IBM Interview Tips: Real Questions Answers & OA Interviews
IBM's hiring process typically begins with an initial resume screening designed to assess an applicant's background and technical expertise in line with the position. Candidates who pass the resume screening are often required to complete an Online Assessment (OA), which focuses on coding and problem-solving skills and is an important gateway to subsequent stages.
After successfully passing the OA, candidates move on to one or two rounds of phone interviews. These phone interviews focus on technical skills, but also include behavioral questions to assess the candidate's fit with IBM's corporate culture. Applicants who pass the phone interviews move on to final onsite interviews (Onsite Interviews).
IBM's on-site interviews typically consist of three to four rounds, covering both technical and behavioral aspects. Interviews are structured in a variety of ways and may include programming challenges, system design questions, and behavioral interviews designed to thoroughly assess a candidate's problem-solving abilities, coding skills, and fit with IBM's values. The overall interview process typically ranges from moderate to challenging, depending on the requirements of the position.
IBM OA usually asks 2 simpler coding questions, but in a tighter time frame, here they areCSOAsupport Interview Support TeamA real-life example of spending 20 minutes helping Candidate A answer questions during an interview.
OA Interview Question 1
Solution Ideas
The core principle of this problem is similar to the previous one: given a finite resource (here k How can we maximize the number of tasks we can complete? Since each task has a fixed processing time, the smartest thing to do is to use a greedy strategy - always prioritize the tasks that take the shortest amount of time. This way, you can cram the most tasks into a limited amount of time.- Identification of available tasks: First, we need to find out all the available tasks that can be "seized". The total number of task types is n species, numbered from 1 to n, corresponding to processing times from 1 to n minutes. We will simply exclude those that are already running the m tasks (they are in the arr array).
- Sort by processing time: Fortunately, the remaining available tasks have been automatically ordered by processing time (1, 2, 3...). This means that we can start directly with the task that takes the shortest time.
- segment performance: We start with the task that takes the least amount of time. If our free time (kIf we have enough, we "process" the task, subtract the time it takes from the budget, and move on to the next, less time-consuming task. We repeat this process until we run out of resources or we can't take on any more new tasks.
- Calculated total: Finally, add the number of our newly completed tasks to the number of tasks that were already running in the m tasks, which is the number of different tasks we handle in total.
def max_tasks(n, arr, k).
"""
Calculates the maximum number of different tasks that can be completed
with the given resources.
Args.
n: The total number of task types, numbered 1 to n. arr: An array representing the m tasks that are already running.
Args: n: The total number of task types, numbered 1 to n. arr: An array representing the m tasks that are already running.
k: The total available computing resources (in minutes).
Returns: int: The maximum total number of different tasks that are already running.
int: The maximum total number of different tasks that can be handled.
"""
# 1. Figure out which tasks are not yet running
running_tasks = set(arr)
available_tasks = []
for i in range(1, n + 1): if i not in running_tasks
available_tasks = [] for i in range(1, n + 1): if i not in running_tasks.
available_tasks.append(i)
# 2. Greedy approach: process the quickest tasks first
newly_completed_tasks = 0
for task_time in available_tasks: if k >= task_time: k
if k >= task_time: k -= task_time
k -= task_time
newly_completed_tasks += 1
k -= task_time newly_completed_tasks += 1
# Not enough resources, so we have to stop here
k -= task_time newly_completed_tasks += 1 else.
# 3. Add the newly completed tasks to the ones already running
total_tasks = len(arr) + newly_completed_tasks
return total_tasks
# Example.
# Let's say we have n=10 tasks, m=3 tasks already running (with IDs 2, 5, 8), # and k=15 minutes of free resources.
# and k=15 minutes of free resources.
n_example = 10
arr_example = [2, 5, 8]
k_example = 15
result = max_tasks(n_example, arr_example, k_example)
print(f "The maximum total number of different tasks we can handle is: {result}") # Output: 7
# Breakdown.
# The tasks that are available are 1, 3, 4, 6, 7, 9, 10. # Greedy pickup.
# Greedy picks.
# - Process task 1 (costs 1), k becomes 14.
# - Process task 3 (costs 3), k becomes 11.
# - Process task 4 (costs 4), k becomes 7.
# - Process task 6 (costs 6), k becomes 1.
# - Task 7 costs 7, which is more than k's remaining value, so we stop.
# We completed 4 new tasks. Total tasks = 3 already running + 4 new ones = 7.
OA Interview Question 2
Solution Ideas
We will use a first-in-first-out (FIFO) caching policy to manage the cache. When the cache is full and new instructions need to be loaded, the instructions that have been in the cache the longest (i.e., FIFO) will be removed to make room for new instructions.Procedure for solving the problem
- Initializing the cache: Use a queue (e.g., Python's collections.deque) to store cached instructions. The "first-in, first-out" nature of a queue is a perfect fit for the FIFO policy.
- Processing of each command: Iterates through each instruction in the instruction list.
- If the instruction is already in the cache (cache hit), the execution time is cache_time.
- If the instruction is not in the cache (cache miss), the execution time is memory_time.
- If the cache is full, remove the instruction at the head of the queue (i.e., the oldest instruction).
- Add the new command to the end of the queue.
- Calculate total time: Sum the execution time of each instruction to get the total elapsed time.
import collections
def simulate_fifo_cache(instructions, cache_size, cache_time, memory_time):
"""
Simulates a FIFO (First-In, First-Out) instruction cache and calculates the total execution time.
:param instructions: A list of all instructions to be processed.
:param instructions: A list of all instructions to be processed. :param cache_size: The maximum capacity of the cache.
:param cache_size: The maximum capacity of the cache. :param cache_time: The execution time for a cache hit.
:param cache_size: The maximum capacity of the cache. :param cache_time: The execution time for a cache hit. :param memory_time: The execution time for a cache miss.
:return: The total time required to execute all instructions.
"""
cache = collections.deque(maxlen=cache_size)
total_time = 0
for instruction in instructions: if instruction in cache.
if instruction in cache: # Cache Hit
# Cache Hit
total_time += cache_time
if instruction in cache: # Cache Hit total_time += cache_time
# Cache Miss
total_time += memory_time
if cache_size > 0: # Ensure cache size is greater than 0
# The deque automatically handles removing the oldest element when full.
The deque automatically handles removing the oldest element when full. cache.append(instruction)
return total_time
# Example usage
instructions_list = ['add', 'sub', 'mul', 'add', 'div', 'sub', 'add', 'mov']
cache_capacity = 3
cache_access_time = 1 # Cache access time is 1 unit
memory_access_time = 10 # Main memory access time is 10 units
total_execution_time = simulate_fifo_cache(instructions_list, cache_capacity, cache_access_time, memory_access_time)
print(f "Instruction List: {instructions_list}")
print(f "Cache Capacity: {cache_capacity}")
print(f "Total Execution Time: {total_execution_time} units")</xmp
Interview Prep
In the preparation period before the interview, you should set up a daily brush-up plan for yourself, especially on platforms such as LeetCode and HackerRank, focusing on practicing data structure and algorithmic questions with IBM tags, and secondly, you can consult the sub-topic community "LeetCode" under the reddit community, and consult some community partners who have had the corresponding interview experience. Secondly, you can consult the reddit community subreddit "LetCode", consult some community partners who have had corresponding interview experience, and strive to obtain first-hand interview information and simulated interviews, if your time schedule is too late to brush the questions by yourself, and the conditions allow you to consider using theCSOAsupport's IBM OA Interview Assistance and Proxy Interview ServiceThe result is that you get twice the effort with half the effort!
