Cloudera Databricks Data Science Certification Questions and Answers (Dumps and Practice Questions)

Question : . It was found that the mean length of parts produced by a lathe was . mm
with a standard deviation of 0.02 mm. Find the probability that a part selected at random would have a length less than 20.01 mm



1. 0.0228
2. 0.0338.
3. Access Mostly Uused Products by 50000+ Subscribers
4. 0.0558.

Correct Answer : Get Lastest Questions and Answer :

Explanation: 20.01 is 2 s.d. (standard deviations) below the mean.
P(X less than 20.01)
=P(Z less than -2)
=0.5-0.4792
=0.0228
So the probability is 0.0228.




Question : It was found that the mean length of parts produced by a lathe was . mm with a
standard deviation of 0.02 mm. Find the probability that a part selected at random would have a length
between 20.06 mm and 20.07 mm

1. 0.1298
2. 0.1398
3. Access Mostly Uused Products by 50000+ Subscribers
4. 0.1598




Correct Answer : Get Lastest Questions and Answer :

Explanation: 20.06 is 0.5 standard deviations above the mean;
20.07 is 1 standard deviation above the mean
P(20.06 less than X less than 20.07)
=P(0.5 less than Z less than 1)
=0.3413-0.1915
=0.1498
So the probability is 0.1498.







Question : It was found that the mean length of parts produced by a lathe was . mm with a standard deviation of . mm.
Find the probability that a part selected at random would have a length greater than 20.09 mm.


1. 0.0228
2. 0.0338
3. Access Mostly Uused Products by 50000+ Subscribers
4. 0.0558

Correct Answer : Get Lastest Questions and Answer :

20.09 is 2 s.d. above the mean,
P(X > 20.09)=0.0228.
Same as
20.01 is 2 s.d. (standard deviations) below the mean.
P(X less than 20.01)
=P(Zless than -2)
=0.5-0.4792
=0.0228
So the probability is 0.0228.

Related Questions

---

Question : Select the statement which applies correctlty to the Naive Bayes


1. Works with a small amount of data
2. Sensitive to how the input data is prepared
3. Works with nominal values
4. All of above




Question :

Select the correct statement which applies to Bayes rule


1. Bayesian probability and Bayes' rule gives us a way to estimate unknown probabilities from known values.
2. You can reduce the need for a lot of data by assuming conditional independence among the features in your data.
3. Bayes' theorem finds the actual probability of an event from the results of your tests.
4. Only 1 and 2
5. All 1,2 and 3 are correct



Question : Which of the following technique can be used to the design of recommender systems?

1. Naive Bayes classifier
2. Power iteration
3. Collaborative filtering
4. 1 and 3
5. 2 and 3


Question : You are working on a problem where you have to predict whether the claim is done valid or not.
And you find that most of the claims which are having spelling errors as well as corrections in the manually
filled claim forms compare to the honest claims. Which of the following technique is suitable to find
out whether the claim is valid or not?

1. Naive Bayes
2. Logistic Regression
3. Random Decision Forests
4. Any one of the above




Question : . Bayes' Theorem allows you to look at an event that has already happened and make an
educated guess about the chain of events that may have led up to that event

1. True
2. False



Question :

Scenario: Suppose that Bob can decide to go to work by one of three modes of transportation,
car, bus, or commuter train. Because of high traffic, if he decides to go by car, there is a 50%
chance he will be late. If he goes by bus, which has special reserved lanes but is sometimes overcrowded,
the probability of being late is only 20%. The commuter train is almost never late, with a probability of
only 1%, but is more expensive than the bus.

Question : Suppose that Bob is late one day, and his boss wishes to estimate the probability that he
drove to work that day by car. Since he does not know which mode of transportation Bob usually uses,
he gives a prior probability of 1 3 to each of the three possibilities. Which of the following method the
boss will use to estimate of the probability that Bob drove to work?


1. Naive Bayes
2. Linear regression
3. Random decision forests
4. None of the above