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

• Question 81:

  Marie is getting married tomorrow, at an outdoor ceremony in the desert. In recent years, it has rained only 5 days each year. Unfortunately, the weatherman has predicted rain for tomorrow. When it actually rains, the weatherman correctly forecasts rain 90% of the time. When it doesn't rain, he incorrectly forecasts rain 10% of the time. Which of the following will you use to calculate the probability whether it will rain on the day of Marie's wedding?

  A. Naive Bayes

  B. Logistic Regression

  C. Random Decision Forests

  D. All of the above

  Correct Answer: A

  Explanation: The sample space is defined by two mutually-exclusive events - it rains or it does not rain. Additionally, a third event occurs when the weatherman predicts rain. You should consider Bayes' theorem when the following conditions exist. ?The sample space is partitioned into a set of mutually exclusive events {A1, A2,... :An}. ?Within the sample space, there exists an event B: for which P(B)>; 0. ?The analytical goal is to compute a conditional probability of the form: P ( Ak B).

• Question 82:

  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

  Correct Answer: AB

  Explanation: 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. Similarly, the marginal probability that P(H=Not Hit) is the sum of the H=Not Hit row

• Question 83:

  What is the best way to ensure that the k-means algorithm will find a good clustering of a collection of vectors?

  A. Only consider values of k larger than log(N), where N is the number of observations in the data set

  B. Run at least log(N) iterations of Lloyd's algorithm, where N is the number of observations in the data set

  C. Choose the initial centroids so that they all He along different axes

  D. Choose the initial centroids so that they are far away from each other

  Correct Answer: D

  Explanation: k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining, k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. The problem is computationally difficult (NP-hard); however there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both algorithms. Additionally, they both use cluster centers to model the data; however k-means clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes This Question-is about the properties that make k-means an effective clustering heuristic which primarily deal with ensuring that the initial centers are far away from each other. This is how modern k-means algorithms like k-means++ guarantee that with high probability Lloyd's algorithm will find a clustering within a constant factor of the optimal possible clustering for each k.

• Question 84:

  Which of the following is a correct example of the target variable in regression (supervised learning)?

  A. Nominal values like true, false

  B. Reptile, fish, mammal, amphibian, plant, fungi

  C. Infinite number of numeric values, such as 0.100, 42.001, 1000.743..

  D. All of the above

  Correct Answer: D

  Explanation: We address two cases of the target variable. The first case occurs when the target variable can take only nominal values: true or false; reptile, fish: mammal, amphibian, plant, fungi. The second case of classification occurs when the target variable can take an infinite number of numeric values, such as 0.100, 42.001, 1000.743, .... This case is called regression.

• Question 85:

  You are using k-means clustering to classify heart patients for a hospital. You have chosen Patient Sex, Height, Weight, Age and Income as measures and have used 3 clusters. When you create a pair-wise plot of the clusters, you notice that there is significant overlap between the clusters. What should you do?

  A. Identify additional measures to add to the analysis

  B. Remove one of the measures

  C. Decrease the number of clusters

  D. Increase the number of clusters

  Correct Answer: C

• Question 86:

  You are working in a data analytics company as a data scientist, you have been given a set of various types of Pizzas available across various premium food centers in a country. This data is given as numeric values like Calorie. Size, and Sale per day etc. You need to group all the pizzas with the similar properties, which of the following technique you would be using for that?

  A. Association Rules

  B. Naive Bayes Classifier

  C. K-means Clustering

  D. Linear Regression

  E. Grouping

  Correct Answer: C

  Explanation: Using K means clustering you can create group of objects based on their properties. Where K is number of the groups. In this case, in each group you determine the center of the group and then find the how far each object characteristics from the center. If it is near the center than it can be part of the group. Suppose we have 100 objects and we need to determine 4 groups. Hence, here K=4. Now we determine 4 center values and based on that center value we determine the distance of each object from the center.

• Question 87:

  Suppose you have made a model for the rating system, which rates between 1 to 5 stars. And you calculated that RMSE value is 1.0 then which of the following is correct

  A. It means that your predictions are on average one star off of what people really think

  B. It means that your predictions are on average two star off of what people really think

  C. It means that your predictions are on average three star off of what people really think

  D. It means that your predictions are on average four star off of what people really think

  Correct Answer: A

• Question 88:

  Projecting a multi-dimensional dataset onto which vector has the greatest variance?

  A. first principal component

  B. first eigenvector

  C. not enough information given to answer

  D. second eigenvector

  E. second principal component

  Correct Answer: A

  Explanation: The method based on principal component analysis (PCA) evaluates the features according to the projection of the largest eigenvector of the correlation matrix on the initial dimensions, the method based on Fisher's linear discriminant analysis evaluates. Them according to the magnitude of the components of the discriminant vector. The first principal component corresponds to the greatest variance in the data, by definition. If we project the data onto the first principal component line, the data is more spread out (higher variance) than if projected onto any other line, including other principal components.

• Question 89:

  You are using one approach for the classification where to teach the agent not by giving explicit categorizations, but by using some sort of reward system to indicate success, where agents might be rewarded for doing certain actions and

  punished for doing others.

  Which kind of this learning?

  A. Supervised

  B. Unsupervised

  C. Regression

  D. None of the above

  Correct Answer: B

  Explanation: Unsupervised learning seems much harder: the goal is to have the computer learn how to do something that we don't tell it how to do! The approach is to teach the agent not by giving explicit categorizations, but by using some sort of reward system to indicate success. Note that this type of training will generally fit into the decision problem framework because the goal is not to produce a classification but to make decisions that maximize rewards. This approach nicely generalizes to the real world, where agents might be rewarded for doing certain actions and punished fordoing others.

• Question 90:

  Select the correct statement which applies to K-Nearest Neighbors

  A. No Assumption about the data

  B. Computationally expensive

  C. Require less memory

  D. Works with Numeric Values

  Correct Answer: ABD

  Explanation: : k-Nearest Neighbors Pros: High accuracy insensitive to outliers, no assumptions about data Cons: Computationally expensive, requires a lot of memory Works with: Numeric values, nominal values