This page was exported from Actual Test Materials [ http://blog.actualtests4sure.com ] Export date:Fri Nov 15 18:35:49 2024 / +0000 GMT ___________________________________________________ Title: [2023] Get Top-Rated Databricks Databricks-Certified-Professional-Data-Scientist Exam Dumps Now [Q81-Q100] --------------------------------------------------- [2023] Get Top-Rated Databricks Databricks-Certified-Professional-Data-Scientist Exam Dumps Now Passing Key To Getting Databricks-Certified-Professional-Data-Scientist Certified Exam Engine PDF The Databricks Certified Professional Data Scientist exam is an important certification for data professionals who want to validate their expertise in working with data science platforms. With this certification, data professionals can demonstrate their skills and knowledge in this critical area, and they can advance their careers in the field of data science.   NEW QUESTION 81Find out the classifier which assumes independence among all its features?  Neural networks  Linear Regression  Naive Bayes  Random forests ExplanationA Bayes classifier is a simple probabilistic classifier based on applying Bayes’ theorem (from Bayesian statistics) with strong (naive) independence assumptions. A more descriptive term for the underlying probability model would be “independent feature model”.A Bayes classifier is a simple probabilistic classifier based on applying Bayes’ theorem (from Bayesian statistics) with strong (naive) independence assumptions. A more descriptive term for the underlying probability model would be “independent feature model”.In simple terms, a naive Bayes classifier assumes that the presence (or absence) of a particular feature of a class is unrelated to the presence (or absence) of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 4″ in diameter Even if these features depend on each other or upon the existence of the other features, a naive Bayes classifier considers all of these properties to independently contribute to the probability that this fruit is an apple.NEW QUESTION 82Scenario: 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 of1 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 ExplanationBayes’ theorem (also known as Bayes’ rule) is a useful tool for calculating conditional probabilities.NEW QUESTION 83Select the correct algorithm of unsupervised algorithm  K-Nearest Neighbors  K-Means  Support Vector Machines  Naive Bayes ExplanationSup Supervised learning tasksClassification Regressionk-Nearest Neighbors LinearNaive Bayes Locally weighted linearSupport vector machines RidgeDecision trees LassoUnsupervised learning tasks Clustering Density estimation k-Means Expectation maximization DBSCAN Parzen windowNEW QUESTION 84Which is an example of supervised learning?  PCA  k-means clustering  SVD  EM  SVM ExplanationSVMs 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 SVMNEW QUESTION 85Question-3: In machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel trick), is a fast and space-efficient way of vectorizing features (such as the words in a language), i.e., turning arbitrary features into indices in a vector or matrix. It works by applying a hash function to the features and using their hash values modulo the number of features as indices directly, rather than looking the indices up in an associative array. So what is the primary reason of the hashing trick for building classifiers?  It creates the smaller models  It requires the lesser memory to store the coefficients for the model  It reduces the non-significant features e.g. punctuations  Noisy features are removed ExplanationThis 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.Models always have a coefficient per feature, which are stored in memory during model building. The hashing trick collapses a high number of features to a small number which reduces the number of coefficients and thus memory requirements. Noisy features are not removed; they are combined with other features and so still have an impact.The validity of this approach depends a lot on the nature of the features and problem domain; knowledge of the domain is important to understand whether it is applicable or will likely produce poor results. While hashing features may produce a smaller model, it will be one built from odd combinations of real-world features, and so will be harder to interpret.An additional benefit of feature hashing is that the unknown and unbounded vocabularies typical of word-like variables aren’t a problem.NEW QUESTION 86You are working in a data analytics company as a data scientist, you have been given a set of various types of Pizzas available across various premium food centers in a country. This data is given as numeric values like Calorie. Size, and Sale per day etc. You need to group all the pizzas with the similar properties, which of the following technique you would be using for that?  Association Rules  Naive Bayes Classifier  K-means Clustering  Linear Regression  Grouping ExplanationUsing K means clustering you can create group of objects based on their properties. Where K is number of the groups. In this case, in each group you determine the center of the group and then find the how far each object characteristics from the center. If it is near the center than it can be part of the group. Suppose we have 100 objects and we need to determine 4 groups. Hence, here K=4. Now we determine 4 center values and based on that center value we determine the distance of each object from the center.NEW QUESTION 87You 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  Integration  Differentiation  Transformation  Generalization ExplanationText Description automatically generatedText Description automatically generatedText Description automatically generatedNEW QUESTION 88You are having 1000 patients’ data with the height and age. Where age in years and height in meters. You wanted to create cluster using this two attributes. You wanted to have near equal effect for both the age and height while creating the cluster. What you can do?  You will be adding height with the numeric value 100  You will be converting each height value to centimeters  You will be dividing both age and height with their respective standard deviation  You will be taking square root of height ExplanationWhen you see the data age in years would have values like 50, 60r 70 90 years etc. And while calculating distance from centroid maximum possible value can be 90-0 and its square will be 8100.While using heights in meter can be 2-0.5(1.5) meters and its square will be 2.25 only. So you can see age has more effect than height. Hence bringing the height on same level you can convert it into centimeters. Can bring data upto 200 centimeters and then it be more effective like square of 200 maximum.However there is another approach is to divide the each value with its standard deviation, which will not have impact of the units e.g. age/sd of the age, which results in value without unit. This can also help in reducing the effect of units.NEW QUESTION 89You are building a classifier off of a very high-dimensiona data set similar to shown in the image with 5000 variables (lots of columns, not that many rows). It can handle both dense and sparse input. Which technique is most suitable, and why?  Logistic regression with L1 regularization, to prevent overfitting  Naive Bayes, because Bayesian methods act as regularlizers  k-nearest neighbors, because it uses local neighborhoods to classify examples  Random forest because it is an ensemble method ExplanationLogistic regression is widely used in machine learning for classification problems. It is well-known that regularization is required to avoid over-fitting, especially when there is a only small number of training examples, or when there are a large number of parameters to be learned. In particular L1 regularized logistic regression is often used for feature selection, and has been shown to have good generalization performance in the presence of many irrelevant features. (Ng 2004; Goodman 2004) Unregularized logistic regression is an unconstrained convex optimization problem with a continuously differentiate objective function. As a consequence, it can be solved fairly efficiently with standard convex optimization methods, such as Newton’s method or conjugate gradient. However, adding the L1 regularization makes the optimization problem com-putationally more expensive to solve. If the L1 regulariza-tion is enforced by an L1 norm constraint on the parameLogistic regression is a classifier and L1 regularization tends to produce models that ignore dimensions of the input that are not predictive. This is particularly useful when the input contains many dimensions, k-nearest neighbors classification is also a classification technique, but relies on notions of distance. In a high-dimensional space, most every data point is “far” from others (the curse of dimensionality) and so these techniques break down. Naive Bayes is not inherently regularizing. Random forests represent an ensemble method; but an ensemble method is not necessarily more suitable to high-dimensional data.Practically, I think the biggest reasons for regularization are 1) to avoid overfitting by not generating high coefficients for predictors that are sparse. 2) to stabilize the estimates especially when there’s collinearity in the data.1) is inherent in the regularization framework. Since there are two forces pulling each other in the objective function, if there’s no meaningful loss reduction, the increased penalty from the regularization term wouldn’t improve the overall objective function. This is a great property since a lot of noise would be automatically filtered out from the model. To give you an example for 2), if you have two predictors that have same values, if you just run a regression algorithm on it since the data matrix is singular your beta coefficients will be Inf if you try to do a straight matrix inversion. But if you add a very small regularization lambda to it, you will get stable beta coefficients with the coefficient values evenly divided between the equivalent two variables. For the difference between L1 and L2, the following graph demonstrates why people bother to have L1 since L2 has such an elegant analytical solution and is so computationally straightforward. Regularized regression can also be represented as a constrained regression problem (since they are Lagrangian equivalent). The implication of this is that the L1 regularization gives you sparse estimates. Namely, in a high dimensional space, you got mostly zeros and a small number of non-zero coefficients. This is huge since it incorporates variable selection to the modeling problem. In addition, if you have to score a large sample with your model, you can have a lot of computational savings since you don’t have to compute features(predictors) whose coefficient is 0. I personally think L1 regularization is one of the most beautiful things in machine learning and convex optimization. It is indeed widely used in bioinformatics and large scale machine learning for companies like Facebook, Yahoo, Google and Microsoft.NEW QUESTION 90Select the correct statement regarding the naive Bayes classification  it only requires a small amount of training data to estimate the parameters  Independent variables can be assumed  only the variances of the variables for each class need to be determined  for each class entire covariance matrix need to be determined ExplanationAn 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 91A researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don’t admit, is a binary variable.Above is an example of  Linear Regression  Logistic Regression  Recommendation system  Maximum likelihood estimation  Hierarchical linear models ExplanationLogistic regressionPros: Computationally inexpensive, easy to implement, knowledge representation easy to interpret Cons: Prone to underfitting, may have low accuracy Works with: Numeric values, nominal valuesNEW QUESTION 92Which of the following question statement falls under data science category?  What happened in last six months?  How many products have been sold in a last month?  Where is a problem for sales?  Which is the optimal scenario for selling this product?  What happens, if these scenario continues? ExplanationThis question wants to check your understanding about Bl and Data Science. Bl was already existing and analytics team already using it. They need to improve and learn data science technique to solve some problems. If you check the option given in the question, it will confuse you. But if you have worked in Bl or as a Data Scientist then it is easy to answer. First 3 option can be easily answered using reporting solution, what sales happened in last six month, what was the problem etc.But for the last two option you need to apply data science techniques like which all scenarios are optimal for product sales, you need to collect the data and applying various techniques for that. Hence, last two option can only be answered using Data Science technique And for this you need to apply techniques like Optimization, predictive modeling, statistical analysis on structured and un-structured data.NEW QUESTION 93In which of the following scenario we can use naTve Bayes theorem for classification  Classify whether a given person is a male or a female based on the measured features. The features include height, weight and foot size.  To classify whether an email is spam or not spam  To identify whether a fruit is an orange or not based on features like diameter, color and shape Explanationnaive 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 parametersNEW QUESTION 94Select the correct statement which applies to Principal component analysis (PCA)  Is a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables.  Is a mathematical procedure that transforms a number of (possibly) correlated variables into a (higher) number of uncorrelated variables  Increase the dimensionality of the data set.  1 and 3 are correct  1 and 2 are correct ExplanationPrincipal 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.NEW QUESTION 95Which analytical method is considered unsupervised?may have a trend component that is quadratic in nature. Which pattern of data will indicate that the trend in the time series data is quadratic in nature?  Naive Bayesian classifier  Decision tree  Linear regression  K-means clustering Explanationkmeans 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 96You 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  Supervised  Unsupervised  Regression  None of the above ExplanationUnsupervised 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 97You 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 ExplanationIn 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 forestsNEW QUESTION 98Refer to the exhibit.You are building a decision tree. In this exhibit, four variables are listed with their respective values of info-gain.Based on this information, on which attribute would you expect the next split to be in the decision tree?  Credit Score  Age  Income  Gender NEW QUESTION 99Which of the following are point estimation methods?  MAP  MLE  MMSE ExplanationPoint estimators* minimum-variance mean-unbiased estimator (MVUE), minimizes the risk (expected loss) of the squared-error loss-function.* best linear unbiased estimator (BLUE)* minimum mean squared error (MMSE)* median-unbiased estimator, minimizes the risk of the absolute-error loss function* maximum likelihood (ML)* method of moments, generalized method of momentsNEW QUESTION 100You 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?  Include least mutual information with other selected features as a feature selection criterion  Include the number of times each of the words appears in the book in your model  Decrease the size of our training data  Evaluate a model that only includes the top 100 words ExplanationCorrelation 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. Loading … Databricks-Certified-Professional-Data-Scientist exam questions for practice in 2023 Updated 140 Questions: https://www.actualtests4sure.com/Databricks-Certified-Professional-Data-Scientist-test-questions.html --------------------------------------------------- Images: https://blog.actualtests4sure.com/wp-content/plugins/watu/loading.gif https://blog.actualtests4sure.com/wp-content/plugins/watu/loading.gif --------------------------------------------------- --------------------------------------------------- Post date: 2023-05-19 14:47:08 Post date GMT: 2023-05-19 14:47:08 Post modified date: 2023-05-19 14:47:08 Post modified date GMT: 2023-05-19 14:47:08