Intel Interview Questions (7+ Questions)

Query a SQLite database table using Python's sqlite3 module, fetch all records, and export the results to a CSV file with proper headers.

def daily_temperatures(temperatures: list[int]) -> list[int]:
res = [0] * len(temperatures)
stack = [] # Stores indices

for i, t in enumerate(temperatures):
    while stack and t > temperatures[stack[-1]]:
        prev_index = stack.pop()
        res[prev_index] = i - prev_index
    stack.append(i)
    
return res

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

while l <= r:
    m = (l + r) // 2
    if nums[m] > target:
        r = m - 1
    elif nums[m] < target:
        l = m + 1
    else:
        return m
return -1

How to Answer Terraform Configuration Interview Questions Effectively

When working on a cloud infrastructure project, particularly one requiring dynamic resource naming and tracking, your ability to create a well-structured data organization system is crucial. In a Terraform configuration, this...

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We need to do the data processing and CSV export using Pandas and SQLite. We are given a SQLite database that is called Sales. It contains three tables: customers, orders, and items. SQLite is a lightweight database that stores everything in a single file. We need to connect to the database, then run an SQL query to join all three tables that we had, load the results into Pandas, calculate revenue metrics per customer, and export everything to CSV file. 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. We will implement the read_sql_query function. We build the total amount column by multiplying quantity with unit price. When we group by customer ID, we will put all rows belonging to the same customer together. In order to calculate the revenue percentage, we will need to divide each customer's total by the overall revenue.

WITH joined AS (
SELECT
pr.product_id,
pr.manufacturing_date,
pr.manufacturing_location,
p.product_name,
p.product_type
FROM {{ ref("production_records") }} pr
INNER JOIN {{ ref("products") }} p
ON pr.product_id = p.product_id
)
SELECT
product_id,
manufacturing_date,
manufacturing_location,
product_name,
product_type,
ROW_NUMBER() OVER (ORDER BY manufacturing_date ASC) AS row_number
FROM joined

SELECT
t.trans_id,
t.trans_amt,
t.date,
c.cust_id,
c.first_name,
c.last_name,
c.age
FROM {{ ref("transactions") }} t
CROSS JOIN {{ ref("customers") }} c