README.md

metadata

license: cc-by-4.0
language:
  - en
task_categories:
  - text-classification
task_ids:
  - multi-class-classification
tags:
  - interview-questions
  - placements
  - campus-recruitment
  - computer-science
  - aptitude
  - technical-interviews
pretty_name: AskedBefore CS Interview Questions
size_categories:
  - 10K<n<100K
configs:
  - config_name: questions
    default: true
    data_files:
      - split: train
        path: askedbefore_cs_interview_questions.csv
  - config_name: company_profiles
    data_files:
      - split: train
        path: company_profiles.csv
  - config_name: company_question_map
    data_files:
      - split: train
        path: company_question_map.csv

AskedBefore CS Interview Questions

1,770 placement and interview questions across 15 subjects, mapped to the hiring processes of 19 companies, from mass recruiters like TCS and Infosys to Google, Amazon and other Big Tech firms.

Pick a company and you get the questions that match what it tests: aptitude and pseudocode for TCS, Wipro and Accenture; DSA, OOP and SQL for Infosys SP and Oracle; guesstimates and cases for ZS; DSA plus behavioural questions for Amazon and Meta.

Every question is written in original wording. None are copied from interview-prep websites, coding platforms or company question lists.

Important: the company mapping says a question is recommended for a company, because its subject and difficulty match that company's publicly documented hiring process. It does not claim the company asked that exact question.

At a glance

Questions	1,770
Subjects	15 (9 core CS and interview subjects + 6 placement-test subjects)
Companies profiled	19
Company-question links	14,780
Difficulty	easy 469, medium 1,007, hard 294
Average length	9.2 words / 50.3 characters
Unique words	2,912
Duplicates	0 exact, 0 near-duplicates
License	CC BY 4.0
Best subject classifier	Logistic Regression on TF-IDF, 0.762 macro F1 over 15 classes

Files

File	What it contains	Rows
askedbefore_cs_interview_questions.csv	The questions, with subject, difficulty and one column per company labelled core, frequent, occasional or blank	1,770
company_profiles.csv	One row per company: category, fresher roles, hiring rounds, core/frequent/occasional subjects, difficulty focus, data confidence, notes, source link	19
company_question_map.csv	Which questions are recommended for which company, with relevance (core, frequent, occasional)	14,780

Companies covered

Company	Category	Core subjects	Difficulty	Questions	Confidence
TCS	IT services (mass recruiter)	Quant, Reasoning, Verbal, Programming Fundamentals	easy, medium	1,002	medium
Infosys	IT services (mass recruiter)	DSA, OOP, DBMS & SQL	easy to hard	1,140	medium
Wipro	IT services (mass recruiter)	Quant, Reasoning, Verbal, Programming Fundamentals	easy, medium	752	medium
Accenture	IT services (mass recruiter)	Verbal, Reasoning, Programming Fundamentals, Cloud & Security	easy, medium	680	medium
Cognizant	IT services (mass recruiter)	Quant, Reasoning, Verbal, Programming Fundamentals, DBMS & SQL	easy, medium	804	medium
Capgemini	IT services (mass recruiter)	Reasoning, Programming Fundamentals, Behavioral	easy, medium	1,002	medium
HCLTech	IT services (mass recruiter)	Quant, Reasoning	easy, medium	1,002	medium
Deloitte	Consulting and technology services	Cloud & Security	easy, medium	910	medium
ZS Associates	Consulting and analytics	Guesstimates & Cases, Statistics, DBMS & SQL, Behavioral	easy to hard	900	medium
ION Group	Fintech and trading software	DSA, OOP, Operating Systems, Programming Fundamentals	easy to hard	1,200	medium
Oracle	Enterprise software	DSA, DBMS & SQL	easy to hard	910	medium
Adobe	Product company	DSA	medium, hard	726	medium
Blinkit	Product company (Indian startup)	DSA	medium, hard	560	low
Google	Big Tech (FAANG/MAANG)	DSA	medium, hard	336	medium
Microsoft	Big Tech (FAANG/MAANG)	DSA	medium, hard	784	medium
Amazon	Big Tech (FAANG/MAANG)	DSA, Behavioral (Leadership Principles)	medium, hard	896	high
Meta	Big Tech (FAANG/MAANG)	DSA, Behavioral	medium, hard	336	medium
Apple	Big Tech (FAANG/MAANG)	Behavioral	medium, hard	504	medium
Netflix	Big Tech (FAANG/MAANG)	System Design, Behavioral	medium, hard	336	medium

Each company name links to the public hiring-process page its profile is based on. company_profiles.csv also lists each company's frequent and occasional subjects, typical fresher roles, round-by-round process and notes.

Confidence levels:

• High: based on the company's own official prep page.
• Medium: based on detailed public hiring-process guides.
• Low: very little public data. Blinkit's profile is a starting point to update with real reports.

How the mapping works

1. Each company profile gives every subject a weight: 3 = core (tested in almost every round), 2 = frequent, 1 = occasional.
2. Each profile also has a difficulty focus. Mass recruiters use easy and medium; product companies use medium and hard.
3. A question is linked to a company when its subject has a weight and its difficulty is in the company's focus. The link's relevance column is core, frequent or occasional.

Example: TCS gets all easy and medium Quant, Reasoning, Verbal and Programming Fundamentals questions as core; DSA, OOP and DBMS questions as frequent; and OS and Networks questions as occasional.

Subjects and topics covered

Subject	Questions	Topics covered
DSA	150	Arrays, strings, hashing, two pointers, sliding window, linked lists, stacks, queues, trees, BSTs, heaps, tries, graphs (BFS, DFS, shortest paths, topological sort, MST, SCCs), recursion, backtracking, dynamic programming, binary search, sorting, bit manipulation, segment trees, union find
Operating Systems	150	Processes and threads, scheduling, synchronization, deadlocks, classic problems, memory management, paging, virtual memory, page replacement, file systems, I/O, system calls, Linux internals, containers and VMs
DBMS & SQL	150	Keys, normalization, ER modelling, SQL (joins, aggregation, subqueries, CTEs, window functions), indexing, transactions, ACID, isolation, locking, recovery, replication, sharding, NoSQL, data warehousing
Computer Networks	150	OSI and TCP/IP, TCP vs UDP, flow and congestion control, IP addressing, subnetting, routing, DNS, DHCP, HTTP, TLS, switching, VLANs, proxies, CDNs, WebSockets, network attacks
OOP	150	Four pillars, constructors, inheritance, polymorphism, abstract classes vs interfaces, SOLID, design patterns, C++/Java/Python specifics, low-level design problems
System Design	150	Scaling, load balancing, caching, queues, microservices, consistency, CAP, consistent hashing, sharding, distributed patterns, reliability, deployments, classic high-level designs
Machine Learning	150	Supervised and unsupervised learning, trees and ensembles, metrics, bias-variance, regularization, optimization, neural networks, CNNs, RNNs, transformers, NLP, feature engineering, MLOps, LLMs
Statistics & Analytics	150	Probability, distributions, hypothesis testing, confidence intervals, A/B testing, product metrics, funnels, cohorts, regression, time series, causal inference
Behavioral / HR	150	Introductions, motivation, strengths and weaknesses, STAR situations (conflict, failure, leadership, deadlines, feedback, ownership), teamwork, ethics
Programming Fundamentals	80	C, Java and Python basics, pointers and memory, pseudocode tracing, operators and bitwise logic, exceptions, strings and collections, compile vs runtime errors
Quantitative Aptitude	80	Percentages, profit and loss, interest, ratios, averages, mixtures, time and work, pipes, speed and distance, trains and boats, permutations, probability, number system, clocks and calendars, mensuration
Logical Reasoning	80	Series, coding-decoding, blood relations, directions, seating, syllogisms, statements and assumptions, data sufficiency, cubes, Venn diagrams, clocks and calendars, classic puzzles
Verbal Ability	60	Synonyms, antonyms, idioms, error spotting, articles and prepositions, voice and speech, para jumbles, reading comprehension strategy, critical reasoning, essay writing
Guesstimates & Case Studies	60	Market sizing, guesstimates, profitability, market entry, pricing, operations, pharma and healthcare cases, fintech and trading cases, structuring and presenting
Cloud & Security	60	Cloud models (IaaS, PaaS, SaaS), virtualization, IAM, Docker, Kubernetes, DevOps, CI/CD, common attacks (SQL injection, XSS, CSRF, phishing, ransomware), encryption, zero trust, incident response

Statistics

Subject × difficulty

Subject	Easy	Medium	Hard	Total
DSA	38	86	26	150
Operating Systems	38	88	24	150
DBMS & SQL	38	86	26	150
Computer Networks	38	86	26	150
OOP	38	86	26	150
System Design	38	86	26	150
Machine Learning	38	86	26	150
Statistics & Analytics	38	86	26	150
Behavioral / HR	38	86	26	150
Programming Fundamentals	24	44	12	80
Quantitative Aptitude	24	44	12	80
Logical Reasoning	26	42	12	80
Verbal Ability	18	34	8	60
Guesstimates & Case Studies	17	33	10	60
Cloud & Security	18	34	8	60
Total	469	1,007	294	1,770

Question length

	Words	Characters
Mean	9.2	50.3
Median	8	48
Shortest	3	12
Longest	43	177

Aptitude and reasoning questions are the longest (about 17 to 18 words on average), because they include numbers and conditions. Concept questions in ML, Cloud & Security, OOP and Networks are the shortest (about 7 words).

Question style

Starts with	Count	Share
What ...	944	53.3%
Other (coding tasks, puzzles, fill-in-the-blanks, yes/no)	373	21.1%
How ...	208	11.8%
Tell me / Describe ...	87	4.9%
Design ...	71	4.0%
Explain ...	30	1.7%
Write ...	24	1.4%
Estimate ...	13	0.7%
Why ...	12	0.7%
Compare ...	8	0.5%

1,288 questions (72.8%) end with a question mark.

Company-question links by relevance

Company	Core	Frequent	Occasional	Total
Accenture	240	316	124	680
Adobe	112	504	110	726
Amazon	224	224	448	896
Apple	112	280	112	504
Blinkit	112	0	448	560
Capgemini	260	368	374	1,002
Cognizant	380	176	248	804
Deloitte	52	808	50	910
Google	112	112	112	336
HCLTech	136	866	0	1,002
Infosys	450	380	310	1,140
ION Group	530	670	0	1,200
Meta	224	0	112	336
Microsoft	112	560	112	784
Netflix	224	112	0	336
Oracle	300	530	80	910
TCS	256	496	250	1,002
Wipro	256	248	248	752
ZS Associates	510	160	230	900

Example questions

Subject	Difficulty	Question
DSA	medium	Implement an LRU cache with O(1) get and put.
DSA	hard	Find the median of a data stream.
Operating Systems	medium	Explain the four necessary conditions for a deadlock.
DBMS & SQL	medium	Write a SQL query to find the nth highest salary.
Computer Networks	medium	What happens when you type a website address into a browser and press enter?
OOP	hard	Design the classes for a chess game.
System Design	hard	Design a distributed message queue like Kafka.
Machine Learning	hard	What is LoRA and why is it used to fine tune large models?
Statistics & Analytics	hard	What is CUPED and how does it reduce variance in experiments?
Behavioral / HR	medium	Describe a conflict with a teammate and how you resolved it.
Programming Fundamentals	medium	What is the difference between call by value and call by reference?
Quantitative Aptitude	medium	A can finish a work in 12 days and B in 18 days. In how many days can they finish it together?
Logical Reasoning	hard	Four people must cross a bridge at night with one torch, taking 1, 2, 5 and 10 minutes. What is the minimum time for all to cross?
Verbal Ability	easy	What does the idiom break the ice mean?
Guesstimates & Case Studies	medium	A quick commerce company wants to open dark stores in a new city. How would you decide where to open them?
Cloud & Security	medium	What is SQL injection and how do you prevent it?

Dataset structure

askedbefore_cs_interview_questions.csv

Column	Description
id	Unique id, ab-0000 to ab-1769
question	The question, usually one sentence
subject	One of the 15 subjects
difficulty	easy, medium or hard
source_type	Always original: written for this dataset
version	v1, v2 or v3 (see Versions)
TCS, Infosys, Wipro, Accenture, Cognizant, Capgemini, HCLTech, Deloitte, ZS Associates, ION Group, Oracle, Adobe, Blinkit, Google, Microsoft, Amazon, Meta, Apple, Netflix	One column per company: core, frequent or occasional if the question is recommended for that company, blank if not
recommended_for	All companies the question is recommended for, separated by ;

Example rows:

question	Google	Microsoft	TCS	ZS Associates
Perform a topological sort of a directed acyclic graph.	core	core	frequent
Find the LCM of 12, 18 and 30.			core	frequent
A pharma client's new drug had good trial results but weak sales after launch. Diagnose the problem and recommend next steps.				core

The company columns contain the same information as company_question_map.csv, in a wider format that is easier to filter. 16 hard questions (in subjects only easy-and-medium companies test) are not recommended for any company.

company_profiles.csv: company, category, fresher_roles, hiring_rounds, core_subjects, frequent_subjects, occasional_subjects, difficulty_focus, data_confidence, notes, sources.

company_question_map.csv: company, question_id (matches id), subject, difficulty, relevance (core, frequent, occasional), weight (3, 2, 1).

How to use

from datasets import load_dataset

repo = "YOUR_USERNAME/askedbefore-cs-interview-questions"
questions = load_dataset(repo, "questions", split="train").to_pandas().fillna("")   # blank company cells -> ""
profiles  = load_dataset(repo, "company_profiles", split="train").to_pandas()
links     = load_dataset(repo, "company_question_map", split="train").to_pandas()

# Core practice set for TCS, straight from the company column
tcs_core = questions[questions["TCS"] == "core"]
print(tcs_core.subject.value_counts())

# Everything recommended for Google, most important first
google = questions[questions["Google"] != ""].sort_values("Google")

# Same thing using the long-format map
tcs = links[(links.company == "TCS") & (links.relevance == "core")]

Baseline results

Subject classification (15 classes) from the question text. Features: TF-IDF word 1-2 grams plus character 3-5 grams. Evaluation: stratified 5-fold cross-validation over all 1,770 questions.

Model	Macro F1	Accuracy
Logistic Regression	0.762 ± 0.031	0.764
Random Forest (baseline)	0.631 ± 0.026	0.634
XGBoost	0.603 ± 0.025	0.627

Per-subject F1 (Logistic Regression, cross-validated):

Subject	Precision	Recall	F1
Behavioral / HR	0.935	0.953	0.944
DSA	0.866	0.907	0.886
Guesstimates & Case Studies	0.844	0.900	0.871
Verbal Ability	0.870	0.783	0.825
OOP	0.833	0.767	0.799
Quantitative Aptitude	0.808	0.787	0.797
DBMS & SQL	0.810	0.740	0.774
Operating Systems	0.745	0.720	0.732
Machine Learning	0.732	0.727	0.729
Computer Networks	0.654	0.807	0.722
System Design	0.771	0.673	0.719
Logical Reasoning	0.761	0.675	0.715
Cloud & Security	0.795	0.583	0.673
Statistics & Analytics	0.625	0.700	0.660
Programming Fundamentals	0.531	0.637	0.580

Programming Fundamentals is the hardest subject to classify because its questions share vocabulary with DSA, OOP and Operating Systems (pointers, memory, recursion). Cloud & Security overlaps with Networks and System Design. The score is lower than the 0.802 of the 9-subject version because there are more classes and more overlap between them.

How it was made

1. Questions: for each subject, listed the concepts commonly tested in placement tests and technical interviews, and wrote one original question per concept, varying the phrasing. Questions were drafted with AI assistance and reviewed and labelled by the maintainer.
2. Deduplication: removed exact and near-duplicate questions automatically after normalising case, punctuation and filler words.
3. Company profiles: for each company, read publicly available descriptions of its fresher hiring process (test sections, interview rounds, topics, difficulty) and turned them into subject weights and a difficulty focus. The source page for each company is listed in company_profiles.csv.
4. Mapping: linked questions to companies automatically from the profiles (see How the mapping works).

Versions

Version	Change	Total questions
v1	9 subjects, 80 questions each	720
v2	70 more questions per subject	1,350
v3	6 new subjects (Programming Fundamentals, Quantitative Aptitude, Logical Reasoning, Verbal Ability, Guesstimates & Case Studies, Cloud & Security), plus company profiles and company-question mapping	1,770

Ids from earlier versions never change.

Limitations

• Recommended, not reported. Company links come from public descriptions of hiring processes, not from records of questions a company actually asked. Hiring processes also change from year to year and between roles.
• Uneven company data. Some profiles rest on a single public source, and Blinkit's is marked low confidence.
• No answers yet. Aptitude and reasoning questions are solvable, but the dataset does not include answers or solutions.
• Short, clean text. Real interview conversations are messier, so classifiers trained here may score lower on them.
• One label per question. Some questions sit between subjects but have exactly one label.
• Difficulty is subjective. It reflects a typical Indian campus-placement and new-graduate interview.
• English only.

License

CC BY 4.0: free to share and adapt, including commercially, with credit. Company names are used only to describe their public hiring processes; this dataset is not affiliated with or endorsed by any of these companies.

Citation

@misc{askedbefore_cs_interview_questions,
  title  = {AskedBefore CS Interview Questions},
  author = {Aditya},
  year   = {2026},
  url    = {https://huggingface.co/datasets/YOUR_USERNAME/askedbefore-cs-interview-questions}
}