Top 30 Most Common Computer Science Interview Questions You Should Prepare For
What are the top 30 most common computer science interview questions I should prepare for?
Short answer: Focus on a mix of behavioral prompts, core data-structures & algorithms problems, system design scenarios, and resume/fit questions — the 30 below cover the most frequently asked topics across entry to senior levels.
Behavioral & soft-skill (8)
Tell me about a time you solved a difficult problem.
Tell me about a time you failed — what did you learn?
Describe a conflict on a team and how you handled it.
Describe a project you led end-to-end.
How do you prioritize tasks under tight deadlines?
Give an example of when you improved a process.
Why do you want to work here?
Walk me through your resume / a key project.
Core coding & algorithms (12)
Two-sum / pair-sum variants.
Reverse a linked list (iterative & recursive).
Detect a cycle in a linked list / Floyd’s cycle.
Merge intervals.
Valid parentheses / stack use cases.
Binary tree traversals / level-order traversal.
Lowest common ancestor in a tree / BST variants.
Kth smallest / largest in BST/array using heap.
Graph traversal: BFS vs DFS and shortest path basics.
Topological sort and cycle detection in DAGs.
Dijkstra (single-source shortest paths) or BFS for unweighted graphs.
Dynamic programming basics: knapsack, LIS, or coin change.
System design & architecture (5)
Design a URL shortener.
Design a messaging/chat system.
Design a caching layer (LRU cache).
Design search autocomplete.
Design a rate limiter for APIs.
Databases / OS / networking fundamentals (3)
Explain ACID vs BASE and transaction isolation levels.
Explain the CAP theorem and trade-offs.
Processes vs threads — concurrency models and locking issues.
Resume, role fit & logistics (2)
Why are you the right fit for this role?
How do you present projects and measure impact?
Top 30, grouped for focused prep:
Use this list as a checklist: pick the weak areas, practice with sample problems, and prepare concise stories for behavioral prompts. Small, consistent practice on each of these 30 topics will noticeably raise interview confidence and performance.
How should I answer behavioral interview questions in a CS interview?
Direct answer: Use a consistent structure like STAR (Situation, Task, Action, Result) and quantify impact where possible.
Expand: Behavioral questions test judgment, teamwork, and cultural fit. Start with a quick context (Situation/Task), describe the concrete steps you took (Action), and finish with measurable outcomes or lessons (Result). For technical roles, include technical constraints, trade-offs, and how you collaborated with engineers or stakeholders. Resources and curated example prompts can accelerate practice — see community collections for sample answers and templates. See structured behavioral collections on GitHub and deep dive checklists on dedicated prep hubs for curated examples and frameworks. For more on STAR, MIT’s CAPD offers a helpful worksheet to build stories and refine outcomes.
Situation/Task: “On Project X we faced production memory spikes that caused downtime.”
Action: “I added profiling, isolated a hot path, refactored caching, and added metrics/alerts.”
Result: “Reduced memory use by 45% and eliminated related outages for three months.”
Example micro-answer (STAR):
Takeaway: Structure + quantifiable results = memorable behavioral answers that show impact and decision-making.
(Cited resources: see the curated behavioral collections on GitHub and structured guides on behavioral interviews for engineers.)
Which technical coding and algorithm questions appear most often and how should I practice them?
Direct answer: Arrays, strings, linked lists, trees, graphs, and dynamic programming appear most frequently — practice representative problems repeatedly with timed constraints.
Two-pointer & sliding window (subarrays, anagrams).
Hash maps & sets for frequency / lookup problems (two-sum, grouping).
Linked list manipulation (reverse, merge, cycle detection).
Tree algorithms (traversals, LCA, Kth element).
Graph basics (BFS/DFS, shortest path, topological sort).
Heaps & sorting for order-statistics.
Dynamic programming for state reuse (knapsack, LIS).
Expand: Most interview loops lean on a core set of problem patterns:
Approach each problem by clarifying constraints, outlining a brute force solution, improving to an optimal approach, and analyzing complexity. Time-box practice sessions (45–90 minutes) to build speed, and review clean implementations after each problem.
Day: warm up with two easy array/string problems.
Main block: solve one medium tree/graph problem.
Review: rewrite the solution cleanly and explain out loud.
Sample practice routine:
Takeaway: Master the patterns, not isolated problems — pattern recognition speeds up coding answers in interviews.
How should I approach system design and architecture questions?
Direct answer: Start with scoping (requirements and constraints), sketch a high-level design, then iterate on components and trade-offs (scalability, availability, consistency).
Ask clarifying questions (traffic patterns, latency, durability).
Define APIs and core data models.
Propose the high-level architecture (clients, load balancers, stateless services, databases, caches).
Drill into bottlenecks: data partitioning, replication, caching, queues, and indexing.
Discuss trade-offs (consistency vs availability), monitoring, and failure modes.
Expand: System design interviews evaluate your ability to break down vague problems and reason about scalable systems. Use a repeatable approach:
Practice common prompts like designing a URL shortener, messaging queue, or an autocomplete service. Sketch diagrams on a whiteboard (or virtual pad) and narrate trade-offs — interviewers want to hear your system-thinking process more than perfect specs.
Takeaway: Clear scoping and iterative trade-off discussion beat perfect specs—show structured thinking and ownership.
What company-specific interview differences should I expect (Google, Amazon, Facebook, etc.)?
Direct answer: Each company emphasizes different mixes of topics and evaluation rubrics — Google often probes algorithms and system design; Amazon emphasizes behavioral leadership principles; Facebook/Meta mixes product-thinking with coding intensity.
Google: strong focus on algorithms and systems thinking; expect rigorous coding rounds and potential on-site system design for senior roles.
Amazon: behavioral fit against Leadership Principles is vital; you’ll face deep behavioral questions plus data-structures coding rounds.
Meta/Facebook: emphasizes product sense and scalable system design alongside coding.
Startups & smaller companies: may prioritize full-stack tasks, deployment experience, or domain-specific skills.
Expand: Company patterns:
Use company-specific interview experiences (reevaluate via candidate forums, company prep guides) to tweak your practice: mock interviews emphasizing leadership answers for Amazon, more system design practice for senior Google/Meta loops, or full-stack take-home projects for startups.
Takeaway: Study company patterns and tailor your prep — interview success is partly technique, partly aligning to expected emphasis.
How should I walk through my resume and prove role fit in an interview?
Direct answer: Tell a concise story for each key project: problem, your role, technical choices, measurable impact, and what you learned.
Start with the context (why the project mattered).
Describe the technical challenge and your contribution.
Explain the design/implementation choices and trade-offs.
State metrics or business outcomes (reduced latency 40%, increased retention 10%, etc.).
Expand: Interviewers use the resume walkthrough to evaluate depth and authenticity. Pick 2–4 projects that best map to the job description. For each:
Practice a 2–3 minute narrative per project that’s both technical and business-aware; be ready to dive deeper into code decisions or architecture diagrams on follow-up questions.
Takeaway: A concise, metric-backed resume walkthrough proves competence and role fit better than long stories.
How can I create an effective 30-day study plan for CS interviews?
Direct answer: Combine daily problem practice, weekly mock interviews, and time-boxed system design or behavioral story refinement.
Weeks 1–2: core DS & algorithms — 1–2 easy, 1 medium daily; review patterns (sliding window, two-pointer, hash, stack).
Week 2–3: trees & graphs intensive, DP fundamentals; 3 timed mocks (45–60 min).
Week 3: system design basics, practice 2 design problems; compile behavioral STAR stories.
Week 4: mixed mock interviews with feedback, timed resume walkthroughs, and light review of weak areas.
Expand: Sample 30-day plan (scalable to your timeline):
Daily routine: 15–30 min review of notes/flashcards, 60–90 min focused coding, 15 min reflection and notes. Use simulated environments (pair programming or mock platforms) to adapt to interview pressure.
Takeaway: Structured daily practice + regular mocks builds speed and interview stamina.
How do I demonstrate leadership, teamwork, and communication during interviews?
Direct answer: Use concise examples that show ownership, collaboration, and measurable impact; explain trade-offs and how you influenced outcomes.
Focus on influence without authority, decision-making under ambiguity, and outcomes tied to data.
Use STAR but highlight cross-functional interactions and stakeholder management.
For technical explanations, tailor depth based on the interviewer: high-level for non-technical, deeper for engineers.
Show accountability: describe what you would do differently next time to demonstrate growth mindset.
Expand: For leadership/communication questions:
Practice answering with clarity and brevity — rehearsed stories should sound natural, not scripted.
Takeaway: Demonstrating leadership in interviews is about outcome-focused stories and clear communication of trade-offs.
What are the best resources and mock interview strategies to pass CS interviews?
Direct answer: Use a mix of curated question banks, structured behavioral guides, and frequent mock interviews with feedback loops.
Problem practice: curated lists and daily challenges (e.g., major online problem banks).
Behavioral guides and STAR templates (community-maintained lists are excellent for examples).
Structured prep guides with schedules and practice rubrics.
Pair or mock interviews with peers or coaches for real-time feedback.
Expand: Recommended resource types:
Community resources and handbooks consolidate company-specific experiences and suggested practice routines, while worksheets on behavioral story-building accelerate narrative polish. For behavioral prompts and STAR examples, community-maintained repositories and formal guides are especially helpful for building a bank of authentic stories.
Community behavioral collections and example templates on GitHub provide many ready-to-adapt stories and frameworks.
Structured behavioral guides and interview handbooks list common prompts and answer strategies.
MIT’s CAPD STAR worksheet helps convert experiences into concise interview-ready stories.
Cited resources:
Takeaway: Combine problem practice, story crafting, and frequent mocks to convert preparation into interview success.
How Verve AI Interview Copilot Can Help You With This
Verve AI analyzes interview context in real time and suggests structured phrasing (STAR, CAR, and technical breakdowns) to keep answers concise. It listens to question cues, proposes next-sentence starters, and offers clarifying questions so you never ramble or miss a requirement. Try Verve AI Interview Copilot for subtle prompts and phrasing boosts. Verve AI helps you stay calm, articulate trade-offs, and close answers with measurable impact during live interviews.
What Are the Most Common Questions About This Topic
Q: Can Verve AI help with behavioral interviews?
A: Yes — it prompts STAR/CAR structure and helps polish stories in real time.
Q: Which algorithms should I master first?
A: Arrays, two-pointer, hash maps, linked lists, trees, graphs, and DP basics.
Q: How long to prepare for tech interviews?
A: Typical focused prep is 4–12 weeks depending on starting level.
Q: Are mock interviews necessary?
A: Yes — they build timing, verbal clarity, and reveal weak spots to fix.
Q: Should I memorize answers?
A: No — prepare structured stories; avoid sounding scripted.
Q: How often should I code practice?
A: Daily or near-daily short sessions (45–90 minutes) are most effective.
Recommended quick checklist before interviews
1–2 behavior stories polished with metrics and lessons (STAR).
3–5 coding patterns rehearsed on whiteboard or editor.
One system-design sketch practiced with trade-offs explained.
Resume projects distilled into 2-minute narratives with impact numbers.
Mock interview scheduled within 7 days of the real one.
Quick sample answers and micro-strategies
“Tell me about a time you solved a difficult problem”: Briefly describe the context, your specific technical steps, and an outcome (e.g., reduced CPU usage by 30%). Finish with a lesson.
“Two-sum”: Clarify if unique pairs or indices required, propose O(n) hash-map solution, discuss space vs time trade-off.
“Design a URL shortener”: Clarify scale, choose database (SQL vs NoSQL), hash or base62 scheme, collision handling, caching, and monitoring plan.
Practice templates you can use immediately
Behavioral STAR template:
Situation: 1 sentence context.
Task: What needed to be done.
Action: 2–4 concrete steps you took (technical choices included).
Result: Numeric outcome + one lesson.
Technical problem template:
Clarify constraints and examples.
Outline brute force approach.
Present optimized approach with complexity.
Write code or pseudocode and walk through one example.
Discuss edge cases and possible follow-ups.
Final prep tips before the interview
Sleep and short review the night before — last-minute cramming reduces clarity.
During the interview: ask clarifying questions, verbalize trade-offs, and summarize at the end.
For take-home or paired programming: write clear tests, explain assumptions, and prioritize readability.
After the interview: capture notes on questions asked and where you struggled; this informs the next practice session.
Conclusion
These 30 questions cover the core behavioral, technical, and design topics you’ll see most often. Use a structured approach — STAR for behavioral, pattern recognition for coding, and scoping + trade-offs for design — and practice with timed mocks. Preparation plus clear structure equals confidence. Try Verve AI Interview Copilot to feel confident and prepared for every interview.
