Leland+ Content Library

Investment Banking Technical Interview: 75 Essential Questions

Prepare for investment banking technical interviews with 75 essential questions covering accounting, enterprise and equity value, valuation, DCF and WACC, accretion/dilution, and M&A. Designed for Analyst and Associate recruiting, with questions progressing from core fundamentals to advanced transaction mechanics and M&A judgment.

Wharton Short Answer Examples

Example successful Short Answer questions for Wharton - admitted to the class of 2028.

Volatility Surface Calibration and Arbitrage Lab

Clean synthetic option quotes, fit transparent smiles, interpolate an implied-volatility surface, inspect calibration error, and test calendar and butterfly conditions. Includes learner and worked notebooks, tests, and a model-risk memo.

Market Data Pipeline Repair Lab

Repair a broken market-data pipeline using Python, SQL, and synthetic files. Work through schema drift, timestamps, duplicates, symbol mapping, late events, stale runs, and split adjustments, then verify the result with twelve tests.

Fixed-Income Curve, Carry, and Hedging Lab

Build a synthetic zero curve, price a bond, estimate duration, convexity, DV01 and key-rate risk, examine carry and roll, size a three-node hedge, and compare first-order curve scenarios in one transparent Excel model.

From Analysis to Answer: How to Build a Confident Case Recommendation

Case frameworks and exhibit reads get most of the attention in consulting prep. The synthesis almost never does. Knowing the answer isn't enough. Being able to drive toward a clear, confident recommendation under time pressure, with ambiguous data, is a skill, and most candidates haven't practiced it deliberately. As a former BCG interviewer, I've watched strong cases fall apart at the end and mediocre ones recover because of a sharp synthesis. This session is focused on one thing: how to close. What Interviewers Are Evaluating at the End of a Case: The synthesis is the moment where everything you've done comes together — or doesn't. -Why synthesis is harder than it looks and why interviewers weight it as heavily as they do -The difference between summarizing what you found and actually making a recommendation -What "structured" means in the context of a synthesis A Framework for Building Your Recommendation: How to land on a clear answer even when the data isn't clean. -What to lead with and what to leave out when you're synthesizing under time pressure -How to structure a recommendation so it sounds decisive without overstating certainty -How to handle it when you're genuinely unsure what the answer is, and what interviewers are looking for in that moment Live Walkthrough: A full synthesis applied to a real case, from the data on the table to the final recommendation, including a side-by-side of what a strong synthesis sounds like vs. a weak one.

Data Science Model Failure Clinic

Diagnose six common model failures through focused notebook exercises on leakage, drift, calibration, class imbalance, unstable features, and weak validation. Includes learner labs, worked solutions, tests, and a review template.

Software Engineering Pull Request and Incident Lab

Review a realistic pull request, interpret production logs, repair failing tests, resolve an incident, and write a concise postmortem. The package includes starter code, a worked solution, ten tests, and a scoring rubric.

Time-Series Forecasting and Walk-Forward Validation Lab

Compare blocked, expanding, rolling, and periodic forecast evaluation on a synthetic series with a structural change. Learners audit chronology, tune refit choices, inspect regime sensitivity, and write a limitations memo.

Part 4: Maintain Your Network

Are you tired of applying to 100 jobs and not hearing back? Maybe you’re nervous about reaching out to other people and talking about yourself. Perhaps you’ve heard about networking from a lot of people, but you’re skeptical about the way it can actually work for you. As a career coach, I have distilled the targeted networking approach of finding jobs into a five-step process, each of which I will guide you through. Every three weeks, we will address another piece of the puzzle through an interactive workshop. In Part 1, we reflected on what exactly you want out of your next job and why, and learned how to tell your story in an elevator pitch and rehearse it so you are comfortable sharing your story with others in as little as one minute. In Part 2, we discussed how to start by finding 5-10 organizations that you want to work at, now that you know what you want to do next. We talked about how to figure out what opportunities actually exist for you in the market, using both online (LinkedIn, virtual communities) and in-person (networking and industry events) methods. In Part 3, we used your existing networks to find people who do the work you want to be doing and ask them for coffee chats so you can learn more about them, harnessing your curiosity and concerns to learn about what they’re doing, their workplaces, and how you can break into this organization or field. Our focus was on figuring out if a good fit exists and also creating a good connection and relationship with that person, not asking for a referral. In Part 4: Maintain Your Network, we will: - Review the results of creating new connections, and revisit your target organizations and network building process. - Learn to stay active on LinkedIn, through email, and industry events, and be patient with yourself and trust the process. Part 5: Use Your Network to Apply (July 31) By the end of the process, you will be fully prepared to use the targeted networking approach to find a job, and more importantly, you will be closer to a chosen job and career that helps you live a purposeful, fulfilling life. Each workshop will include exercises that we will work through together, and I will answer questions. While you can attend any workshop as a one-off, it can be very powerful to attend the entire five-workshop sequence. If you want more personalized support as you go through the job search process, sign up for 1-on-1 coaching sessions with me! This workshop is appropriate for: - Students and early-career professionals who are not sure how to get started with the internship or full-time job search - Mid-career professionals who are looking to pivot into another industry or find a job after a career gap - Anyone who wants to become more adept at networking and use it strategically

Quant Recruiting, Readiness, and Offer Dashboard

Manage a quant job search in one workbook. Track target roles, applications, contacts, interview rounds, preparation, weekly actions, and offer facts, then use the dashboard to spot gaps without turning estimates into claims.

Slides for Fit Is Not a Freebie: How Firms Actually Evaluate Your Behavioral Interviews

Most candidates spend 90% of their prep time on cases and assume fit will take care of itself. It doesn't. Fit is evaluated with the same rigor as the case at every MBB firm, and the interviewers asking behavioral questions are trained to score them. As a former BCG interviewer, I've sat on both sides of that table, and the gap between what candidates think they're being evaluated on and what's actually happening is significant. This session covers what firms are actually looking for and how to build a fit preparation strategy that works. How Fit Is Actually Scored at MBB: This isn't a casual conversation, it's a structured evaluation with specific criteria. -Why fit isn't just about whether your interviewer likes you, but it's a scored component with defined qualities -The qualities that interviewers are looking for in every fit story, regardless of firm Telling Fit Stories That Actually Work: The format most candidates use produces weak answers. -Why STAR produces flat, unmemorable answers and what to do instead -How to structure a leadership, impact, or conflict story so it actually lands -What makes a fit story memorable vs. one that interviewers forget before the next candidate walks in Building Your Story Bank Before Recruiting Season: Having one good story is not enough. -How many stories you actually need and how to choose which experiences to use -Why most candidates recycle the same stories across question types and how to avoid it -How to adapt a single experience to multiple fit questions without it sounding rehearsed Live Example: A strong fit answer, broken down for what makes it work, and the common version of the same story that doesn't.

Execution and Order-Slicing Simulator

Compare TWAP, volume-weighted, participation-based, and custom order schedules across four synthetic market replays. Inspect fills, timing, spread, impact, and opportunity cost before defending an execution choice.

Options Greeks and Delta-Hedging Sandbox

Explore Black-Scholes prices and Greeks on a fixed synthetic path, then compare static, periodic, and unhedged positions. Adjust volatility, gap size, hedge timing, and costs to study hedge error and model limits.

Limit Order Book Replay Lab

Work through four synthetic limit order book replays, track queue position and partial fills, calculate markouts, and explain execution outcomes. Includes starter code, tests, a worked solution, and a concise scoring rubric.

Portfolio Construction and Risk Sandbox

Compare equal weight, inverse volatility, score-based, and manual portfolios using synthetic assumptions. Edit mirrored correlations, trace risk contributions, test turnover and costs, and evaluate fixed stress scenarios.

Backtesting Red-Team Lab

Audit a deliberately flawed backtest and identify look-ahead bias, survivorship bias, cost errors, weak validation, and unsupported conclusions. Includes a learner notebook, synthetic data, verifier, and worked answer key.

Software Engineering System Design Walkthrough Pack

A visual interview pack covering URL shortening, notification delivery, and event ingestion. Candidates practice requirements, capacity estimates, architecture choices, failure modes, tradeoffs, and clear spoken recommendations.

[Recording] How I Built an AI-Powered Grocery Expense-Splitting App [8/11/2026]

Splitting grocery bills fairly, without the back-and-forth of screenshots and mental math, is exactly the kind of everyday problem AI is now good enough to solve. In this session, you'll see how one Leland customer built an app that does just that: tracking shared purchases and automatically figuring out who owes what. No coding background required, and that's the point. The speaker will demo the app live, then break down how she actually built it: the tools she used, the choices she made along the way, and where she got stuck. If you've got a small, nagging problem in your own life, this session should leave you with a real sense of how to go build the fix yourself.

Consulting Written Case Practice Pack

Two original written consulting cases with candidate prompts, exhibits, calculations, worked solutions, interviewer guidance, and scoring rubrics. The cases are fictional and designed for realistic, self-directed practice.