records, and customers.csv with 50 records. We need to join these two files together using a broadcast join, then count orders and print the number of distinct cities. We will use a regular inner join because we only want orders that have a matching customer. The only thing is how we perform that join. Instead of shuffling both data frames across the network, Spark takes the small data frame and sends a full copy of it to every worker. So each worker now has its own partition of the large data frame and small data frame. It means that it can perform the join right here without any movement. We don't want to shuffle this large data frame across the network. That's why we take the smallest one, because it is easier and cheaper to copy. We will also import the broadcast function from the library. header that is set to true uses the first row as column names, and inferSchema automatically detects data types for each column. Then we count orders per city with the help of group by.

This is a Snowflake question, which is a cloud-based data warehouse that uses SQL and all its concepts. We are given three tables: customers, orders, and products. Full name in the customers table stores first and last name together in one column, separated by space. We need to split them into two columns: first name and last name. Here we also have to split product info column into two separate ones. In Snowflake, we use data build tool, which is a framework that manages and organizes tables. We use the ref function wrapped in double curly braces, and then ref function finds the correct table in the Snowflake environment automatically. Join connects two tables based on a common column. We are more interested in inner join because it returns only the rows where there is a match in both tables. To split full name and product info columns, we will use split part function. This function takes three arguments, splits the string, and returns a specific part. Second argument is delimiter. It's basically the character where the cut happens.

Master data filtering and aggregation in PySpark. Learn how to filter rows by comparing two columns against each other, rename columns during a GroupBy operation, and count interaction occurrences.

Objective

To answer the interview question regarding SQL, you need to write an SQL query that calculates the average number of days taken to deliver orders after they have been shipped. Only orders with both a shipping date and a delivery date recorded should be included in the calculation.

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Detailed Explanation for SQL Interview Question on Unpurchased Product Categories

Objective

Write an SQL query to determine which product categories have not been purchased by each customer. The query should return a list of customers along with the categories they have not purchased, sor...

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Amazon operates the world's largest e-commerce marketplace with over 300 million active customers and 12 million products. QA testing of Amazon marketplace APIs requires comprehensive validation of product search, cart management, order processing, and inventory tracking to ensure reliable shopping ...

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