Databricks Databricks-Certified-Professional-Data-Scientist Free Sample Exam Questions 2026

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Total Questions: 138

Questions & Answers PDF

• Question 1

  

  You are working with the Clustering solution of the customer datasets. There are almost 40 variables are available for each customer and almost 1.00,0000 customer's data is available. You want to reduce the number of variables for clustering, what would you do?

  • You will randomly reduce the number of variables
    
  • You will find the correlation among the variables and from their variables are not co-related will be discarded.
    
  • You will find the correlation among the variables and from the highly co-related variables, you will be considering only one or two variables from it.
    
  • You cannot discard any variable for creating clusters.
    
  • You can combine several variables in one variable
    When you are applying clustering technique and you find that there are quite a huge number of variables are available. Then it is better the find the co-relation among the variables and consider only one or two variables from the highly co-related variables. Because highly co-related variable will have the same effect, while creating the cluster. We can use scatter plot matrix among the variables to find the co-relation.
    You can also combine several variables into a single variable. For example if you have two values in the dataset like Asset and Debt than by combining these two values like Debt to Asset ratio and use it while creating the cluster.
    

  Reveal Answer Answer: 3,,5 Next Question

• Question 2

  

  Regularization is a very important technique in machine learning to prevent over fitting. And Optimizing with a L1 regularization term is harder than with an L2 regularization term because

  • The penalty term is not differentiate
    
  • The second derivative is not constant
    
  • The objective function is not convex
    
  • The constraints are quadratic
    Regularization is a very important technique in machine learning to prevent overfitting. Mathematically speaking, it adds a regularization term in order to prevent the coefficients to fit so perfectly to overfit. The difference between the L1 and L2 is just that L2 is the sum of the square of the weights, while L1 is just the sum of the weights.
    Much of optimization theory has historically focused on convex loss functions because they're much easier to optimize than non-convex functions: a convex function over a bounded domain is guaranteed to have a minimum, and it's easy to find that minimum by following the gradient of the function at each point no matter where you start. For non-convex functions, on the other hand, where you start matters a great deal; if you start in a bad position and follow the gradient, you're likely to end up in a local minimum that is not necessarily equal to the global minimum.
    You can think of convex functions as cereal bowls: anywhere you start in the cereal bowl, you're likely to roll down to the bottom. A non-convex function is more like a skate park: lots of ramps, dips, ups and downs. It's a lot harder to find the lowest point in a skate park than it is a cereal bowl.
    

  Reveal Answer Answer: 1 Next Question

• Question 3

  

  Select the correct statement which applies to logistic regression

  • Computationally inexpensive, easy to implement knowledge representation easy to interpret
    
  • May have low accuracy
    
  • Works with Numeric values
    

  Reveal Answer Answer: 1,,2,,3 Next Question

• Question 4

  

  You are asked to create a model to predict the total number of monthly subscribers for a specific magazine. You are provided with 1 year's worth of subscription and payment data, user demographic data, and 10 years worth of content of the magazine (articles and pictures). Which algorithm is the most appropriate for building a predictive model for subscribers?

  • Linear regression
    
  • Logistic regression
    
  • Decision trees
    
  • TF-IDF
    

  Reveal Answer Answer: 1 Next Question

• Question 5

  

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

  • first principal component
    
  • first eigenvector
    
  • not enough information given to answer
    
  • second eigenvector
    
  • second principal component
    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.
    

  Reveal Answer Answer: 1 Next Question

• Question 6

  

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

  • Linear classification
    
  • Non-linear classification
    
  • Regression
    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.
    

  Reveal Answer Answer: 1,,2,,3 Next Question

• Question 7

  

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

  • Nominal values like true, false
    
  • Reptile, fish, mammal, amphibian, plant, fungi
    
  • Infinite number of numeric values, such as 0.100, 42.001, 1000.743..
    
  • All of the above
    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.
    

  Reveal Answer Answer: 4 Next Question

• Question 8

  

  Scenario: Suppose that Bob can decide to go to work by one of three modes of transportation,car, bus, or commuter train. Because of high traffic, if he decides to go by car. there is a 50% chance he will be late. If he goes by bus, which has special reserved lanes but is sometimes overcrowded, the probability of being late is only 20%. The commuter train is almost never late, with a probability of only 1 %, but is more expensive than the bus.Suppose that Bob is late one day, and his boss wishes to estimate the probability that he drove to work that day by car. Since he does not know Which mode of transportation Bob usually uses, he gives a prior probability of 1 3 to each of the three possibilities. Which of the following method the boss will use to estimate of the probability that Bob drove to work?

  • Naive Bayes
    
  • Linear regression
    
  • Random decision forests
    
  • None of the above
    Bayes' theorem (also known as Bayes' rule) is a useful tool for calculating conditional probabilities.
    

  Reveal Answer Answer: 1 Next Question

• Question 9

  

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

  • credit scoring
    
  • tumor detection
    
  • image recognition
    
  • drug discovery
    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
    

  Reveal Answer Answer: 4 Next Question

• Question 10

  

  A bio-scientist is working on the analysis of the cancer cells. To identify whether the cell is cancerous or not, there has been hundreds of tests are done with small variations to say yes to the problem. Given the test result for a sample of healthy and cancerous cells, which of the following technique you will use to determine whether a cell is healthy?

  • Linear regression
    
  • Collaborative filtering
    
  • Naive Bayes
    
  • Identification Test
    In this problem you have been given high-dimensional independent variables like yes, no: 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
    

  Reveal Answer Answer: 3 Next Question

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Total Questions: 138

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