Databricks DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Online Practice Questions and Exam Preparation

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

• Question 1:

  Select the correct problems which can be solved using SVMs:

  A. SVMs are helpful in text and hypertext categorization
  B. Classification of images can also be performed using SVMs
  C. SVMs are also useful in medical science to classify proteins with up to 90% of the compounds classified correctly
  D. Hand-written characters can be recognized using SVM
  A. SVMs are helpful in text and hypertext categorization
  B. Classification of images can also be performed using SVMs
  C. SVMs are also useful in medical science to classify proteins with up to 90% of the compounds classified correctly
  D. Hand-written characters can be recognized using SVM

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  Explanation/Reference:

  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

• Question 2:

  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
  E. Model building

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  Explanation/Reference:

  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 3:

  In which of the following scenario we can use naTve Bayes theorem for classification

  A. Classify whether a given person is a male or a female based on the measured features. The features include height, weight and foot size.
  B. To classify whether an email is spam or not spam
  C. To identify whether a fruit is an orange or not based on features like diameter, color and shape
  A. Classify whether a given person is a male or a female based on the measured features. The features include height, weight and foot size.
  B. To classify whether an email is spam or not spam
  C. To identify whether a fruit is an orange or not based on features like diameter, color and shape

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  Explanation/Reference:

  naive Bayes classifiers have worked quite well in many real-world situations, famously document classification and spam filtering. They requires a small amount of training data to estimate the necessary parameters

• Question 4:

  Your customer provided you with 2. 000 unlabeled records three groups. What is the correct analytical method to use?

  A. Semi Linear Regression
  B. Logistic regression
  C. Naive Bayesian classification
  D. Linear regression
  E. K-means clustering
  E. K-means clustering

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  Explanation/Reference:

  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.

  The algorithm has nothing to do with and should not be confused with k-nearest neighbor another popular machine learning technique.

• Question 5:

  What are the advantages of the Hashing Features?

  A. Requires the less memory
  B. Less pass through the training data
  C. Easily reverse engineer vectors to determine which original feature mapped to a vector location
  A. Requires the less memory
  B. Less pass through the training data

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  Explanation/Reference:

  SGD-based classifiers avoid the need to predetermine vector size by simply picking a reasonable size and shoehorning the training data into vectors of that size. This approach is known as feature hashing. The shoehorning is

  done by picking one or more locations by using a hash of the name of the variable for continuous variables or a hash of the variable name and the category name or word for categorical, text-like, or word-like data.

  This hashed feature approach has the distinct advantage of requiring less memory and one less pass through the training data, but it can make it much harder to reverse engineer vectors to determine which original feature mapped to a vector

  location. This is because multiple features may hash to the same location. With large vectors or with multiple locations per feature, this isn't a problem for accuracy but it can make it hard to understand what a classifier is doing.

  An additional benefit of feature hashing is that the unknown and unbounded vocabularies typical of word-like variables aren't a problem.

• Question 6:

  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
  D. Option D

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  Explanation/Reference:

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

• Question 7:

  What describes a true limitation of Logistic Regression method?

  A. It does not handle redundant variables well.
  B. It does not handle missing values well.
  C. It does not handle correlated variables well.
  D. It does not have explanatory values.
  B. It does not handle missing values well.

• Question 8:

  Let's say you have two cases as below for the movie ratings

  1.

  You recommend to a user a movie with four stars and he really doesn't like it and he'd rate it two stars

  2.

  You recommend a movie with three stars but the user loves it (he'd rate it five stars). So which statement correctly applies?

  A. In both cases, the contribution to the RMSE is the same
  B. In both cases, the contribution to the RMSE is the different
  C. In both cases, the contribution to the RMSE, could varies
  D. None of the above
  A. In both cases, the contribution to the RMSE is the same

• Question 9:

  Which of the following metrics are useful in measuring the accuracy and quality of a recommender system?

  A. Cluster Density
  B. Support Vector Count
  C. Mean Absolute Error
  D. Sum of Absolute Errors
  C. Mean Absolute Error

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  Explanation/Reference:

  The MAE measures the average magnitude of the errors in a set of forecasts, without considering their direction. It measures accuracy for continuous variables. The equation is given in the library references. Expressed in words,

  the MAE is the average over the verification sample of the absolute values of the differences between forecast and the corresponding observation. The MAE is a linear score which means that all the individual differences are weighted equally

  in the average. The sum of absolute errors is a valid metric, but doesn't give any useful sense of how the recommender system is performing.

  Support vector count and cluster density do not apply to recommender systems. MAE and AUC are both valid and useful metrics for measuring recommender systems.

• Question 10:

  Which of the following statement true with regards to Linear Regression Model?

  A. Ordinary Least Square can be used to estimates the parameters in linear model
  B. In Linear model, it tries to find multiple lines which can approximate the relationship between the outcome and input variables.
  C. Ordinary Least Square is a sum of the individual distance between each point and the fitted line of regression model.
  D. Ordinary Least Square is a sum of the squared individual distance between each point and the fitted line of regression model.
  A. Ordinary Least Square can be used to estimates the parameters in linear model
  D. Ordinary Least Square is a sum of the squared individual distance between each point and the fitted line of regression model.

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  Explanation/Reference:

  Linear regression model are represented using the below equation

  

  Where B(0) is intercept and B(1) is a slope. As B(0) and B(1) changes then fitted line also shifts accordingly on the plot. The purpose of the Ordinary Least Square method is to estimates these parameters B(0) and B(1). And similarly it is a sum of squared distance between the observed point and the fitted line. Ordinary least squares (OLS) regression minimizes the sum of the squared residuals. A model fits the data well if the differences between the observed values and the model's predicted values are small and unbiased.