Simulation DATA-SCIENCE-OPTIMIZE Practice Questions (Page 2)

Questions 6–10

1. 6foundation · easy

   What is the key characteristic of bootstrapping?

   • AResampling the original dataset with replacement
   • BSampling without replacement to create smaller datasets
   • CGenerating new data from a theoretical distribution
   • DSplitting the data into training and test sets
   Reveal Solution Discussion Select an answer first
2. 7foundation · easy

   What is the goal of sensitivity analysis via simulation?

   • ATo make all input variables constant to simplify the model
   • BTo remove uncertainty from the model entirely
   • CTo determine which input variables have the most impact on model outputs
   • DTo find the exact optimal values for all input variables
   Reveal Solution Discussion Select an answer first
4. 8application · medium

   A pharmaceutical company is using a simulation model to estimate the efficacy of a new drug. The model has several uncertain parameters, including absorption rate and clearance rate. The company wants to identify which parameter, when varied within its plausible range, causes the largest change in predicted efficacy. Which approach is most appropriate?

   • ARun a Monte Carlo simulation that varies all parameters simultaneously and then compute the correlation between each parameter and efficacy.
   • BSet all parameters to their most likely values and run the model once.
   • CPerform a one-at-a-time sensitivity analysis: vary each parameter across its plausible range while holding others at their baseline values, and measure the resulting change in efficacy.
   • DUse bootstrapping on clinical trial data to estimate the distribution of efficacy.
   Reveal Solution Discussion Select an answer first
5. 9foundation · easy

   What is the primary use of bootstrapping in statistics?

   • ATo generate completely new independent data
   • BTo remove outliers from the dataset
   • CTo increase the size of the original dataset
   • DTo estimate the sampling distribution of a statistic
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6. 10application · medium

   A financial analyst is evaluating a loan default model. The model's output is highly sensitive to the interest rate assumption, which is uncertain. The analyst wants to understand how changes in the interest rate affect the predicted default rate. Which approach is most appropriate?

   • ASet the interest rate to its most likely value and run the model once to get a point estimate.
   • BUse bootstrapping to resample historical loan data and refit the model to each resample.
   • CRun a sensitivity analysis by varying the interest rate across a plausible range while holding other inputs constant, and observe how the default rate changes.
   • DRun a Monte Carlo simulation that varies all inputs simultaneously and then compute the correlation between the interest rate and the default rate.
   Reveal Solution Discussion Select an answer first