Jpmorgan Interview Questions (9+ Questions)

We have a scenario with slash var directory that contains logs from multiple applications and we need to plan some cleanup. We're tasked to find count of all files ending with dot log that is anywhere in slash var folder, but also we need to include sub directories and we need to find only files, not directories, the count of them. Additionally, we need to identify larger log files and save their count to large log file count txt. The way to find those files is with command called find. We can type type F, which will give us only files under slash var folder. We can filter out only log file extensions by typing name and we can use wildcard dot log. We will use WC and then we will count lines L. Then pipe this out into log count txt. Next we need to find files that are larger than 512 kb. We will add hyphen size flag with 512 kb.

Reduce storage overhead and accelerate builds by aligning Dockerfiles to share common layers across application versions. Diagnose layer differences using docker history, understand how instruction order and base images affect layer reuse, eliminate metadata inconsistencies, and achieve sub-3-layer variance between versions. Essential for multi-version deployments, registry optimization, container versioning strategies, and efficient image storage in production environments.

def can_complete_circuit(gas: list[int], cost: list[int]) -> int:
if sum(gas) < sum(cost):
return -1

current_tank = 0
start_index = 0

for i in range(len(gas)):
    current_tank += gas[i] - cost[i]
    
    if current_tank < 0:
        start_index = i + 1
        current_tank = 0
        
return start_index

Practice a medium difficulty Snowflake SQL interview question tagged JPMorgan that tests deduplication, CTEs, and INNER JOIN. In a manufacturing scenario, you must remove duplicate rows from two tables using SELECT DISTINCT or ROW_NUMBER before joining them on a shared key. This question evaluates your ability to handle data quality issues before performing joins, a skill commonly tested in financial services and data engineering interviews.

Master data cleansing techniques in PySpark. Learn how to filter invalid primary keys, use regular expressions for date validation, and drop duplicate records before performing relational joins.

import heapq
from collections import defaultdict

class TopKFrequent:
def init(self, k):
self.k = k
self.freq = defaultdict(int)

def add(self, num):
    self.freq[num] += 1

def topK(self):
    items = [(-count, num) for num, count in self.freq.items()]
    top = heapq.nsmallest(self.k, items)
    return [num for _, num in top]

SELECT
card_name,
MAX(issued_amount) - MIN(issued_amount) AS issued_difference
FROM
card_issuance
GROUP BY
card_name
ORDER BY
issued_difference DESC;

WITH
ranked AS (
SELECT
card_name,
issued_amount,
ROW_NUMBER() OVER (
PARTITION BY
card_name
ORDER BY
issue_year,
issue_month
) AS rn
FROM
monthly_cards_issued
)
SELECT
card_name,
issued_amount
FROM
ranked
WHERE
rn = 1
ORDER BY
issued_amount DESC;

Fetch and parse HTML from a web page, extract all hyperlinks, filter external domains excluding the source domain, and save unique results to a text file using Python and BeautifulSoup.