Databricks DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Exam Questions & Answers

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.

A. Naive Bayes

B. Laplace Smoothing

C. Logistic Regression

D. Linear Regression

What is the best way to evaluate the quality of the model found by an unsupervised algorithm like k-means clustering, given metrics for the cost of the clustering (how well it fits the data) and its stability (how similar the clusters are across multiple runs over the same data)?

A. The lowest cost clustering subject to a stability constraint

B. The lowest cost clustering

C. The most stable clustering subject to a minimal cost constraint

D. The most stable clustering

Suppose that the probability that a pedestrian will be tul by a car while crossing the toad at a pedestrian crossing without paying attention to the traffic light is lo be computed. Let H be a discrete random variable taking one value from (Hit. Not Hit). Let L be a discrete random variable taking one value from (Red. Yellow. Green).

Realistically, H will be dependent on L That is, P(H = Hit) and P(H = Not Hit) will take different values depending on whether L is red, yellow or green. A person is. for example, far more likely to be hit by a car when trying to cross while Hie lights for cross traffic are green than if they are red In other words, for any given possible pair of values for Hand L. one must consider the joint probability distribution of H and L to find the probability* of that pair of events occurring together if Hie pedestrian ignores the state of the light

Here is a table showing the conditional probabilities of being bit. defending on ibe stale of the lights (Note that the columns in this table must add up to 1 because the probability of being hit oi not hit is 1 regardless of the stale of the light.)

A. The marginal probability P(H=Hit) is the sum along the H=Hit row of this joint distribution table, as this is the probability of being hit when the lights are red OR yellow OR green.

B. marginal probability that P(H=Not Hit) is the sum of the H=Not Hit row

C. marginal probability that P(H=Not Hit) is the sum of the H= Hit row