[Q56-Q70] 100% Passing Guarantee – Brilliant Databricks-Certified-Professional-Data-Scientist Exam Questions PDF [Nov-2022]

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100% Passing Guarantee – Brilliant Databricks-Certified-Professional-Data-Scientist Exam Questions PDF [Nov-2022]

Databricks-Certified-Professional-Data-Scientist Dumps 2022 – NewDatabricks Databricks-Certified-Professional-Data-Scientist Exam Questions

NO.56 Which technique you would be using to solve the below problem statement? “What is the probability that individual customer will not repay the loan amount?”

Classification

Clustering

Linear Regression

Logistic Regression

Hypothesis testing

NO.57 Regularization is a very important technique in machine learning to prevent overfitting. Mathematically speaking, it adds a regularization term in order to prevent the coefficients to fit so perfectly to overfit. The difference between the L1 and L2 is…

L2 is the sum of the square of the weights, while L1 is just the sum of the weights

L1 is the sum of the square of the weights, while L2 is just the sum of the weights

L1 gives Non-sparse output while L2 gives sparse outputs

None of the above

NO.58 Select the statement which applies correctly to the Naive Bayes

Works with a small amount of data

Sensitive to how the input data is prepared

Works with nominal values

NO.59 Which of the below best describe the Principal component analysis

Dimensionality reduction

Collaborative filtering

Classification

Regression

Clustering

NO.60 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?

NO.61 What are the advantages of the mutual information over the Pearson correlation for text classification problems?

The mutual information has a meaningful test for statistical significance.

The mutual information can signal non-linear relationships between the dependent and independent variables.

The mutual information is easier to parallelize.

The mutual information doesn’t assume that the variables are normally distributed.

NO.62 Question-13. Which of the following is not the Classification algorithm?

Logistic Regression

Support Vector Machine

Neural Network

Hidden Markov Models

None of the above

NO.63 Consider the following confusion matrix for a data set with 600 out of 11,100 instances positive:
In this case, Precision = 50%, Recall = 83%, Specificity = 95%, and Accuracy = 95%.
Select the correct statement

Precision is low, which means the classifier is predicting positives best

Precision is low, which means the classifier is predicting positives poorly

problem domain has a major impact on the measures that should be used to evaluate a classifier within it

1 and 3

2 and 3

NO.64 You are working with the Clustering solution of the customer datasets. There are almost 40 variables are available for each customer and almost 1.00,0000 customer’s data is available. You want to reduce the number of variables for clustering, what would you do?

You will randomly reduce the number of variables

You will find the correlation among the variables and from their variables are not co-related will be discarded.

You will find the correlation among the variables and from the highly co-related variables, you will be considering only one or two variables from it.

You cannot discard any variable for creating clusters.

You can combine several variables in one variable

NO.65 Which is an example of supervised learning?

PCA

k-means clustering

SVD

SVM

NO.66 You are creating a Classification process where input is the income, education and current debt of a customer, what could be the possible output of this process.

Probability of the customer default on loan repayment

Percentage of the customer loan repayment capability

Percentage of the customer should be given loan or not

The output might be a risk class, such as “good”, “acceptable”, “average”, or “unacceptable”.

NO.67 Which of the following is a Continuous Probability Distributions?

Binomial probability distribution

Negative binomial distribution

Poisson probability distribution

Normal probability distribution

NO.68 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

NO.69 A data scientist is asked to implement an article recommendation feature for an on-line magazine.
The magazine does not want to use client tracking technologies such as cookies or reading history. Therefore, only the style and subject matter of the current article is available for making recommendations. All of the magazine’s articles are stored in a database in a format suitable for analytics.
Which method should the data scientist try first?

K Means Clustering

Naive Bayesian

Logistic Regression

Association Rules

NO.70 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?

Databricks Databricks-Certified-Professional-Data-Scientist Exam Syllabus Topics:

Topic	Details
Topic 1	A intermediate understanding of the steps in the machine learning lifecycle Model training, selection, and production
Topic 2	Tree-based models like decision trees, random forest and gradient boosted trees Categories of machine learning
Topic 3	A complete understanding of the basics of machine learning model management Linear, logistic, and regularized regression
Topic 4	A complete understanding of basic machine learning algorithms and techniques Unsupervised techniniques like K-means and PCA
Topic 5	A complete understanding of the basics of machine learning in-sample vs. out-of sample data