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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 Hide Answer
Suggested Answer: A 🗳️
by DonaldCMLIN at Nov. 16, 2019, 3:03 p.m.
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ComPah
Highly Voted 3 years, 4 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
upvoted 56 times
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swagy
Highly Voted 3 years, 4 months ago
Ans: A https://brilliant.org/wiki/poisson-distribution/
upvoted 8 times
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Mobasher
Most Recent 4 days, 11 hours 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 days, 11 hours 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.
upvoted 1 times
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growe
1 month, 1 week 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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87ebc7d
2 months, 2 weeks ago
B Poisson is suitable for modeling the number of events (like buses arriving) in a fixed time frame, not the time between events when the events occur at regular intervals. The waiting time variable is not about the count of buses but rather the time to the next bus, which is evenly distributed.
upvoted 1 times
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elvin_ml_qayiran25091992razor
1 year, 2 months ago
Selected Answer: A
A is correct
upvoted 1 times
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Fred93
1 year, 4 months ago
Selected Answer: A
Poisson distribution is discrete, and gives the number of events that occur in a given time interval
upvoted 2 times
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loict
1 year, 4 months ago
Selected Answer: A
A. YES - Poisson distribution is discrete, and gives the number of events that occur in a given time interval B. NO - Uniform distribution is continuous, we want discrete C. NO - Normal distribution is continuous we want discrete D. NO - Binomial distribution give the probability that a random variable is A or B (possibly in with different weight)
upvoted 2 times
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Mickey321
1 year, 5 months ago
Selected Answer: A
Option A indeed
upvoted 1 times
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Nadia0012
1 year, 11 months ago
Selected Answer: A
ANSWER IS A https://www.investopedia.com/terms/d/discrete-distribution.asp
upvoted 2 times
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bakarys
1 year, 11 months ago
Selected Answer: A
The Poisson distribution is commonly used for count data, which is the case here as we are interested in the number of minutes New Yorkers wait for a bus. The Poisson distribution is characterized by a single parameter, lambda, which represents the mean and variance of the distribution. In this case, the mean is 3 minutes, so we would set lambda to 3. The Poisson distribution assumes that events occur independently of each other, which is a reasonable assumption in this case since the waiting time for each individual is likely to be independent of the waiting time for others.
upvoted 4 times
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AjoseO
1 year, 12 months ago
Selected Answer: A
The Poisson distribution is a discrete probability distribution that is commonly used to model the number of events that occur in a fixed interval of time, given an average rate of occurrence. Since the buses cycle every 10 minutes and the mean wait time is 3 minutes, it is reasonable to assume that the number of minutes New Yorkers wait for a bus can be modeled by a Poisson distribution.
upvoted 3 times
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Tomatoteacher
2 years ago
Selected Answer: A
100% A, as discrete, while binomial has to be binary data (success or failure)
upvoted 1 times
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Sonoko
2 years, 1 month ago
Selected Answer: A
A is a discrete distribution
upvoted 1 times
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Peeking
2 years, 2 months ago
I do choose Poisson. A.
upvoted 1 times
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Shailendraa
2 years, 4 months ago
12-sep exam
upvoted 3 times
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Shailendraa
2 years, 4 months ago
Answer is A .. these types on footfalls ,etc ..answer always Poisson-distribution
upvoted 1 times
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