Ibm Interview Questions (13+ Questions)

Learn how to parse mixed-format log files with epoch timestamps to detect SYN flood attacks using Linux Bash commands. This guide covers handling heterogeneous log formats, extracting SYN events with case-insensitive matching, filtering by time windows, and generating detection reports essential for security monitoring, DDoS detection, and rapid incident response.

We have a container CPU test that's running from the image, my app CPU, and it performs CPU intensive computations. This container regularly spikes to 150 to a hundred percent of the CPU usage and our task is to limit that. We need to set 500 M meaning half CPU to achieve that. Type docker stat CPU test and it will show us in the real time the CPU utilization of this container. Inspect this container by using Docker inspect, and we grab CPU related keywords and everything is either null or zero, meaning that there's no CPU related limits set on this container. We will run docker, run in the detached mode. We will use hyphen CPUs zero five, which equals to 500 M, meaning we'll set a limit of half CPU to this container. The container is utilizing below 60%, around 50% of the CPU.

Safely recover from force-pushed remote branches by resetting your local repository to match the new remote state. Use git reset --hard origin/main to discard conflicting local history, resolve diverged branch errors, and restore sync with remote. Essential for team coordination after history rewrites, preventing merge conflicts, and maintaining repository consistency when leaders rewrite shared branches.

Imagine a common scenario when you've been working on some git repository on the feature ui and suddenly you need to do something to fix authentication issue on the main branch. For this, you have to create new hotfix branch, commit changes into that branch and then merge that with main branch. While you've been working on feature ui, you cannot simply change the branch because you have uncommitted changes. Before moving to main branch, we need to stash those changes, meaning to put them aside. Next, move to our main branch and to fix our authentication issue, we first need to create hotfix branch. Move to the main branch and merge our hotfix with main. And finally we delete the hotfix branch since we don't need it anymore. We need to move back to our work that we paused meaning to feature ui, and then rebase it from main. Finally, we need to move back the work that we stashed aside. For that, we need to type git stash pop.

def swim_in_water(grid: list[list[int]]) -> int:
n = len(grid)
min_heap = [(grid[0][0], 0, 0)]
visited = set([(0, 0)])
directions = [[0, 1], [1, 0], [0, -1], [-1, 0]]

while min_heap:
    t, r, c = heapq.heappop(min_heap)
    
    if r == n - 1 and c == n - 1:
        return t
        
    for dr, dc in directions:
        nr, nc = r + dr, c + dc
        if 0 <= nr < n and 0 <= nc < n and (nr, nc) not in visited:
            visited.add((nr, nc))
            heapq.heappush(min_heap, (max(t, grid[nr][nc]), nr, nc))
            
return 0

How to Retrieve and Calculate Employee Bonuses with SQL

To tackle the problem of constructing a SQL query that calculates and categorizes employee bonuses, follow these steps based on the given criteria and tables:

Objective:

Construct a SQL query to retrieve each employee's name, their c...

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Objective

In this interview question, you are given a sales table that tracks product sales across various regions and categories. Your task is to write a SQL query to compute the total sales for each region, segmented by the categories 'Electronics', 'Clothing', and 'Books'. The resulting out...

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Interview Question: Calculating Discounted Amount and Cumulative Revenue for Each Order

Objective

Write an SQL query to compute the discounted amount and cumulative revenue for each order in a sales table. The table includes columns for:

• order_date
• order_id
• original_amount
• `d...

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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 two tables, orders and products. Our job here is to parse the order date column from string into a real date format, and then drop any invalid dates. After that, we need to detect whether the order was placed on a weekend or not. If it's from Monday to Friday, then the output is false. If it's Saturday or Sunday, then the output is true. We will use a CTE, in other words, a common table expression, which is a temporary result set that we define at the top of our query. In Snowflake, we use data build, which is a framework that manages and organizes tables. We put it inside of ref function, and this whole thing is wrapped in double curly braces. We are more interested in inner join because it returns only the rows where there is a match in both tables. Within parse date column, we implement try to date function. This function converts a string into a real date type. If the string is invalid, it simply returns a null value. Day of week is a Snowflake function that takes a date and returns a number that represents the day of the week. Sunday is assigned with zero, Saturday is assigned with six. We check if the day of the week result is either zero or six, meaning Sunday or Saturday.

Practice a Snowflake SQL interview question tagged IBM that tests regular expressions and pattern matching with RLIKE or REGEXP. In a data validation scenario, you filter user-submitted text to keep only valid arithmetic expressions containing digits and operators. This hard difficulty question covers regex patterns in Snowflake, RLIKE for row filtering, and input validation logic commonly asked in data quality and engineering interviews.

Master financial data aggregation in PySpark. Learn how to join relational tables, multiply columns to calculate holding values, and group by multiple dimensions to compute daily private equity portfolio totals.

SQL Query for Department Interview Success Ratio

The objective of this SQL query task is to extract the name of each department along with its interview success ratio, sorted in descending order of the success ratio. In cases where two departments share the same success ratio, they are sorted al...

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SELECT
COALESCE(unique_queries, 0) AS unique_queries,
COUNT(*) AS employee_count
FROM
(
SELECT
e.employee_id,
COUNT(DISTINCT q.query_id) AS unique_queries
FROM
employees e
LEFT JOIN queries q ON e.employee_id = q.employee_id
AND EXTRACT(
MONTH
FROM
q.execution_date
) IN (7, 8, 9)
AND EXTRACT(
YEAR
FROM
q.execution_date
) = 2023
GROUP BY
e.employee_id
) AS employee_query_counts
GROUP BY
unique_queries
ORDER BY
unique_queries ASC;