Exam AWS Certified Machine Learning - Specialty topic 1 question 42 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty

Question #: 42
Topic #: 1

[All AWS Certified Machine Learning - Specialty Questions]

A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.
Which prior probability distribution should the ML Specialist use for this variable?

A. Poisson distribution
B. Uniform distribution
C. Normal distribution
D. Binomial distribution

Show Suggested Answer

Suggested Answer: A 🗳️

by DonaldCMLIN at Nov. 16, 2019, 3:03 p.m.

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ComPah

Highly Voted 3 years, 9 months ago

A If you have information about the average (mean) number of things that happen in some given time period / interval,Poisson distribution can give you a way to predict the odds of getting some other value on a given future day

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swagy

Highly Voted 3 years, 8 months ago

Ans: A https://brilliant.org/wiki/poisson-distribution/

upvoted 8 times

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Mobasher

Most Recent 4 months, 2 weeks ago

Selected Answer: B

ChatGPT's answer: B Explanation The problem describes a random variable representing the waiting time for a bus, where buses arrive every 10 minutes, and the mean waiting time is 3 minutes. In such a periodic arrival process, the waiting time follows a Uniform Distribution because: - Any given person’s waiting time is equally likely to be any value between 0 and 10 minutes. - There is no clustering around a particular value—every moment within the cycle is equally probable. Thus, the waiting time follows a Uniform(0, 10) distribution.

upvoted 1 times

Mobasher

4 months, 2 weeks ago

Why Not the Other Options? (A) Poisson Distribution Poisson is used for counting discrete events over a fixed period (e.g., number of buses arriving per hour). Since waiting time is continuous, Poisson is not appropriate. (C) Normal Distribution Normal (Gaussian) distribution assumes values cluster around the mean and extend infinitely. Here, waiting time is evenly spread between 0–10 minutes, not forming a bell curve. (D) Binomial Distribution Binomial is used for counting successes in a fixed number of trials (e.g., flipping a coin multiple times). Waiting time is continuous, not a count of discrete occurrences.

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growe

5 months, 3 weeks ago

Selected Answer: B

Buses cycle every 10 minutes, and waiting time can be modeled as a uniform random variable between [0, 10] minutes. The average waiting time of 3 minutes suggests that waiting is uniformly distributed, not event-based like Poisson. If buses arrive every 10 minutes and riders arrive randomly, the waiting time follows a Uniform Distribution (B) because: The arrival process is regular (every 10 minutes). There’s no stochastic randomness in the bus arrival schedule, ruling out Poisson. Poisson would apply if buses arrived randomly at an average rate rather than at fixed intervals.

upvoted 2 times

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