Databricks-Certified-Professional-Data-Scientist Exam Questions - Databricks-Certified-Professional-Data-Scientist PDF

Latest Databricks Databricks-Certified-Professional-Data-Scientist Exam Questions

We regularly update our Databricks Databricks-Certified-Professional-Data-Scientist Exam Questions, following is a glimpse of the latest Databricks-Certified-Professional-Data-Scientist Exam Questions updated in our Databricks Databricks-Certified-Professional-Data-Scientist Exam preparation products. Buy Databricks Databricks-Certified-Professional-Data-Scientist Exam preparation material listed above to avail a full set of the updated exam preparation material.

Question 1

Which method is used to solve for coefficients bO, b1, ... bn in your linear regression model:

AApriori Algorithm

BRidge and Lasso

COrdinary Least squares

DInteger programming

Question 2

Which of the following is a Continuous Probability Distributions?

ABinomial probability distribution

BNegative binomial distribution

CPoisson probability distribution

DNormal probability distribution

Question 3

Consider flipping a coin for which the probability of heads is p, where p is unknown, and our goa is to estimate p. The obvious approach is to count how many times the coin came up heads and divide by the total number of coin flips. If we flip the coin 1000 times and it comes up heads 367 times, it is very reasonable to estimate p as approximately 0.367. However, suppose we flip the coin only twice and we get heads both times. Is it reasonable to estimate p as 1.0? Intuitively, given that we only flipped the coin twice, it seems a bit

rash to conclude that the coin will always come up heads, and____________is a way of avoiding such rash

conclusions.

ANaive Bayes

BLaplace Smoothing

CLogistic Regression

DLinear Regression
Smooth the estimates: consider flipping a coin for which the probability of heads is p, where p is unknown, and our goal is to estimate p. The obvious approach is to count how many times the coin came up heads and divide by the total number of coin flips. If we flip the coin 1000 times and it comes up heads 367 times, it is very reasonable to estimate p as approximately 0.367. However, suppose we flip the coin only twice and we get heads both times. Is it reasonable to estimate p as 1.0? Intuitively, given that we only flipped the coin twice, it seems a bit rash to conclude that the coin will always come up heads, and smoothing is a way of avoiding such rash conclusions. A simple smoothing method, called Laplace smoothing (or Laplace's law of succession or add-one smoothing in R&N), is to estimate p by (one plus the number of heads) / (two plus the total number of flips). Said differently, if we are keeping count of the number of heads and the number of tails, this rule is equivalent to starting each of our counts at one, rather than zero. Another advantage of Laplace smoothing is that it avoids estimating any probabilities to be zero, even for events never observed in the data. Laplace add-one smoothing now assigns too much probability to unseen words

Question 4

You are working on a email spam filtering assignment, while working on this you find there is new word e.g. HadoopExam comes in email, and in your solutions you never come across this word before, hence probability of this words is coming in either email could be zero. So which of the following algorithm can help you to avoid zero probability?

ANaive Bayes

BLaplace Smoothing

CLogistic Regression

DAll of the above
Laplace smoothing is a technique for parameter estimation which accounts for unobserved events. It is more robust and will not fail completely when data that has never been observed in training shows up.

Question 5

Suppose there are three events then which formula must always be equal to P(E1|E2,E3)?

AP(E1,E2,E3)P(E1)/P(E2:E3)

BP(E1,E2;E3)/P(E2,E3)

CP(E1,E2|E3)P(E2|E3)P(E3)

DP(E1,E2|E3)P(E3)

EP(E1,E2,E3)P(E2)P(E3)
This is an application of conditional probability: P(E1,E2)=P(E1|E2)P(E2). so
P(E1|E2) = P(E1.E2)/P(E2)
P(E1,E2,E3)/P(E2,E3)
If the events are A and B respectively, this is said to be 'the probability of A given B'
It is commonly denoted by P(A|B): or sometimes PB(A). In case that both 'A' and 'B' are categorical variables, conditional probability table is typically used to represent the conditional probability.

Prepare Databricks-Certified-Professional-Data-Scientist Exam

With Databricks Certified Professional Data Scientist Exam Exam Questions

Databricks-Certified-Professional-Data-Scientist Desktop Practice Test Software

Databricks-Certified-Professional-Data-Scientist Questions & Answers (PDF)

Databricks-Certified-Professional-Data-Scientist Web-Based Self-Assessment
Practice Test

Latest Databricks Databricks-Certified-Professional-Data-Scientist Exam Questions

Prepare Databricks-Certified-Professional-Data-Scientist Exam

With Databricks Certified Professional Data Scientist Exam Exam Questions

Databricks-Certified-Professional-Data-Scientist Desktop Practice Test Software

Databricks-Certified-Professional-Data-Scientist Questions & Answers (PDF)

Databricks-Certified-Professional-Data-Scientist Web-Based Self-Assessment Practice Test

Latest Databricks Databricks-Certified-Professional-Data-Scientist Exam Questions

Databricks-Certified-Professional-Data-Scientist Web-Based Self-Assessment
Practice Test