Practice Test Geeks(CRA) Certified Risk Architect Practice Test
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Quantitative Risk Analysis Flashcards

7 cards from real CRA practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.

Read the first 7 Quantitative Risk Analysis flashcards as text

  1. What is the key difference between Value at Risk (VaR) and Expected Shortfall (ES)?

    Answer: ES averages losses beyond the VaR threshold; VaR only marks a loss quantile

    Expected Shortfall (CVaR) is the mean of losses that exceed the VaR level, providing a fuller picture of tail risk.

  2. Which simulation technique reduces variance in Monte Carlo output by ensuring samples are more evenly distributed across the input probability space?

    Answer: Latin Hypercube Sampling (LHS)

    LHS stratifies the input distribution into equal-probability intervals, ensuring better coverage and reducing output variance with fewer iterations.

  3. In a Bayesian network used for risk analysis, what do the conditional probability tables (CPTs) represent?

    Answer: The probability of a node's state given the states of its parent nodes

    CPTs define the conditional probability of each node given every combination of its parent nodes' states in the Bayesian network.

  4. A project schedule risk analysis shows a P50 completion date of June 1 and a P90 date of August 15. What does this mean for the risk manager?

    Answer: There is a 50% chance the project finishes before June 1 and a 90% chance before August 15

    P50 and P90 are percentiles: 50% of simulated outcomes complete by June 1 and 90% complete by August 15.

  5. Which of the following is a key limitation of using historical data alone for quantitative operational risk modeling?

    Answer: Rare tail events may not be represented in the historical record

    Black swan or low-frequency, high-severity events may simply not appear in historical datasets, causing models to underestimate tail risk.

  6. When combining frequency and severity distributions in a loss aggregation model, which mathematical operation is typically used?

    Answer: Convolution of the frequency and severity distributions

    Convolution combines the frequency and severity distributions to derive the aggregate loss distribution.

  7. In the context of credit risk, what does 'Loss Given Default' (LGD) measure?

    Answer: The proportion of exposure that is lost when a default occurs

    LGD is the fraction of the exposure at default that the lender cannot recover after a counterparty defaults.