[Jul 31, 2022] Databricks-Certified-Professional-Data-Scientist Dumps Full Questions – Exam Study Guide [Q33-Q57]

Rate this post

[Jul 31, 2022] Databricks-Certified-Professional-Data-Scientist Dumps Full Questions – Exam Study Guide

Databricks Certification Free Certification Exam Material from Test4Cram with 140 Questions

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

Topic	Details
Topic 1	A complete understanding of basic machine learning algorithms and techniques Unsupervised techniniques like K-means and PCA
Topic 2	Specific algorithms like ALS for recommendation and isolation forests for outlier detection Logging and model organization with MLflow
Topic 3	A complete understanding of the basics of machine learning in-sample vs. out-of sample data
Topic 4	A complete understanding of the basics of machine learning model management Linear, logistic, and regularized regression
Topic 5	Applied statistics concepts bias-variance tradeoff

NEW QUESTION 33
RMSE is a useful metric for evaluating which types of models?

Logistic regression

Naive Bayes classifier

Linear regression

All of the above

NEW QUESTION 34
If you are trying to predict or forecast a discrete target value, then which is the correct options

Supervised Learning regression algorithms

Supervised Learning classification algorithms

Un supervised Learning

Density estimation algorithm

NEW QUESTION 35
Which method is used to solve for coefficients bO, b1, … bn in your linear regression model:

Apriori Algorithm

Ridge and Lasso

Ordinary Least squares

Integer programming

NEW QUESTION 36
Support vector machines (SVMs) are a set of supervised learning methods used for

Linear classification

Non-linear classification

Regression

NEW QUESTION 37
Marie is getting married tomorrow, at an outdoor ceremony in the desert. In recent years, it has rained only 5 days each year. Unfortunately, the weatherman has predicted rain for tomorrow. When it actually rains, the weatherman correctly forecasts rain 90% of the time. When it doesn’t rain, he incorrectly forecasts rain 10% of the time. Which of the following will you use to calculate the probability whether it will rain on the day of Marie’s wedding?

Naive Bayes

Logistic Regression

Random Decision Forests

All of the above

NEW QUESTION 38
You have collected the 100’s of parameters about the 1000’s of websites e.g. daily hits, average time on the websites, number of unique visitors, number of returning visitors etc. Now you have find the most important parameters which can best describe a website, so which of the following technique you will use

PCA (Principal component analysis)

Linear Regression

Logistic Regression

Clustering

NEW QUESTION 39
Question-26. There are 5000 different color balls, out of which 1200 are pink color. What is the maximum likelihood estimate for the proportion of “pink” items in the test set of color balls?

2.4

24 0

.24

.48

4.8

NEW QUESTION 40
What describes a true property of Logistic Regression method?

It handles missing values well.

It works well with discrete variables that have many distinct values.

It is robust with redundant variables and correlated variables.

It works well with variables that affect the outcome in a discontinuous way.

NEW QUESTION 41
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.

NEW QUESTION 42
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.

Support vector machines

Naive Bayes

Logistic regression

Random decision forests

All of the above

NEW QUESTION 43
What describes a true limitation of Logistic Regression method?

It does not handle redundant variables well.

It does not handle missing values well.

It does not handle correlated variables well.

It does not have explanatory values.

NEW QUESTION 44
A bio-scientist is working on the analysis of the cancer cells. To identify whether the cell is cancerous or not, there has been hundreds of tests are done with small variations to say yes to the problem. Given the test result for a sample of healthy and cancerous cells, which of the following technique you will use to determine whether a cell is healthy?

Linear regression

Collaborative filtering

Naive Bayes

Identification Test

NEW QUESTION 45
A website is opened 3 times by a user. What is the probability of he clicks 2 times the advertisement, is best calculated by

Binomial

Poisson

Normal

Any of the above

NEW QUESTION 46
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?

NEW QUESTION 47
Which of the following technique can be used to the design of recommender systems?

Naive Bayes classifier

Power iteration

Collaborative filtering

1 and 3

2 and 3

NEW QUESTION 48
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 49
Of all the smokers in a particular district, 40% prefer brand A and 60% prefer brand B.Of those smokers who prefer brand A. 30% are females, and of those who prefer brand B.40% are female. What is the probability that a randomly selected smoker prefers brand A, given that the person selected is a female?
Which of the following is a best way to solve this problem?

Bays Theorem

Poisson Distribution

Binomial Distribution

None of the above

NEW QUESTION 50
Refer to image below

Option A

Option B

Option C

Option D

NEW QUESTION 51
Select the correct statement which applies to K-Nearest Neighbors

No Assumption about the data

Computationally expensive

Require less memory

Works with Numeric Values

NEW QUESTION 52
What is the best way to evaluate the quality of the model found by an unsupervised algorithm like k-means clustering, given metrics for the cost of the clustering (how well it fits the data) and its stability (how similar the clusters are across multiple runs over the same data)?

The lowest cost clustering subject to a stability constraint

The lowest cost clustering

The most stable clustering subject to a minimal cost constraint

The most stable clustering

NEW QUESTION 53
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?

NEW QUESTION 54
What type of output generated in case of linear regression?

Continuous variable

Discrete Variable

Any of the Continuous and Discrete variable

Values between 0 and 1

NEW QUESTION 55
You are working in an ecommerce organization, where you are designing and evaluating a recommender system, you need to select which of the following metric wilt always have the largest value?

Root Mean Square Error

Sum of Errors

Mean Absolute Error

Both land 2

Information is not good enough.

NEW QUESTION 56
Which of the following is not a correct application for the Classification?

credit scoring

tumor detection

image recognition

drug discovery

NEW QUESTION 57
Select the correct objectives of principal component analysis

To reduce the dimensionality of the data set