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

• Question 1:

  Which of the following is not a correct application for the Classification?

  A. credit scoring

  B. tumor detection

  C. image recognition

  D. drug discovery

  Correct Answer: D

  Explanation: Classification : Build models to classify data into different categories credit scoring, tumor detection, image recognition Regression: Build models to predict continuous data, electricity load forecasting, algorithmic trading, drug discovery

• Question 2:

  Select the correct statement which applies to Principal component analysis (PCA):

  A. Is a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables.

  B. Is a mathematical procedure that transforms a number of (possibly) correlated variables into a (higher) number of uncorrelated variables

  C. Increase the dimensionality of the data set.

  D. 1 and 3 are correct

  E. 1 and 2 are correct

  Correct Answer: A

  Explanation: Principal component analysis (PCA) involves a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrected variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible.

• Question 3:

  You are working as a data science consultant for a gaming company. You have three member team and all other stake holders are from the company itself like project managers and project sponsored, data team etc. During the discussion project managed asked you that when can you tell me that the model you are using is robust enough, after which step you can consider answer for this question?

  A. Data Preparation

  B. Discovery

  C. Operationalize

  D. Model planning

  E. Model building

  Correct Answer: E

  To answer whether the model you are building is robust enough or not you need to have answer below questions at least

  -Model is performing as expected with the test data or not?

  -Whatever hypothesis defined in the initial phase is being tested or not?

  -Do we need more data?

  - Domain experts are convinced or not with the model? And all these can be answered when you have built the model and tested with the test data sets. Hence, correct option will be Model Building.

• Question 4:

  Support vector machines (SVMs) are a set of supervised learning methods used for:

  A. Linear classification

  B. Non-linear classification

  C. Regression

  Correct Answer: ABC

  Explanation: In machine learning, support vector machines (SVMs). also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns^ used for classification and regression analysis. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel tricky implicitly mapping their inputs into high- dimensional feature spaces.

• Question 5:

  In which of the following scenario you should apply the Bay's Theorem?

  A. The sample space is partitioned into a set of mutually exclusive events {A1, A2, . .., An }.

  B. Within the sample space, there exists an event B, for which P(B) > 0.

  C. The analytical goal is to compute a conditional probability of the form: P(Ak | B ).

  D. In all above cases

  Correct Answer: D

• Question 6:

  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?

  A. Naive Bayes

  B. Logistic Regression

  C. Random Decision Forests

  D. Any one of the above

  Correct Answer: D

  Explanation: In this problem you have been given high-dimensional independent variables like texts, corrections, test results etc. and you have to predict either valid or not valid (One of two). So all of the below technique can be applied to this problem. Support vector machines Naive Bayes Logistic regression Random decision forests

• Question 7:

  Which of the following steps you will be using in the discovery phase?

  A. What all are the data sources for the project?

  B. Analyze the Raw data and its format and structure.

  C. What all tools are required, in the project?

  D. What is the network capacity required

  E. What Unix server capacity required?

  Correct Answer: ABCDE

  Explanation: During the discovery phase you need to find how much resources are required as early as possible and for that even you can involve various stakeholders like Software engineering team, DBAs,

  Network engineers, System administrators etc. for your requirement and these resources are already available or you need to procure them. Also, what would be source of the data? What all tools and software's are required to execute the

  same?

• Question 8:

  Select the correct statement which applies to logistic regression:

  A. Computationally inexpensive, easy to implement knowledge representation easy to interpret

  B. May have low accuracy

  C. Works with Numeric values

  Correct Answer: ABC

• Question 9:

  Classification and regression are examples of___________.

  A. supervised learning

  B. un-supervised learning

  C. Clustering

  D. Density estimation

  Correct Answer: A

  Explanation: In classification, our job is to predict what class an instance of data should fall into. Another task in machine learning is regression. Regression is the prediction of a numeric value. Most people have probably seen an example of regression with a best-fit line drawn through some data points to generalize the data points. Classification and regression are examples of supervised learning. This set of problems is known as supervised because we're telling the algorithm what to predict.

• Question 10:

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

  

  A. Apriori Algorithm

  B. Ridge and Lasso

  C. Ordinary Least squares

  D. Integer programming

  Correct Answer: C

  Explanation: : RY = b0 + b1x1+b2x2+ .... +bnxn In the linear model, the bi's represent the unknown p parameters. The estimates for these unknown parameters are chosen so that, on average, the model provides a reasonable estimate of a person's income based on age and education. In other words, the fitted model should minimize the overall error between the linear model and the actual observations. Ordinary Least Squares (OLS) is a common technique to estimate the parameters