[2023] Get Top-Rated Databricks Databricks-Certified-Professional-Data-Scientist Exam Dumps Now [Q81-Q100]

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[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 81
Find out the classifier which assumes independence among all its features?

Neural networks

Linear Regression

Naive Bayes

Random forests

NEW QUESTION 82
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

NEW QUESTION 83
Select the correct algorithm of unsupervised algorithm

K-Nearest Neighbors

K-Means

Support Vector Machines

Naive Bayes

NEW QUESTION 84
Which is an example of supervised learning?

PCA

k-means clustering

SVD

SVM

NEW QUESTION 85
Question-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

NEW QUESTION 86
You 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

NEW QUESTION 87
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

Integration

Differentiation

Transformation

Generalization

NEW QUESTION 88
You 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

NEW QUESTION 89
You 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

Explanation
Logistic 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 90
Select 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

NEW QUESTION 91
A 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

NEW QUESTION 92
Which 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?

NEW QUESTION 93
In 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

NEW QUESTION 94
Select 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

NEW QUESTION 95
Which 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

NEW QUESTION 96
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

Supervised

Unsupervised

Regression

None of the above

NEW QUESTION 97
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

NEW QUESTION 98
Refer 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 99
Which of the following are point estimation methods?

MAP

MLE

MMSE

NEW QUESTION 100
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?

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

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