Microsoft Interview Questions (20+ Questions)

We have a Linux server and we need to evaluate its performance. We need to use uptime and determine how long the server has been running and extract the 15 minute load average and save uptime to uptime txt, and then load average to the load average txt files. Those load averages stands for five, 10, and 15 minute load average. Since we've been asked to provide 15 minute load average, we'll use the last line.

We have files in ETC folder that are at risk of being lost due to accidental changes or deletion. We need to write a script that accepts target backup path as a command line argument, creates a compressed archive of ETC directory with the naming format ETC backup year, month, day Tar gc, and automatically removes backups older than seven days. Then we need to create a CR job that will run file every time at 2:00 AM in the morning. Once file is created, give it an execution permission. We create a conditional statement where we say that if the argument number one, meaning the expected target location is not provided, it'll throw an error saying backup directory path is required. Then the main command to archive the file. We implement this option that we need to delete files that older than seven days. We'll use find command for this, and this M time plus seven delete command to delete files that are older than seven days. Chron TAP E will edit the chron tab. We'll type zero, two star, star, star, which will run it every day at 2:00 AM.

We'll have a container where child processes are turning into zombies after they finish. The main process runs as PID 1, but doesn't know how to clean up, which is reap its dead children. When a process finishes executing, it doesn't fully disappear immediately. Rather, it stays in the process table...

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We already have a working CI/CD pipeline that builds and deploys a Docker image. The problem is that it tags everything as myapp:latest. So there's no way to tell which commit a running container came from. Our task here is that we need to replace latest with a short commit SHA. Every commit in Git ...

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We have a repo with a Dockerfile, but no CI/CD pipeline setup. We are given a starter workflow file with just the basic structure, and we need to complete it. The goal is that every time someone pushes to main, a Docker image named app gets built and tagged with a short commit SHA. Every commit in Git has a unique hash. The short version is just the first seven characters. We get it by running git rev-parse --short head. Each step runs in its own shell, so a regular variable in one step doesn't exist in the next. GitHub Actions provides a mechanism called github_env that lets us share values across steps. Actions/checkout pulls the code from the repo into this runner so that the steps after it can actually access the Dockerfile and build the image. It grabs the first seven characters of the current commit hash and puts it in a variable called TAG. This builds a Docker image and tags it as app:env.TAG.

def find_kth_largest(nums: list[int], k: int) -> int:
min_heap = []

for num in nums:
    heapq.heappush(min_heap, num)
    
    if len(min_heap) > k:
        heapq.heappop(min_heap)
        
return min_heap[0]

def ladder_length(begin_word: str, end_word: str, word_list: list[str]) -> int:
if end_word not in word_list:
return 0

word_list.append(begin_word)
adj = defaultdict(list)

for word in word_list:
    for j in range(len(word)):
        pattern = word[:j] + "*" + word[j+1:]
        adj[pattern].append(word)
        
q = deque([begin_word])
visited = set([begin_word])
res = 1

while q:
    for _ in range(len(q)):
        curr_word = q.popleft()
        
        if curr_word == end_word:
            return res
            
        for j in range(len(curr_word)):
            pattern = curr_word[:j] + "*" + curr_word[j+1:]
            for neighbor in adj[pattern]:
                if neighbor not in visited:
                    visited.add(neighbor)
                    q.append(neighbor)
                    
    res += 1
    
return 0

Inspect a server certificate to identify missing Subject Alternative Name (SAN) entries and reissue it with the correct DNS names.

How to Determine Monthly and Quarterly Sales Trends from the monthly_metrics Table

In an SQL-based data analytics interview, you might be asked to analyze sales data trends. One such challenge could be to determine both monthly and quarterly sales trends from a given table. Let's break down t...

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This is a Snowflake question, which is a cloud-based data warehouse that uses SQL and all its main concepts. We are given only one table that is called Transactions. Each row represents one purchase made by a customer. First, for each transaction, we need to find what product the customer bought most recently before the current one. Second, we need to create a new column, combine the date and that previous product into one string, and use the word none when there is no previous product. A CTE, or Common Table Expression, is a temporary result set that we define at the top of our query. Lag specifically within product ID row looks backwards, reaches to the previous row, and grabs its value. Partition by creates sort of a group for each customer. We wrap previous product in COALESCE function. COALESCE takes a list of values and returns the first one that is not null.

WITH joined AS (
SELECT a., r.climate
FROM {{ ref("animals") }} AS a
INNER JOIN {{ ref("regions") }} AS r
ON a.region = r.region
)
SELECT
species,
climate,
ROUND(AVG(age), 2) AS avg_age,
FLOOR(AVG(weight)) AS avg_weight,
COUNT() AS total_animals
FROM joined
GROUP BY species, climate

Master daily sales aggregation in PySpark. Learn how to join transaction tables with product catalogs and use multi-column GroupBy operations to calculate total quantities sold per category per day.

from collections import defaultdict
import bisect

class LogAggregator:
def init(self):
self.logs = defaultdict(list)

def record(self, timestamp, key):
    self.logs[key].append(timestamp)

def count(self, key, start, end):
    if key not in self.logs:
        return 0
    timestamps = self.logs[key]
    left = bisect.bisect_left(timestamps, start)
    right = bisect.bisect_right(timestamps, end)
    return right - left

Understanding and Calculating the Cart Abandonment Rate in SQL

When managing an e-commerce platform, monitoring user behavior is crucial to optimize sales and customer satisfaction. One significant metric to track is the cart abandonment rate. This metric highlights the percentage of shopping c...

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SELECT
employee_name,
department_name,
salary
FROM
(
SELECT
e.employee_name,
d.department_name,
e.salary,
ROW_NUMBER() OVER (
PARTITION BY
d.department_name
ORDER BY
e.salary DESC,
e.employee_name ASC
) AS salary_rank
FROM
employee e
INNER JOIN department d ON e.department_id = d.department_id
) AS ranked_employees
WHERE
salary_rank <= 3
ORDER BY
department_name ASC,
salary DESC,
employee_name ASC;

def plus_one(digits: list[int]) -> list[int]:
n = len(digits)

for i in range(n - 1, -1, -1):
    if digits[i] < 9:
        digits[i] += 1
        return digits
    digits[i] = 0
    
return [1] + digits

def subsets_with_dup(nums: list[int]) -> list[list[int]]:
nums.sort()
res = []

def dfs(i, current_subset):
    if i == len(nums):
        res.append(current_subset.copy())
        return
        
    current_subset.append(nums[i])
    dfs(i + 1, current_subset)
    
    current_subset.pop()
    while i + 1 < len(nums) and nums[i] == nums[i + 1]:
        i += 1
        
    dfs(i + 1, current_subset)
    
dfs(0, [])
return res

Analyzing Order Statistics in SQL: A Step-by-Step Guide to Writing the Perfect Query

Objective

In this task, you need to write an SQL query to effectively analyze the order statistics from a given table of orders. The table contains crucial information about each order, including its stat...

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Master basic element interaction testing with Selenium. Learn button clicks, element verification, and dynamic content validation....

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