How IBM Interviews in 2026: Process, Questions & What They Score

1. 01

   Reverse a linked list / delete a given node from a linked list in constant time

   Linked-list fundamentals: pointer reversal and the copy-next-value trick for constant-time deletion.

2. 02

   Find the second-highest salary from a table using SQL (classic SQL round question, confirmed in candidate write-ups)

   Classic SQL ranking problem: subquery with MAX exclusion or a window function.

3. 03

   Remove duplicates from an array

   Array warm-up: in-place two-pointer dedupe or a hash set, with complexity trade-offs.

4. 04

   Flatten a nested array

   Tests recursion or an explicit stack to flatten arbitrary nesting depth.

5. 05

   Implement a debounce function

   Tests closures and timer control: canceling and rescheduling a delayed call.

6. 06

   Find the maximum subarray sum / subarrays with a sum exactly equal to k

   Kadane's algorithm for max subarray; prefix-sum hash map for subarrays summing exactly to k.

7. 07

   Sort an array containing only three distinct values, e.g. 0s/1s/2s (Dutch national flag style, confirmed in candidate write-ups)

   Dutch national flag problem: single-pass three-way partition with pointers.

8. 08

   Find the missing/single non-duplicate element in a sorted array (similar to LeetCode 'Single Element in a Sorted Array')

   Binary search on index parity to find the single element in O(log n).

Reported problems, listed so you know what to expect. Practice them in your own editor or on the platform you prefer; SupaCV's practice mode coaches how you talk through them.

1. 01

   Explain OOP concepts (inheritance, polymorphism, encapsulation) with examples from your own code

   Tests OOP vocabulary grounded in your own code: inheritance, polymorphism, encapsulation.

2. 02

   Walk through basic Linux commands and what they do

   Command-line fluency check: everyday Linux commands and what each does.

3. 03

   Design a URL shortener

   System design staple: key generation and encoding, redirects, and caching a read-heavy workload.

4. 04

   Design a scalable messaging app (e.g., WhatsApp/Messenger-style system)

   System design: message delivery, fan-out, presence, and storage for chat at scale.

5. 05

   How would you design a system considering load balancing, caching, and database sharding for an enterprise client?

   Scalability toolkit: load balancing, cache placement and invalidation, and database sharding trade-offs.

6. 06

   Explain a project you built end-to-end and the architectural tradeoffs you made

   Project deep dive; expects concrete architectural decisions, alternatives considered, and why trade-offs were made.

7. 07

   What is the difference between AI and generative AI? (confirmed as an actual asked question; increasingly common given IBM's watsonx/AI push)

   Conceptual AI knowledge check: discriminative versus generative models and what makes generative AI distinct.

Reported problems, listed so you know what to expect. Practice them in your own editor or on the platform you prefer; SupaCV's practice mode coaches how you talk through them.

1. 01

   Explain the bias-variance tradeoff

   Core ML theory: underfitting versus overfitting and how model complexity moves the trade-off.

2. 02

   How do you handle an imbalanced dataset?

   Practical ML: resampling, class weights, threshold tuning, and metric choice under class imbalance.

3. 03

   Compare precision, recall, and AUC-ROC as evaluation metrics: when would you prioritize one over another?

   Evaluation-metric judgment: matching precision, recall, or AUC-ROC to error costs and class balance.

4. 04

   Write a SQL query involving multi-table joins and window functions

   SQL depth check: multi-table joins plus window functions such as ROW_NUMBER and running aggregates.

5. 05

   Solve a data manipulation task in Python/pandas (cleaning, grouping, aggregating)

   Hands-on pandas: cleaning, grouping, and aggregating real data.

6. 06

   How would you select features for a model, and how do you decide between algorithms (e.g., regression vs. tree-based)?

   Modeling judgment: feature-selection methods and choosing between regression and tree-based families.

7. 07

   Walk through a past ML project and how you validated the model's business impact

   ML project deep dive; expects validation methodology and evidence the model moved a business metric.

Reported problems, listed so you know what to expect. Practice them in your own editor or on the platform you prefer; SupaCV's practice mode coaches how you talk through them.

1. 01

   Candidate-led business case involving a technology layer, e.g., estimating ROI of a cloud migration for a mid-size enterprise client

2. 02

   Case involving payback/business case for an AI automation initiative

3. 03

   Case involving tradeoffs in an ERP modernization decision

4. 04

   Market-sizing or profitability-style case framed around a named or disguised client industry

1. 01

   Tell me about a time you failed and what you learned from it

2. 02

   Tell me about a time you had to learn a new technology or skill quickly

3. 03

   Give an example that shows your leadership and initiative

4. 04

   Tell me about a time you solved a problem creatively

5. 05

   Give an example of a time you went out of your way for a customer or client

6. 06

   What was the most challenging part of your previous role, and how did you handle it?

7. 07

   Why IBM, and why this role?

8. 08

   Tell me about a time you disagreed with a teammate or manager: how did you resolve it?

1. 01

   Tell me about a time you had to influence a client or stakeholder without direct authority

2. 02

   Describe a project where you had to manage ambiguity with limited data

3. 03

   Why consulting, and why IBM Consulting specifically (vs. MBB or another Big Four/tech consultancy)?