If there is a long latency for a Java application running on a server, what could be the possible causes and how would you diagnose and resolve them?

You are tasked by a Cloud Provider to create a CDN Product similar to Cloudflare. Design the architecture for the CDN service, focusing on content caching, load balancing, and global content delivery.

Design a distributed file storage system similar to Google Drive or Dropbox, capable of storing and retrieving large amounts of data across a distributed network. Consider the following aspects:

How would you implement a caching proxy server? Describe the architecture, key components, and the steps involved in building it.

How can I efficiently perform a series of operations on an array to solve a given problem as part of a DevOps automation script, considering constraints like execution time and resources?

How can I efficiently merge two sets of CSV files using standard input, where the first line in each file identifies the dataset, ensuring a seamless data integration process?

How can I efficiently sort and remove duplicates from three different lists in Python, ensuring a single, sorted list as output?

What is an efficient dynamic programming approach to solve the classic stairway problem using Python that optimizes both time and space complexity?

How can Java collections be effectively integrated with SQL databases to enhance backend systems' performance and scalability?

Describe an efficient algorithm to calculate the median from a stream of data. How would you implement this in a real-time data processing system?

Given a dataset of 2 billion URLs and their sizes, describe an efficient algorithm to find the 95th percentile of all the sizes. Provide a brief explanation and a sample implementation using a distributed computing framework.

1. SQL Query for Top N Records: Write an SQL query to fetch the top 5 employees with the highest salaries from an employees table with columns employee_id, name, and salary.

2. SQL Query for Aggregate Function: Write an SQL query to find the average salary of employees in each department from a table employees with columns department_id, employee_id, and salary.

3. Python Function for Factorial: Write a Python function to compute the factorial of a given number.

4. Python List Comprehension: Write a Python one-liner using list comprehension to create a list of squares of the first 10 natural numbers.

5. Python String Manipulation: Write a Python function to reverse a given string.

Given two strings s1 and s2, write a Python function to check if s1 is a rotation of s2. Provide an explanation and the solution.

Given two data sets, build a Python application to combine the data and post the combined data into an API. Describe the approach and provide a sample implementation.

1. MapReduce Random Lines from Large Log Files: Using the MapReduce framework, how would you get X amount of random lines from large log files?

2. Managing a Pool of IPs: Describe how you would design a system to manage a pool of IP addresses, ensuring efficient allocation and deallocation.

3. Rotated Array Search: Given a rotated sorted array, write a Python function to search for a target value. Explain your approach and provide the solution.

Implement a function in Python to calculate the square root of any given number up to 2 decimal points precision. Then, optimize the function and implement a caching mechanism to eliminate redundant calculations. Provide an explanation and the solution.

How do you write a Python program to get the top ten data entries based on the last column from a comma-separated flat file (CSV)?

How can a DevOps Engineer design a web scraping utility using Python and Beautiful Soup for data extraction from a chosen website and store the results in MongoDB?

Write a function to reverse a singly linked list. You should be able to transform the list from a sequence such as 1→2→3→41→2→3→4 to 4→3→2→14→3→2→1 using your function.

Given a dataset containing customer transaction records in a CSV format, describe how you would create a data quality dashboard to track key metrics like missing values, duplicate entries, and data type mismatches. Specify the tools and technologies you would use.

Write a basic Python function to print a diamond of asterisks given an integer n representing the number of rows in the upper half of the diamond (excluding the middle row).

Input An integer n.

Example For n = 3, the output should be:

  *
 ***
*****
 ***
  *

Written question: How would you forecast pricing for this particular model of TV based on this sales data from last year?

Explain how you would use linear regression to predict a continuous variable and how you would interpret the p-values of the regression coefficients.

What factors should be used in generating a forecast for an item, and what should be the process of generation?

How would you identify and address potentially under-performing predictive models in a production environment?

How would you handle skewed distributions and imbalanced classes in a dataset intended for machine learning? Provide specific techniques and tools you would use.

Here is sample data for the transactions table:

| customer_id | month      | amount_spent |
|-------------|------------|--------------|
| 1           | 2022-10-01 | 100.50       |
| 1           | 2022-11-01 | 150.75       |
| 1           | 2022-12-01 | 120.00       |
| 2           | 2022-10-01 | 200.00       |
| 2           | 2022-11-01 | 180.25       |
| 2           | 2022-12-01 | 210.50       |
| 3           | 2022-10-01 | 50.25        |
| 3           | 2022-11-01 | 75.00        |
| 3           | 2022-12-01 | 90.00        |

Additionally, assume the dataset also provides churn information with the following columns: churn_info table:

| customer_id | has_churned |
|-------------|-------------|
| 1           | 0           |
| 2           | 1           |
| 3           | 0           |

Write a SQL query to:

1. Calculate the average monthly spend per customer for the past year.

Write a Python script that uses the dataset to: 2. Calculate the probability of customer churn using logistic regression. Assume you have columns for customer_id, month, amount_spent, and a target column has_churned.

Write a SQL query to find the top 5 customers based on total expenditure over the last year.

Additionally, create a Python script to plot a bar chart showing total monthly orders over the same period.

Sample Input Data

orders Table:

| order_id | customer_id | order_date | total_amount |
|----------|-------------|------------|--------------|
| 1        | 1           | 2022-11-20 | 150.75       |
| 2        | 2           | 2022-12-05 | 200.50       |
| 3        | 1           | 2022-12-20 | 75.00        |
| 4        | 3           | 2023-01-05 | 300.00       |
| 5        | 2           | 2023-01-15 | 180.75       |
| 6        | 1           | 2023-02-10 | 120.00       |
| 7        | 4           | 2023-02-20 | 250.00       |
| 8        | 3           | 2023-03-01 | 90.00        |

customers Table:

| customer_id | customer_name | signup_date |
|-------------|---------------|-------------|
| 1           | Alice         | 2021-05-01  |
| 2           | Bob           | 2022-03-15  |
| 3           | Carol         | 2022-07-22  |
| 4           | Dave          | 2022-09-19  |

Write a SQL query to find the top 3 products based on total sales value.

Additionally, create a Python script to plot a line graph showing daily total sales over the past month.

Lastly, explain how you would use Excel to calculate and visualize the sales trend.

Input:

transactions Table:

| transaction_id | date       | product_id | quantity | price  |
|----------------|------------|------------|----------|--------|
| 1              | 2023-09-01 | 101        | 2        | 10.00  |
| 2              | 2023-09-01 | 102        | 1        | 20.00  |
| 3              | 2023-09-02 | 101        | 1        | 10.00  |
| 4              | 2023-09-02 | 103        | 3        | 15.00  |
| 5              | 2023-09-03 | 102        | 2        | 20.00  |
| 6              | 2023-09-03 | 101        | 2        | 10.00  |

Given a dataset that contains the following columns: session_id, user_id, event_type, and timestamp. The event_type column can have values such as view, add_to_cart, and purchase. Write a Python script to calculate the cart abandonment rate, defined as the percentage of sessions where items were added to the cart, but a purchase was not completed.

Input: session_data.csv looks like this:

| session_id | user_id | event_type   | timestamp           |
|------------|---------|--------------|---------------------|
| 1          | 101     | view         | 2023-09-01 10:00:00 |
| 1          | 101     | add_to_cart  | 2023-09-01 10:05:00 |
| 2          | 101     | view         | 2023-09-01 11:00:00 |
| 2          | 101     | purchase     | 2023-09-01 11:15:00 |
| 2          | 101     | add_to_cart  | 2023-09-01 11:10:00 |
| 3          | 102     | view         | 2023-09-02 09:00:00 |
| 3          | 102     | add_to_cart  | 2023-09-02 09:10:00 |
| 4          | 103     | view         | 2023-09-02 10:00:00 |
| 4          | 103     | purchase     | 2023-09-02 10:15:00 |

You are given a dataset sales_data with a column sales_amount representing the sales figures for different transactions. Write Python code to calculate the mean, median, and standard deviation of the sales amounts.

Write an SQL query to merge these tables, sort the result by department_name and employee_name, and rank employees within their departments.

Additionally, write a small Python program using list comprehensions to filter and sort a list of dictionaries representing employees.

employees Table:

| employee_id | employee_name | department_id |
|-------------|---------------|---------------|
| 1           | Alice         | 10            |
| 2           | Bob           | 10            |
| 3           | Carol         | 20            |
| 4           | Dave          | 20            |
| 5           | Eve           | 30            |

departments Table:

| department_id | department_name |
|---------------|-----------------|
| 10            | Sales           |
| 20            | Marketing       |
| 30            | Finance         |

You are given 2 eggs and a building with n floors. The goal is to determine the highest floor from which an egg can be dropped without breaking. Describe a strategy to minimize the number of drops required in the worst-case scenario and write a Python program to implement this strategy for a building with a given number of floors n.

You have a CSV file driver_requests.csv with columns driver_id, num_requests, and hours_driven. Write a Python program to calculate the weighted average of requests per driver, weighted by the number of hours driven.