Interview prep

Open access Question bank, mock practice (type or speak), stories & guides

55 questions · 19 behavioral · 24 technical · 9 guides

Suggested study order

1) Core section: STAR behavioral — prep 2 stories per theme 2) Your field section: practice by technical / product / design groups 3) Filter + mock practice; review history in Practice log 4) Before applying, read HK internship + OA guides below

All Technical (6)

Artificial Intelligence

6 questions

Technical · 6

Explain train / validation / test splits. Why not tune on the test set? Technical Artificial Intelligence Field-specific Easy

Reference answer
Train fits params; validation picks hyperparameters; test estimates generalization once. Tuning on test leaks information and overestimates performance.

Tips
Mention cross-validation for small data.

What is prompt engineering? Give one good and one bad prompt example. Technical Artificial Intelligence Field-specific Easy

Reference answer
Good: role, constraints, format, examples. Bad: vague “help me with homework” with no context.

Tips
Link to study-bot project.

Explain overfitting and how to reduce it. Technical Artificial Intelligence Coding basics Medium

Reference answer
Overfitting = model memorizes training noise. Reduce via regularization, more data, simpler models, cross-validation.

Compare precision and recall. When would you optimize each for a campus chatbot? Technical Artificial Intelligence Field-specific Medium

Reference answer
Precision = of predicted positives, how many correct; recall = of real positives, how many found. For unsafe answers, prioritize precision; for FAQ coverage, raise recall then filter.

Tips
Relate to false positive vs false negative costs.

How can ML models be biased? One mitigation in deployment. Technical Artificial Intelligence Field-specific Medium

Reference answer
Biased training data or labels; monitor slice metrics, human review on sensitive cases, diverse eval sets.

Tips
Ethics matter for HK finance/HR AI.

How would you detect data leakage in an internship ML task? Technical Artificial Intelligence Field-specific Hard

Reference answer
Look for future features, target-derived columns, improper joins, and suspiciously high metrics; validate with time-based splits.

Tips
Give one concrete example (e.g. using post-click labels).

Guides Open to read

Hong Kong

HK internship interview loop

Typical loop: online application → OA / take-home (optional) → HR screen → technical or case → manager chat → offer. Prepare: 90s self-intro (EN + 粵語/普通話), 3 project stories, and questions for them. Bring: portfolio link, GitHub, and one artifact you can explain deeply.

Internship prep

60-second self-intro template (internship)

1) Name + programme + year 2) One strength with proof 3) One project relevant to the role 4) Why this team Keep under 60–75 seconds; smile and pause.

Interview guides

Interview question bank roadmap

Suggested order for interns: 1) Behavioral STAR set (general section) — 2 stories per theme 2) Your field section — technical/product/design by role tabs 3) Mock practice with filters → profile practice history 4) Read HK loop + OA guides before employer deadlines

Link interviews to your CareerAck labs

Map labs to stories: H5 portfolio = shipping UI; Kanban = state/CRUD; Node API + SQL = backend; Security lab = injection/password hashing; Deploy = ownership. Practice explaining trade-offs aloud.

Behavioral STAR cheatsheet for HK fresh grads

S/T: 1–2 sentences context. A: your actions (I, not we). R: metric or learning. Prep stories: teamwork conflict, failure, leadership without title, deadline, ethics.

OA & take-home survival tips

Read constraints twice. Start with a correct brute force if needed, then optimize. Comment assumptions. For take-homes: README, sample input, and time spent note. Never plagiarize.

STAR Method for Behavioral Interviews

Structure answers as Situation, Task, Action, Result. Keep results measurable when possible.

Technical Interview Checklist

Review DSA basics, prepare 2–3 project stories, practice aloud, and ask clarifying questions.

Technical screens

Technical phone screen — what to expect

Often 30–45 min: intro, 1–2 coding or conceptual questions, your project deep-dive, Q&A. Say your thinking aloud; ask constraints; start simple then optimize.

Experience shares

Consumer Internet — Product Design Intern

Design & UX

Portfolio walkthrough first. Then redesign critique on a messy settings page. They cared about trade-offs and accessibility, not fancy visuals only.

Local Bank Tech — Technology Summer Intern

Computer Science

HR asked STAR + why banking tech. Technical round: REST basics, one SQL group-by, and explain a project README. Tip: prepare a 60s Cantonese self-intro.

Harbor Tech — AI Intern

Artificial Intelligence

OA focused on arrays + one ML conceptual question. Onsite was system-design lite for a recommender.