Databricks-Certified-Professional-Data-Scientist practice materials Questions Answers

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Databricks-Certified-Professional-Data-Scientist
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Databricks Certified Professional Data Scientist Exam
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Jun 01,2026

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Page: 1 / 28
Total Questions: 138

Questions & Answers PDF

• Question 1

  

  The figure below shows a plot of the data of a data matrix M that is 1000 x 2. Which line represents the first principal component?

  • yellow
    
  • blue
    
  • Neither
    Principal component analysis (PCA) involves a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated 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.
    The first principal component corresponds to the greatest variance in the data. The blue line is evidently this first principal component, because if we project the data onto the blue line, the data is more spread out (higher variance) than if projected
    onto any other line, including the yellow one.
    

  Reveal Answer Answer: 2 Next Question

• Question 2

  

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

  • What all are the data sources for the project?
    
  • Analyze the Raw data and its format and structure.
    
  • What all tools are required, in the project?
    
  • What is the network capacity required
    
  • What Unix server capacity required?
    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?
    

  Reveal Answer Answer: 1,,2,,3,,4,,5 Next Question

• Question 3

  

  You are working in an ecommerce organization, where you are designing and evaluating a recommender system, you need to select which of the following metric wilt always have the largest value?

  • Root Mean Square Error
    
  • Sum of Errors
    
  • Mean Absolute Error
    
  • Both land 2
    
  • Information is not good enough.
    

  Reveal Answer Answer: 5 Next Question

• Question 4

  

  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?

  • Logistic Regression
    
  • Decision Trees
    
  • Linear Regression
    
  • ARIMA
    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.
    

  Reveal Answer Answer: 2 Next Question

• Question 5

  

  Select the correct problems which can be solved using SVMs

  • SVMs are helpful in text and hypertext categorization
    
  • Classification of images can also be performed using SVMs
    
  • SVMs are also useful in medical science to classify proteins with up to 90% of the compounds classified correctly
    
  • Hand-written characters can be recognized using SVM
    SVMs can be used to solve various real world problems:
    * SVMs are helpful in text and hypertext categorization as their application can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings.
    * Classification of images can also be performed using SVMs. Experimental results show that SVMs achieve significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.
    * SVMs are also useful in medical science to classify proteins with up to 90% of the compounds classified correctly.
    * Hand-written characters can be recognized using SVM
    

  Reveal Answer Answer: 1,,2,,3,,4 Next Question

• Question 6

  

  What describes a true property of Logistic Regression method?

  • It handles missing values well.
    
  • It works well with discrete variables that have many distinct values.
    
  • It is robust with redundant variables and correlated variables.
    
  • It works well with variables that affect the outcome in a discontinuous way.
    

  Reveal Answer Answer: 3 Next Question

• Question 7

  

  Question-26. There are 5000 different color balls, out of which 1200 are pink color. What is the maximum likelihood estimate for the proportion of 'pink' items in the test set of color balls?

  • 2.4
    
  • 24 0
    
  • .24
    
  • .48
    
  • 4.8
    

  Reveal Answer Answer: 3 Next Question

• Question 8

  

  Which of the following true with regards to the K-Means clustering algorithm?

  • Labels are not pre-assigned to each objects in the cluster.
    
  • Labels are pre-assigned to each objects in the cluster.
    
  • It classify the data based on the labels.
    
  • It discovers the center of each cluster.
    
  • It find each objects fall in which particular cluster
    Clustering does not require any predefined labels on the object, rather it consider the attributes on the object. Hence, option-B is out. Clustering is different than classification technique.
    Hence you can discard the option-C as well. It does not use the pre-defined labels, hence it is called unsupervised learning and option-Ais correct. Main purpose of the Clustering technique is to determine the center of each Cluster and then find the distance from that center. If object is near the center than it would fall in that particular cluster. Hence, finally you will have group or clusters created and get to know that objects fall in which particular cluster.
    

  Reveal Answer Answer: 1,,4,,5 Next Question

• Question 9

  

  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?

  • Naive Bayes
    
  • Logistic Regression
    
  • Random Decision Forests
    
  • Any one of the above
    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
    

  Reveal Answer Answer: 4 Next Question

• Question 10

  

  Google Adwords studies the number of men, and women, clicking the advertisement on searchengine during the midnight for an hour each day.Google find that the number of men that click can be modeled as a random variable with distributionPoisson(X), and likewise the number of women that click as Poisson(Y).What is likely to be the best model of the total number of advertisement clicks during the midnight for an hour ?

  • Binomial(X+Y,X+Y)
    
  • Poisson(X/Y)
    
  • Normal(X+Y(M+Y)1/2)
    
  • Poisson(X+Y)
    The total number of clicks is the sum of the number of men and
    women. The sum of two Poisson random variables also follows a Poisson distribution with
    rate equal to the sum of their rates.
    The Normal and Binomial distribution can approximate the Poisson distribution in
    certain cases, but the expressions above do not approximate Poisson(X+Y).
    

  Reveal Answer Answer: 4 Next Question

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Total Questions: 138

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