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

• Question 101:

  Assume some output variable "y" is a linear combination of some independent input variables "A" plus some independent noise "e". The way the independent variables are combined is defined by a parameter vector B y=AB+e where X is an m x n matrix. B is a vector of n unknowns, and b is a vector of m values. Assuming that m is not equal to n and the columns of X are linearly independent, which expression correctly solves for B?

  

  A. Option A

  B. Option B

  C. Option C

  D. Option D

  Correct Answer: D

  Explanation: This is the standard solution of the normal equations for linear regression. Because A is not square, you cannot simply take its inverse.

• Question 102:

  What is the probability that the total of two dice will be greater than 8, given that the first die is a 6?

  A. 1/3

  B. 2/3

  C. 1/6

  D. 2/6

  Correct Answer: B

• Question 103:

  Refer to the Exhibit.

  

  In the Exhibit, the table shows the values for the input Boolean attributes "A", "B", and "C". It also shows the values for the output attribute "class". Which decision tree is valid for the data?

  A. Tree A

  B. Tree B

  C. Tree C

  D. Tree D

  Correct Answer: B

• Question 104:

  In which of the scenario you can use the regression to predict the values?

  A. Samsung can use it for mobile sales forecast

  B. Mobile companies can use it to forecast manufacturing defects

  C. Probability of the celebrity divorce

  D. Only 1 and 2

  E. All 1 ,2 and 3

  Correct Answer: E

  Explanation: Regression is a tool which Companies may use this for things such as sales forecasts or forecasting manufacturing defects. Another creative example is predicting the probability of celebrity divorce.

• Question 105:

  You are analyzing data in order to build a classifier model. You discover non-linear data and discontinuities that will affect the model. Which analytical method would you recommend?

  A. Logistic Regression

  B. Decision Trees

  C. Linear Regression

  D. ARIMA

  Correct Answer: B

  Explanation: A decision tree is a flowchart-like structure in which each internal node represents a "test" on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outcome of the test and each leaf node

  represents a class label (decision taken after computing all attributes). The paths from root to leaf represents classification rules.

  In decision analysis a decision tree and the closely related influence diagram are used as a visual and analytical decision support tool, where the expected values (or expected utility) of competing alternatives are calculated.

  A decision tree consists of 3 types of nodes:

  1.

  Decision nodes - commonly represented by squares

  2.

  Chance nodes - represented by circles

  3.

  End nodes - represented by triangles Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal. If in practice decisions have to be taken online with no recall under incomplete knowledge, a decision tree should be paralleled by a probability model as a best choice model or online selection model algorithm. Another use of decision trees is as a descriptive means for calculating conditional probabilities.

  Decision trees, influence diagrams, utility functions, and other decision analysis tools and methods are taught to undergraduate students in schools of business, health economics, and public health, and are examples of operations research or management science methods.

• Question 106:

  Select the correct statement which applies to Supervised learning

  A. We asks the machine to learn from our data when we specify a target variable.

  B. Lesser machine's task to only divining some pattern from the input data to get the target variable

  C. Instead of telling the machine Predict Y for our data X, we're asking What can you tell me about X?

  Correct Answer: ABC

  Explanation: : Supervised learning asks the machine to learn from our data when we specify a target variable.

  This reduces the machine's task to only divining some pattern from the input data to get the target variable.

  In unsupervised learning we don't have a target variable as we did in classification and regression.

  Instead of telling the machine Predict Y for our data X> we're asking What can you tell me about X?

  Things we ask the machine to tell us about X may be What are the six best groups we can make out of X? or What three features occur together most frequently in X?

• Question 107:

  Of all the smokers in a particular district, 40% prefer brand A and 60% prefer brand B. Of those smokers who prefer brand A. 30% are females, and of those who prefer brand B. 40% are female. What is the probability that a randomly selected smoker prefers brand A, given that the person selected is a female?

  Which of the following is a best way to solve this problem?

  A. Bays Theorem

  B. Poisson Distribution

  C. Binomial Distribution

  D. None of the above

  Correct Answer: A

• Question 108:

  You are building a classifier off of a very high-dimensiona data set similar to shown in the image with 5000 variables (lots of columns, not that many rows). It can handle both dense and sparse input. Which technique is most suitable, and why?

  

  A. Logistic regression with L1 regularization, to prevent overfitting

  B. Naive Bayes, because Bayesian methods act as regularlizers

  C. k-nearest neighbors, because it uses local neighborhoods to classify examples

  D. Random forest because it is an ensemble method

  Correct Answer: A

  Explanation: Logistic regression is widely used in machine learning for classification problems. It is well-known that regularization is required to avoid over-fitting, especially when there is a only small number of training examples, or when there are a large number of parameters to be learned. In particular L1 regularized logistic regression is often used for feature selection, and has been shown to have good generalization performance in the presence of many irrelevant features. (Ng 2004; Goodman 2004) Unregularized logistic regression is an unconstrained convex optimization problem with a continuously differentiate objective function. As a consequence, it can be solved fairly efficiently with standard convex optimization methods, such as Newton's method or conjugate gradient. However, adding the L1 regularization makes the optimization problem com-putationally more expensive to solve. If the L1 regulariza-tion is enforced by an L1 norm constraint on the parameLogistic regression is a classifier and L1 regularization tends to produce models that ignore dimensions of the input that are not predictive. This is particularly useful when the input contains many dimensions, k-nearest neighbors classification is also a classification technique, but relies on notions of distance. In a high- dimensional space, most every data point is "far" from others (the curse of dimensionality) and so these techniques break down. Naive Bayes is not inherently regularizing. Random forests represent an ensemble method; but an ensemble method is not necessarily more suitable to high-dimensional data. Practically, I think the biggest reasons for regularization are 1) to avoid overfitting by not generating high coefficients for predictors that are sparse. 2) to stabilize the estimates especially when there's collinearity in the data.

  1) is inherent in the regularization framework. Since there are two forces pulling each other in the objective function, if there's no meaningful loss reduction, the increased penalty from the regularization term wouldn't improve the overall objective function. This is a great property since a lot of noise would be automatically filtered out from the model. To give you an example for 2), if you have two predictors that have same values, if you just run a regression algorithm on it since the data matrix is singular your beta coefficients will be Inf if you try to do a straight matrix inversion. But if you add a very small regularization lambda to it, you will get stable beta coefficients with the coefficient values evenly divided between the equivalent two variables. For the difference between L1 and L2, the following graph demonstrates why people bother to have L1 since L2 has such an elegant analytical solution and is so computationally straightforward. Regularized regression can also be represented as a constrained regression problem (since they are Lagrangian equivalent). The implication of this is that the L1 regularization gives you sparse estimates. Namely, in a high dimensional space, you got mostly zeros and a small number of non-zero coefficients. This is huge since it incorporates variable selection to the modeling problem. In addition, if you have to score a large sample with your model, you can have a lot of computational savings since you don't have to compute features(predictors) whose coefficient is 0. I personally think L1 regularization is one of the most beautiful things in machine learning and convex optimization. It is indeed widely used in bioinformatics and large scale machine learning for companies like Facebook, Yahoo, Google and Microsoft.

• Question 109:

  If you are trying to predict or forecast a discrete target value, then which is the correct options?

  A. Supervised Learning regression algorithms

  B. Supervised Learning classification algorithms

  C. Un supervised Learning

  D. Density estimation algorithm

  Correct Answer: B

  Explanation: If you're trying to predict or forecast a target value, then you need to look into supervised learning. If not, then unsupervised learning is the place you want to be. If you've chosen supervised learning, what's your target value? Is it

  a discrete value like Yes/No:

  1/2/3, A/B/C: or Red/Yellow/Black? If so: then you want to look into classification. If the target value can take on a number of values, say any value from 0.00 to 100.00: or-999 to 999, or +_to -_, then you need to look into regression.

• Question 110:

  Select the correct algorithm of unsupervised algorithm

  A. K-Nearest Neighbors

  B. K-Means

  C. Support Vector Machines

  D. Naive Bayes

  Correct Answer: A

  Explanation: Sup Supervised learning tasks Classification Regression k-Nearest Neighbors Linear Naive Bayes Locally weighted linear Support vector machines Ridge Decision trees Lasso Unsupervised learning tasks Clustering Density estimation k-Means Expectation maximization DBSCAN Parzen window