Updated PDF (New 2022) Actual Databricks Databricks-Certified-Professional-Data-Scientist Exam Questions
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NEW QUESTION 71
You are using one approach for the classification where to teach the agent not by giving explicit categorizations, but by using some sort of reward system to indicate success, where agents might be rewarded for doing certain actions and punished for doing others. Which kind of this learning
- A. None of the above
- B. Unsupervised
- C. Supervised
- D. Regression
Answer: B
Explanation:
Explanation
Unsupervised learning seems much harder: the goal is to have the computer learn how to do something that we don't tell it how to do! The approach is to teach the agent not by giving explicit categorizations, but by using some sort of reward system to indicate success. Note that this type of training will generally fit into the decision problem framework because the goal is not to produce a classification but to make decisions that maximize rewards. This approach nicely generalizes to the real world, where agents might be rewarded for doing certain actions and punished fordoing others.
NEW QUESTION 72
You are studying the behavior of a population, and you are provided with multidimensional data at the individual level. You have identified four specific individuals who are valuable to your study, and would like to find all users who are most similar to each individual. Which algorithm is the most appropriate for this study?
- A. Linear regression
- B. Association rules
- C. Decision trees
- D. K-means clustering
Answer: D
Explanation:
Explanation
kmeans uses an iterative algorithm that minimizes the sum of distances from each object to its cluster centroid, over all clusters. This algorithm moves objects between clusters until the sum cannot be decreased further. The result is a set of clusters that are as compact and well-separated as possible. You can control the details of the minimization using several optional input parameters to kmeans, including ones for the initial values of the cluster centroids, and for the maximum number of iterations.
Clustering is primarily an exploratory technique to discover hidden structures of the data: possibly as a prelude to more focused analysis or decision processes. Some specific applications of k-means are image processing^ medical and customer segmentation. Clustering is often used as a lead-in to classification. Once the clusters are identified, labels can be applied to each cluster to classify each group based on its characteristics. Marketing and sales groups use k-means to better identify customers who have similar behaviors and spending patterns.
NEW QUESTION 73
Suppose you have been given a relatively high-dimension set of independent variables and you are asked to come up with a model that predicts one of Two possible outcomes like "YES" or "NO", then which of the following technique best fit.
- A. Logistic regression
- B. Support vector machines
- C. All of the above
- D. Random decision forests
- E. Naive Bayes
Answer: C
Explanation:
Explanation
In this problem you have been given high-dimensional independent variables like yeS; nO; no English words , 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
NEW QUESTION 74
Which of the following statement true with regards to Linear Regression Model?
- A. Ordinary Least Square is a sum of the squared individual distance between each point and the fitted line of regression 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 can be used to estimates the parameters in linear model
- D. Ordinary Least Square is a sum of the individual distance between each point and the fitted line of regression model.
Answer: A,C
Explanation:
Explanation
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.
NEW QUESTION 75
Select the correct problems which can be solved using SVMs
- A. Classification of images can also be performed using SVMs
- B. SVMs are also useful in medical science to classify proteins with up to 90% of the compounds classified correctly
- C. SVMs are helpful in text and hypertext categorization
- D. Hand-written characters can be recognized using SVM
Answer: A,B,C,D
Explanation:
Explanation
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
NEW QUESTION 76
Which of the following are advantages of the Support Vector machines?
- A. Number of features is much greater than the number of samples, the method still give good performances
- B. Effective in high dimensional spaces.
- C. it is memory efficient
- D. possible to specify custom kernels
- E. Effective in cases where number of dimensions is greater than the number of samples
- F. SVMs directly provide probability estimates
Answer: B,C,D,E
Explanation:
Explanation
Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection.
The advantages of support vector machines are:
Effective in high dimensional spaces.
Still effective in cases where number of dimensions is greater than the number of samples.
Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.
Versatile: different Kernel functions can be specified for the decision function.
Common kernels are provided, but it is also possible to specify custom kernels.
The disadvantages of support vector machines include:
If the number of features is much greater than the number of samples, the method is likely to give poor performances.
SVMs do not directly provide probability estimates, these are calculated using an expensive five-fold cross-validation.
NEW QUESTION 77
A website is opened 3 times by a user. What is the probability of he clicks 2 times the advertisement, is best calculated by
- A. Normal
- B. Binomial
- C. Poisson
- D. Any of the above
Answer: B
Explanation:
Explanation
In a binomial distribution, only 2 parameters, namely n and p, are needed to determine the probability. Where p is the probability of success and q is the probability of failure in a binomial trial, then the expected number of successes in n trials.
This is a binomial distribution because there are only 2 possible outcomes (we get a 5 or we don't).
NEW QUESTION 78
Which of the following could be features?
- A. 0nly 1 and 2
- B. All 1,2 and 3 are possible
- C. Words in the document
- D. Characteristics of an unidentified object
- E. Symptoms of a diseases
Answer: B
Explanation:
Explanation
Any dataset that can be turned into lists of features. A feature is simply something that is either present or absent for a given item. In the case of documents, the features are the words in the document but they could also be characteristics of an unidentified object symptoms of a disease, or anything else that can be said to be present of absent.
NEW QUESTION 79
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?
- A. Linear regression
- B. Logistic regression
- C. Decision trees
- D. TF-IDF
Answer: A
Explanation:
Explanation : A data model explicitly describes a relationship between predictor and response variables.
Linear regression fits a data model that is linear in the model coefficients. The most common type of linear regression is a least-squares fit, which can fit both lines and polynomials, among other linear models.
Before you model the relationship between pairs of quantities, it is a good idea to perform correlation analysis to establish if a linear relationship exists between these quantities. Be aware that variables can have nonlinear relationships, which correlation analysis cannot detect. For more information, see Linear Correlation.
If you need to fit data with a nonlinear model, transform the variables to make the relationship linear.
Alternatively try to fit a nonlinear function directly using either the Statistics and Machine Learning Toolbox nlinfit function, the Optimization Toolbox Isqcurvefit function, or by applying functions in the Curve Fitting Toolbox.
79
NEW QUESTION 80
Suppose A, B , and C are events. The probability of A given B , relative to P(|C), is the same as the probability of A given B and C (relative to P ). That is,
- A. P(A,B|C) P(B|C) =P(A|B,C)
- B. P(A,B|C) P(B|C) =P(C|B,C)
- C. P(A,B|C) P(B|C) =P(B|A,C)
- D. P(A,B|C) P(B|C) =P(A|C,B)
Answer: A
Explanation:
Explanation
From the definition, P(A,B|C) P(B|C) =P(A,B.C)/P(C) P(B.C)/P(C) =P(A,B.C) P(B,C) =P(A|BC) This follows from the definition of conditional probability, applied twice: P(A,B)=(PA|B)P(B)
NEW QUESTION 81
What type of output generated in case of linear regression?
- A. Continuous variable
- B. Values between 0 and 1
- C. Any of the Continuous and Discrete variable
- D. Discrete Variable
Answer: A
Explanation:
Explanation
Linear regression model generate continuous output variable.
NEW QUESTION 82
Which of the below best describe the Principal component analysis
- A. Clustering
- B. Dimensionality reduction
- C. Regression
- D. Collaborative filtering
- E. Classification
Answer: B
NEW QUESTION 83
You are working in a classification model for a book, written by HadoopExam Learning Resources and decided to use building a text classification model for determining whether this book is for Hadoop or Cloud computing. You have to select the proper features (feature selection) hence, to cut down on the size of the feature space, you will use the mutual information of each word with the label of hadoop or cloud to select the 1000 best features to use as input to a Naive Bayes model. When you compare the performance of a model built with the 250 best features to a model built with the 1000 best features, you notice that the model with only 250 features performs slightly better on our test data.
What would help you choose better features for your model?
- A. Evaluate a model that only includes the top 100 words
- B. Decrease the size of our training data
- C. Include least mutual information with other selected features as a feature selection criterion
- D. Include the number of times each of the words appears in the book in your model
Answer: C
Explanation:
Explanation
Correlation measures the linear relationship (Pearson's correlation) or monotonic relationship (Spearman's correlation) between two variables, X and Y.
Mutual information is more general and measures the reduction of uncertainty in Y after observing X.
It is the KL distance between the joint density and the product of the individual densities. So Ml can measure non-monotonic relationships and other more complicated relationships Mutual information is a quantification of the dependency between random variables. It is sometimes contrasted with linear correlation since mutual information captures nonlinear dependence.
Features with high mutual information with the predicted value are good. However a feature may have high mutual information because it is highly correlated with another feature that has already been selected.
Choosing another feature with somewhat less mutual information with the predicted value, but low mutual information with other selected features, may be more beneficial. Hence it may help to also prefer features that are less redundant with other selected features.
NEW QUESTION 84
In which of the following scenario you should apply the Bay's Theorem
- A. In all above cases
- B. The sample space is partitioned into a set of mutually exclusive events {A1, A2, . .., An }.
- C. Within the sample space, there exists an event B, for which P(B) > 0.
- D. The analytical goal is to compute a conditional probability of the form: P(Ak | B ).
Answer: A
NEW QUESTION 85
If E1 and E2 are two events, how do you represent the conditional probability given that E2 occurs given that E1 has occurred?
- A. P(E1)/P(E2)
- B. P(E1+E2)/P(E1)
- C. P(E2)/P(E1)
- D. P(E2)/(P(E1+E2)
Answer: C
NEW QUESTION 86
You have modeled the datasets with 5 independent variables called A,B,C,D and E having relationships which is not dependent each other, and also the variable A,B and C are continuous and variable D and E are discrete (mixed mode).
Now you have to compute the expected value of the variable let say A, then which of the following computation you will prefer
- A. Transformation
- B. Integration
- C. Differentiation
- D. Generalization
Answer: B
Explanation:
Explanation
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NEW QUESTION 87
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. None of the above
- B. In both cases, the contribution to the RMSE, could varies
- C. In both cases, the contribution to the RMSE is the different
- D. In both cases, the contribution to the RMSE is the same
Answer: D
NEW QUESTION 88
The method based on principal component analysis (PCA) evaluates the features according to
- A. None of the above
- B. The projection of the smallest eigenvector of the correlation matrix on the initial dimensions
- C. The projection of the largest eigenvector of the correlation matrix on the initial dimensions
- D. According to the magnitude of the components of the discriminate vector
Answer: C
Explanation:
Explanation
Feature Selection:
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 discriminate analysis evaluates. Them according to the magnitude of the components of the discriminate vector.
NEW QUESTION 89
Select the correct statement regarding the naive Bayes classification
- A. only the variances of the variables for each class need to be determined
- B. it only requires a small amount of training data to estimate the parameters
- C. for each class entire covariance matrix need to be determined
- D. Independent variables can be assumed
Answer: A,B,D
Explanation:
Explanation
An advantage of naive Bayes is that it only requires a small amount of training data to estimate the parameters (means and variances of the variables) necessary for classification. Because independent variables are assumed, only the variances of the variables for each class need to be determined and not the entire covariance matrix.
NEW QUESTION 90
Refer to the exhibit.
You are using K-means clustering to classify customer behavior for a large retailer. You need to determine the optimum number of customer groups. You plot the within-sum-of-squares (wss) data as shown in the exhibit.
How many customer groups should you specify?
- A. 0
- B. 1
- C. 2
- D. 3
Answer: A
NEW QUESTION 91
A fruit may be considered to be an apple if it is red, round, and about 3" in diameter. A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of the
- A. None of the above
- B. Absence of the other features.
- C. Presence of the other features.
- D. Presence or absence of the other features
Answer: D
Explanation:
Explanation
In simple terms, a naive Bayes classifier assumes that the value of a particular feature is unrelated to the presence or absence of any other feature, given the class variable. For example, a fruit may be considered to be an apple if it is red, round, and about 3" in diameter A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of the presence or absence of the other features.
NEW QUESTION 92
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