Risk Management interview preparation

Questions

100

Traced to a firm

37

Firms

12

Updated

September 2026

Asked at

All firmsUBS14MSCI7BLBlackRock5FTFranklin Templeton3Oaktree Capital Management2Scotiabank2Jane Street1Moody's1Neuberger Berman1PIMCO1SSState Street1TSTruist Securities1

Topic

All topicsMarket risk and VaR14Tail risk and stress testing5Greeks and sensitivities5Credit risk11Counterparty risk and CVA6Operational risk5Model risk and validation6Regulatory capital7Liquidity risk and ALM6Statistics and quant foundations7Indian regulation7Risk governance and appetite4Markets and macro9Fit and career8

Level

AnyCoreIntermediateHard

Type

AnyTechnicalCaseBrainteaserMarket viewFit

1. 058Why does a leverage ratio exist alongside risk-weighted capital?Regulatory capitalIntermediatetechnicalRegulatory reportingBank market risk

   Say this

   Because risk weights are model outputs and models can be wrong or gamed. The leverage ratio is a non-risk-based backstop: Tier 1 over total exposure, minimum 3 percent, and it doesn't care what you think the risk is.

   Then walk it

   1. The pre-crisis evidence is the whole argument. Banks entered 2008 with comfortable risk-based ratios and leverage of 30 or 50 to one, because sovereign debt, AAA tranches and repo books all carried tiny weights and turned out not to be riskless.
   2. So the design intent is a floor that survives being wrong about risk. It binds when a bank holds a lot of assets it has judged safe, which is exactly the situation that has historically preceded trouble.
   3. The exposure measure is deliberately broad: on-balance-sheet assets, derivative exposures including a potential future exposure add-on, securities financing transactions, and off-balance-sheet commitments converted at credit conversion factors. You can't shrink it by netting the way you can for RWA.
   4. Who it binds: banks with large low-risk-weight books. Custodians, repo intermediaries, and banks holding large government bond portfolios. For those, leverage rather than RWA is the constraint that drives the business decision.
   5. Its own weakness, and you should say it: it's risk-insensitive by construction, so it treats a treasury bill and an unsecured emerging market loan identically. That creates an incentive to shift toward higher-yielding, higher-risk assets once the ratio binds, which is the opposite of what you want.
   6. So the two measures are deliberate complements. Risk weights give you sensitivity and can be gamed; leverage gives you robustness and rewards risk-taking at the margin. Neither alone is adequate, which is the point of having both.
   7. Live example: in 2020 several jurisdictions temporarily excluded central bank reserves from the exposure measure, because deposit inflows and QE were inflating the denominator and constraining lending. That's a good illustration of the ratio binding for reasons unrelated to risk.

   Where candidates lose it

   Stating the definition without the pre-crisis motivation, and without the downside. A complete answer says the leverage ratio pushes banks toward riskier assets at the margin, because the interviewer wants to see you can criticise a rule you also support.

   Expect next

   • Which kinds of bank does the leverage ratio bind?
   • What perverse incentive does it create?
   • Why did supervisors exclude central bank reserves in 2020?
2. 060Distinguish funding liquidity risk from market liquidity risk.Liquidity risk and ALMCoretechnicalTreasury and ALMBank market risk

   Say this

   Funding liquidity risk is not being able to meet your obligations as they fall due. Market liquidity risk is not being able to sell an asset at anything near its marked price. They're different risks, and the danger is that each one triggers the other.

   Then walk it

   1. Funding liquidity is a balance sheet and cash flow problem: deposits leave, a wholesale line isn't rolled, a margin call lands, and you need cash today. It's binary and it's fatal. You are either able to pay or you are not.
   2. Market liquidity is a price problem: bid-offer, depth, and how far the price moves against you when you try to sell size. It's continuous, and it shows up as a haircut on what your book is really worth.
   3. The spiral is the real answer. You need funding, so you sell assets. Selling into a thin market depresses the price. The lower mark reduces your collateral value and your capital, which makes funding harder, so you sell more. That's the liquidity spiral, and it's what turned 2008 from a credit event into a systemic one.
   4. Measurement differs completely. Funding liquidity: contractual and behavioural cash flow ladders, survival horizon, LCR and NSFR, and a stress test on deposit outflow. Market liquidity: bid-offer spreads, days of average daily volume to exit, and a liquidity-adjusted VaR or an exit-cost haircut.
   5. Worked example: a bond book marked at 100 crore, where the position is ten days of average volume. In a stress you might realise 92, so the honest liquidity-adjusted value is 92, not 100. The mark is not the exit price, and that 8 crore is the market liquidity risk in money.
   6. FRTB codified this by making liquidity horizons vary from 10 to 120 days by risk factor, so illiquid risk now costs more capital. That's the regulatory acknowledgement that a mark is not a price you can get.
   7. The thing to say without prompting: almost every bank failure is ultimately a funding liquidity failure. Solvency problems kill banks slowly and liquidity kills them in a week.

   Where candidates lose it

   Conflating the two, or giving definitions without the interaction. The answer that earns respect explains the spiral in both directions and says that a marked price is not an exit price. And naming that banks fail from liquidity, not capital, frames everything else you say.

   Expect next

   • How would you measure market liquidity risk in a bond book?
   • Which one killed more institutions in 2008?
   • How does FRTB handle illiquidity?
3. 061Explain LCR and NSFR.Liquidity risk and ALMIntermediatetechnicalTreasury and ALMRegulatory reporting

   Say this

   Both are Basel III liquidity ratios with a minimum of 100 percent. LCR is a 30-day survival test: high quality liquid assets over stressed net outflows. NSFR is a one-year structural test: available stable funding over required stable funding. Short-term shock versus long-term funding mismatch.

   Then walk it

   1. LCR numerator, HQLA: Level 1 is cash, central bank reserves and most sovereign debt at no haircut. Level 2A is high-grade covered and corporate bonds at a 15 percent haircut, Level 2B is lower-rated corporates and some equities at 25 to 50 percent, and Level 2 is capped at 40 percent of the total.
   2. LCR denominator: outflows minus capped inflows, with prescribed run-off rates. Stable retail deposits 3 to 5 percent, less stable retail 10, operational wholesale deposits 25, non-operational corporate deposits 40, and financial institution deposits 100. Inflows are capped at 75 percent of outflows, so you can't rely on collecting to pay.
   3. That run-off table is the heart of it, and it encodes a real judgement: insured retail deposits are sticky, and money from other banks disappears entirely. SVB's deposits were overwhelmingly uninsured corporate money, which the table would treat as the fastest-running kind.
   4. NSFR pairs funding stability against asset liquidity over a year. Equity and long-term debt count fully as stable funding, retail deposits at 90 to 95 percent, short wholesale funding at little or nothing. On the asset side, long-dated loans need high stable funding and cash needs none.
   5. So NSFR is a structural constraint on maturity transformation, which is the business banks are in. It limits how much of a long loan book you can fund with three-month wholesale paper.
   6. Indian specifics: RBI implemented LCR from 2015 and NSFR from 2021, both at 100 percent, and has periodically adjusted the treatment of SLR securities within HQLA. The 2024 draft revisions raised run-off assumptions on retail deposits with internet and mobile banking access, which is a direct response to how fast deposits can now move.
   7. The critique to volunteer: both are point-in-time ratios with prescribed assumptions, and they can be window-dressed at reporting dates. And in a real run, the run-off rates have been far higher than the table assumes. SVB lost a quarter of its deposits in a day.

   Where candidates lose it

   Mixing up the horizons or quoting the ratios without any run-off rates. Knowing that financial institution deposits run off at 100 percent and stable retail at 3 to 5 is what shows you've seen the schedule. And the point that real runs are faster than the assumed rates is the part with judgement in it.

   Expect next

   • What counts as HQLA, and what haircuts apply?
   • Why did banks with a 100 percent LCR still fail in 2023?
   • How does NSFR constrain the lending business?
4. 062What is interest rate risk in the banking book, and how do you measure it?Liquidity risk and ALMIntermediatetechnicalTreasury and ALMIndian bank risk and treasury

   Say this

   IRRBB is the risk that rate moves hurt the banking book, and you measure it two ways that often disagree. Economic value of equity is the present value view over the full life of the balance sheet. Net interest income is the earnings view over one to three years.

   Then walk it

   1. EVE: revalue all assets, liabilities and off-balance-sheet items under a rate shock and look at the change in net present value. It's the long-horizon, economic answer, and it's where a big fixed-rate asset book shows up immediately.
   2. NII: project interest income and expense over one to three years under the shock. It's the accounting and earnings answer, and it's the one management actually cares about because it hits reported profit.
   3. They can point in opposite directions, and that's the interesting part. A bank funding long fixed-rate mortgages with short deposits looks fine on NII when rates rise slowly, because deposit rates lag, while EVE is deeply negative from day one. That gap is exactly the SVB configuration.
   4. The Basel standardised framework prescribes six shock scenarios: parallel up and down, steepener, flattener, short rate up and short rate down. Non-parallel scenarios matter because most banks are not exposed to the level so much as to the shape.
   5. Then the behavioural assumptions, which are where all the model risk lives. Non-maturity deposits have no contractual maturity so you assume one. Prepayment on fixed loans. Early withdrawal on term deposits. Pipeline commitments. Move the deposit assumption from a two-year to a five-year effective duration and the answer changes sign.
   6. The supervisory outlier test: if EVE sensitivity exceeds 15 percent of Tier 1 capital under any of the six scenarios, you attract supervisory attention under Pillar 2. That's the number to know.
   7. In India, RBI requires both the traditional gap approach and duration-based EVE reporting, and IRRBB is a core Pillar 2 item in ICAAP. Indian banks carry large SLR portfolios of government bonds, so rate risk in the banking book is structurally significant and the AFS versus HTM classification decision drives how much of it hits reported capital.
   8. How you manage it: reprice the book, use interest rate swaps to shorten effective asset duration, adjust deposit pricing, and set limits on both EVE and NII sensitivity so neither view can be ignored.

   Where candidates lose it

   Giving only one of the two measures. If you say EVE and not NII, or the reverse, you've described half the framework and missed the tension that makes IRRBB interesting. And the behavioural deposit assumption is the single biggest driver of the answer, so name it as the main model risk.

   Expect next

   • EVE and NII disagree. Which do you act on?
   • What is the supervisory outlier test?
   • How would you hedge a negative EVE position?
5. 066What does it mean if an estimator is BLUE?Statistics and quant foundationsHardtechnicalUBSRisk Management · Zurich · 2021

   Say this

   Best Linear Unbiased Estimator. Among all estimators that are linear in the data and unbiased, it has the smallest variance. That's the Gauss-Markov result: ordinary least squares is BLUE provided a specific set of assumptions holds.

   Then walk it

   1. Unpack each word, because that's what the question is testing. Linear in the observations. Unbiased, so its expected value equals the true parameter. Best, meaning minimum variance within that class.
   2. The Gauss-Markov conditions: correct linear specification, errors with zero conditional mean, homoskedasticity, no autocorrelation, and no perfect multicollinearity. Notice normality is not required for BLUE. You need normality for the t and F tests in small samples, not for OLS to be efficient.
   3. The restriction that matters is 'linear'. A biased or non-linear estimator can easily beat OLS on mean squared error. Ridge and lasso are deliberately biased and often predict better, and James-Stein shrinkage famously dominates the sample mean. So BLUE is optimality within a box, not optimality.
   4. In finance the assumptions fail routinely. Returns are heteroskedastic and volatility clusters, so OLS stays unbiased but the standard errors are wrong, which means your t-statistics lie. That's the practical consequence and it's the one to lead with when asked what breaks.
   5. The fixes: White or Newey-West robust standard errors for heteroskedasticity and autocorrelation, generalised least squares if you know the error structure, and instrumental variables if the regressor is endogenous. Endogeneity is the serious one, because it destroys unbiasedness rather than just efficiency.
   6. The distinction to keep straight: heteroskedasticity and autocorrelation cost you efficiency and valid inference. Omitted variables, measurement error in a regressor and simultaneity cost you unbiasedness. Those are different problems needing different fixes.
   7. So the answer I'd close with: BLUE is a useful benchmark and a weak guarantee. In a risk model I care more about whether the specification is right and whether the relationship is stable than about being efficient within the linear unbiased class.

   Where candidates lose it

   Expanding the acronym and stopping, or claiming normality is a Gauss-Markov requirement. It isn't. The two answers that separate candidates are that 'best' is only within linear unbiased estimators, so biased shrinkage estimators can beat it, and that in finance the binding violation is heteroskedasticity making your standard errors wrong.

   Expect next

   • Is normality required for OLS to be BLUE?
   • Which Gauss-Markov assumption fails most often in financial data?
   • Can a biased estimator ever be preferable?

   Reported by candidates at UBS (Risk Management, Zurich, 2021). Source: Wall Street Oasis.

6. 067Would you model equity returns as normal or Student's t, and what difference does it make?Statistics and quant foundationsIntermediatetechnicalBank market riskBuy-side risk

   Say this

   Student's t, with something like four to six degrees of freedom for daily equity returns. Real returns are leptokurtic, so a normal assumption systematically understates the tail, and the understatement gets worse the further out you go.

   Then walk it

   1. The evidence is simple and worth quoting: daily equity index returns have excess kurtosis well above zero, often 3 to 8, against zero for a normal. The 1987 crash was more than 20 standard deviations under a normal fitted to prior data, which that distribution says should never happen in the age of the universe.
   2. A t with low degrees of freedom has polynomial rather than exponential tail decay, which fits observed extremes far better. Around four to six degrees of freedom is the usual empirical range for daily equity data, and it converges to normal as the degrees of freedom rise.
   3. The practical consequence for VaR: at 95 percent the two distributions give similar answers, and at 99.9 percent the normal can understate by a large multiple. So the choice barely matters for routine reporting and matters enormously for capital and stress.
   4. The second issue is that the unconditional fat tail is partly a mixture effect. Returns are closer to normal conditional on the current volatility regime, and it's volatility clustering that produces fat unconditional tails. So a GARCH model with normal innovations gets you a long way, and GARCH with t innovations gets you further.
   5. For a multivariate problem it gets harder. A multivariate t gives you tail dependence, which a multivariate normal does not, and tail dependence is the property that matters in a crisis. That's the bridge to copulas.
   6. Where the t is not enough: extreme value theory, fitting a generalised Pareto distribution to the exceedances above a threshold, is the right tool if you genuinely only care about the far tail. It uses less data more efficiently for that specific job.
   7. The caveat I'd give: a t distribution has more parameters and the degrees of freedom estimate is unstable in small samples. And skew matters too, since equity downside tails are fatter than upside ones, so a skewed t or an empirical distribution is often the pragmatic choice.

   Where candidates lose it

   Saying 'returns aren't normal' without knowing what to use instead or how big the error is. Naming a plausible degrees-of-freedom range, and saying that the choice matters at 99.9 percent but hardly at 95, shows you've fitted these to real data rather than repeating a slogan.

   Expect next

   • How would you estimate the degrees of freedom?
   • Does GARCH with normal innovations solve the problem?
   • When would you use extreme value theory instead?
7. 068Correlation versus dependence. Why does correlation fail exactly when you need it?Statistics and quant foundationsHardsuperdayBank market riskModel validation

   Say this

   Pearson correlation measures only the linear component of dependence, and it's a single average number. Dependence is the whole joint distribution. So two variables can be uncorrelated and completely dependent, and correlation tells you nothing about whether they move together in the tail.

   Then walk it

   1. The clean counterexample: Y equals X squared with X symmetric around zero. Perfectly dependent, zero linear correlation. Any non-monotonic relationship defeats Pearson.
   2. It's also not invariant to non-linear transforms, which matters because option payoffs are non-linear transforms of the underlying. Rank measures like Kendall's tau and Spearman's rho are invariant and are what you should use for dependence.
   3. The failure that costs money is that correlation is an average over the whole distribution, dominated by the many ordinary days. Tail dependence, the probability that both variables are extreme together, is a separate property that Pearson does not capture at all.
   4. Two distributions can share the same correlation matrix and have utterly different joint tails. A Gaussian copula has zero asymptotic tail dependence: extreme joint events are asymptotically independent. A t copula has positive tail dependence. Same correlation, completely different stress behaviour.
   5. Then correlations are non-stationary and they rise in a crisis. Equity pair correlations that sit around 0.3 in calm markets go to 0.8 in a sell-off. Diversification built on the calm number evaporates precisely when you were counting on it.
   6. Part of that rise is a statistical artefact worth knowing about: conditioning on large moves mechanically increases measured correlation even with a stable underlying joint distribution. So not all of the observed increase is a regime change, and that subtlety is a genuinely strong thing to say.
   7. What I'd do instead: use rank correlations for dependence, look at exceedance correlations conditional on large moves, use a copula with tail dependence when I need a joint distribution, and stress correlations to one in the scenario rather than trusting the estimate.
   8. Real cost: the 2007 quant equity deleveraging and the 2008 structured credit losses were both failures of dependence assumptions rather than of the individual marginal distributions.

   Where candidates lose it

   Saying 'correlation goes to one in a crisis' as the whole answer. That's true and it's the easy half. The deeper point is that correlation and dependence are different objects, that two joint distributions can share a correlation matrix and differ entirely in the tail, and that part of the observed crisis rise is a conditioning artefact.

   Expect next

   • What is tail dependence?
   • Why do Gaussian and t copulas differ if they have the same correlation?
   • How would you stress correlations in a scenario?
8. 069What is a copula, and what went wrong with the Gaussian copula in 2008?Statistics and quant foundationsHardsuperdayModel validationBank credit risk

   Say this

   A copula separates the marginal distributions from the dependence structure. Sklar's theorem says any joint distribution can be written as a copula applied to its marginals, so you can model each variable's tail properly and then choose how they move together. The 2008 failure was choosing a Gaussian copula, which has no tail dependence, for a problem that is entirely about tail dependence.

   Then walk it

   1. Mechanically: transform each variable to a uniform through its own cumulative distribution, then model the joint behaviour of those uniforms. That's the copula. It's a clean separation of 'how fat is each tail' from 'do they go bad together'.
   2. Gaussian copula: parameterised by a correlation matrix, and its asymptotic tail dependence is zero for any correlation below one. So as you go further into the tail, extreme joint events become asymptotically independent. That's the mathematical fact behind the failure.
   3. t copula: has positive tail dependence controlled by the degrees of freedom, so extremes cluster. Gumbel gives you upper tail dependence only, Clayton lower tail only, which is useful for credit where you care about joint defaults and not joint survivals.
   4. What actually happened with CDOs. David Li's Gaussian copula model let you price a tranche off a single correlation number, calibrated to historical data from a benign period, often 0.3 for mortgage pools. The senior tranche's value depends almost entirely on the probability that many defaults happen together, which is exactly the quantity the Gaussian copula sets too low.
   5. So the model said AAA was safe because widespread simultaneous default across regions was nearly impossible. In a national housing downturn, correlation went towards one and losses blew through tranches the model said were remote.
   6. The market knew before the maths admitted it. The base correlation skew, needing a different correlation for each tranche to fit observed prices, was the model telling you it was wrong, and it was read as a market quirk instead.
   7. The honest lesson, and the one to say: the failure was not really the copula. It was calibrating a tail parameter to data with no tail in it, using one number to describe dependence across a whole system, and treating a pricing convention as a risk model. A t copula with a bad correlation input would have failed too.
   8. Practical use today: copulas are standard in economic capital, portfolio credit models and multi-asset VaR. You'd use a t copula, calibrate dependence with attention to stress periods, and stress the dependence parameter rather than point-estimating it.

   Where candidates lose it

   Blaming 'the formula that killed Wall Street' without being able to say what property was missing. The specific answer is zero asymptotic tail dependence, plus a correlation parameter calibrated on benign data. And the mature closing point is that a better copula with the same bad calibration would also have failed.

   Expect next

   • Why does the Gaussian copula have zero tail dependence?
   • What was base correlation telling the market?
   • Would a t copula have prevented it?
9. 070What's the tracking error formula?Statistics and quant foundationsIntermediatetechnicalMSCIFinancial Tools · Monterrey · 2013

   Say this

   Tracking error is the standard deviation of the difference between portfolio and benchmark returns. Compute active return each period, take its standard deviation, then annualise by multiplying by the square root of the number of periods in a year.

   Then walk it

   1. Formula in words: active return equals portfolio return minus benchmark return each period. Tracking error is the standard deviation of that series. On monthly data you annualise by root twelve, on daily by root 252.
   2. The ex-post version uses realised returns. The ex-ante version uses a risk model: active weights transposed times the covariance matrix times active weights, then square root. Those two numbers routinely disagree, and explaining the gap is a real part of a buy-side risk job.
   3. Be careful about mean adjustment. Some definitions use the standard deviation of active returns and others use the root mean square of active returns, which includes the average outperformance. They differ, and you should say which one you mean.
   4. Feel for the numbers: an index fund runs 5 to 50 basis points, an enhanced index strategy 0.5 to 2 percent, an active core equity fund 3 to 6 percent, and a concentrated high-conviction fund 8 percent or more. Quoting a range is what shows you've looked at real funds.
   5. What it's used for: mandate limits, since most institutional mandates cap tracking error. And the information ratio, active return divided by tracking error, which is the risk-adjusted measure of skill and the number that actually matters.
   6. Decomposition is the useful part. Break tracking error into factor and specific contributions: how much comes from a systematic style or sector tilt versus stock selection. A manager paid for stock picking who is running most of their tracking error on an unintended country bet has a problem, and that's exactly the conversation a risk analyst has with a PM.
   7. Limitations to volunteer: it's symmetric, so it penalises outperformance identically to underperformance. It assumes a stable covariance structure, so realised tracking error jumps in a crisis. And it's backward-looking, so a manager who has just changed their positioning has a stale number.

   Where candidates lose it

   Giving the formula and nothing else. This question is a screen: the formula takes five seconds, and the rest of your answer is what's being assessed. Knowing typical ranges by strategy, the ex-ante versus ex-post gap, and factor decomposition is what converts a definition into an answer.

   Expect next

   • What tracking error would you expect from an index fund?
   • Why do ex-ante and ex-post tracking error differ?
   • What is the information ratio?

   Reported by candidates at MSCI (Financial Tools, Monterrey, 2013). Source: Wall Street Oasis.

10. 073Walk me through how an Indian bank classifies a loan as non-performing under the IRAC norms.Indian regulationIntermediatetechnicalIndian bank risk and treasuryGlobal capability centres

    Say this

    Under RBI's income recognition and asset classification norms, an account becomes non-performing when principal or interest is overdue for more than 90 days. Before that it sits in special mention accounts, and after that it ages from substandard to doubtful to loss, with provisioning rising at each step.

    Then walk it

    1. Before default: SMA-0 is up to 30 days overdue, SMA-1 is 31 to 60 days, SMA-2 is 61 to 90. These are reported to the Central Repository of Information on Large Credits, so the whole system can see a large borrower slipping. That transparency is a genuinely good feature of the Indian framework.
    2. At 90 days past due the account is an NPA. For cash credit and overdrafts the trigger is the account being out of order, meaning continuously in excess of the limit or with insufficient credits to cover interest, for 90 days.
    3. Then ageing. Substandard for up to 12 months as an NPA. Doubtful after that, sub-divided by how long it's been doubtful. Loss when it's considered unrecoverable.
    4. Provisioning rises with the ageing: 15 percent on secured substandard and 25 percent on unsecured, then doubtful provisioning stepping up on the secured portion from 25 to 40 to 100 percent depending on the duration, with the unsecured portion at 100 percent throughout, and 100 percent for loss assets.
    5. Two things that catch people out. Income recognition stops: interest on an NPA can't be taken to the P&L unless actually received, and previously accrued unrealised interest has to be reversed. And borrower-level classification means all facilities of that borrower go NPA together, not just the delinquent one.
    6. Then the 2021 clarification on daily overdue recognition and on upgrading. An account can only be upgraded out of NPA when the entire arrears of interest and principal are paid, which stopped the practice of a token payment moving an account back to standard.
    7. The reason the framework is so prescriptive: rule-based and time-based classification removes management discretion. It's deliberately less judgemental than IFRS 9, and that's the trade-off, less economic sensitivity in exchange for far less gaming.
    8. Worth knowing the scale: gross NPAs in the Indian banking system peaked around 11 percent in 2018 and have fallen to the low single digits, and the asset quality review of 2015 to 2016 was what forced the recognition.

    Where candidates lose it

    Saying '90 days' and stopping. The interviewer wants the SMA buckets before it, the substandard-doubtful-loss ageing after it, the provisioning percentages, and the income reversal rule. For any India-based risk role, this is the most basic credit question there is and vagueness is fatal.

    Expect next

    • What are the SMA buckets and why do they exist?
    • What provisioning applies to a secured doubtful asset after two years?
    • When can an NPA be upgraded to standard?

Firm tags come from public, anonymous candidate reports on Wall Street Oasis: strong signal, not sworn testimony. Firms are named as the places a question was reported, not as partners of Fin Maverick. Answers are written for this page to show how to think out loud; they are not scripts to recite.