Simulation DATA-SCIENCE-OPTIMIZE Practice Questions (Page 3)

Questions 11–15

1. 11application · medium

   A data scientist has a small sample of 50 customer satisfaction scores and wants to construct a confidence interval for the median score without assuming a normal distribution. Which method is most appropriate?

   • AUse a t-distribution with 49 degrees of freedom to construct the interval.
   • BTake the 2.5th and 97.5th percentiles of the original 50 scores directly as the confidence interval.
   • CUse bootstrapping: resample the 50 scores with replacement many times, compute the median for each resample, and take the 2.5th and 97.5th percentiles of those medians as the confidence interval.
   • DUse the formula mean ± 1.96 × (standard deviation / √50) to get the confidence interval.
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2. 12foundation · easy

   Why is setting a random seed important in simulation studies?

   • AIt makes the random numbers truly unpredictable
   • BIt ensures the simulation produces the same random sequence across runs
   • CIt speeds up the simulation by reducing the number of trials needed
   • DIt removes the need to specify probability distributions
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4. 13foundation · easy

   What is a benefit of using simulation to assess model performance under various scenarios?

   • AIt eliminates the need for any real-world data
   • BIt guarantees the model will perform perfectly in real-world deployment
   • CIt ensures the model is simple enough to run quickly
   • DIt allows testing of extreme or rare conditions that may not exist in historical data
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5. 14foundation · easy

   Which step is essential in a Monte Carlo simulation process?

   • ADefining probability distributions for uncertain input variables
   • BRemoving all randomness from the model to ensure stability
   • CSolving the model analytically to find the exact output
   • DCollecting a large sample of real-world data for each trial
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6. 15foundation · easy

   In a simulation-based sensitivity analysis, what is typically varied?

   • AThe model's algorithm to see which performs best
   • BThe values of input variables according to their probability distributions
   • CThe random seed to ensure reproducibility
   • DThe output variable to see which one is easiest to predict
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