[2022] Databricks-Certified-Professional-Data-Scientist Exam Dumps, Test Engine Practice Test Questions [Q13-Q32]

NEW QUESTION 13
Refer to Exhibit

In the exhibit, the x-axis represents the derived probability of a borrower defaulting on a loan. Also in the exhibit, the pink represents borrowers that are known to have not defaulted on their loan, and the blue represents borrowers that are known to have defaulted on their loan. Which analytical method could produce the probabilities needed to build this exhibit?

Linear Regression

Logistic Regression

Discriminant Analysis

Association Rules

NEW QUESTION 14
Select the choice where Regression algorithms are not best fit

When the dimension of the object given

Weight of the person is given

Temperature in the atmosphere

Employee status

NEW QUESTION 15
A data scientist wants to predict the probability of death from heart disease based on three risk factors: age, gender, and blood cholesterol level. What is the most appropriate method for this project?

Linear regression

K-means clustering

Logistic regression

Apriori algorithm

NEW QUESTION 16
Which of the following statement is true for the R square value in the regression model?

When R square =1 , all the residuals are equal to 0

When R square =0, all the residual are equal to 1

R-squared never decreases upon adding more independent variables.

R square can be increased by adding more variables to the model.

NEW QUESTION 17
Select the correct statement which applies to logistic regression

Computationally inexpensive, easy to implement knowledge representation easy to interpret

May have low accuracy

Works with Numeric values

Only 1 and 3 are correct

All 1, 2 and 3 are correct

NEW QUESTION 18
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 19
Which activity is performed in the Operationalize phase of the Data Analytics Lifecycle?

Define the process to maintain the model

Try different analytical techniques

Try different variables

Transform existing variables

NEW QUESTION 20
Which of the following are point estimation methods?

MAP

MLE

MMSE

NEW QUESTION 21
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 22
Spam filtering of the emails is an example of

Supervised learning

Unsupervised learning

Clustering

1 and 3 are correct

2 and 3 are correct

NEW QUESTION 23
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 24
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 25
A problem statement is given as below
Hospital records show that of patients suffering from a certain disease, 75% die of it. What is the probability that of 6 randomly selected patients, 4 will recover?
Which of the following model will you use to solve it.

Binomial

Poisson

Normal

Any of the above

NEW QUESTION 26
You are working on a email spam filtering assignment, while working on this you find there is new word e.g.
HadoopExam comes in email, and in your solutions you never come across this word before, hence probability of this words is coming in either email could be zero. So which of the following algorithm can help you to avoid zero probability?

Naive Bayes

Laplace Smoothing

Logistic Regression

All of the above

NEW QUESTION 27
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 28
You are designing a recommendation engine for a website where the ability to generate more personalized recommendations by analyzing information from the past activity of a specific user, or the history of other users deemed to be of similar taste to a given user. These resources are used as user profiling and helps the site recommend content on a user-by-user basis. The more a given user makes use of the system, the better the recommendations become, as the system gains data to improve its model of that user. What kind of this recommendation engine is ?

Naive Bayes classifier

Collaborative filtering

Logistic Regression

Content-based filtering

NEW QUESTION 29
Which of the below best describe the Principal component analysis

Dimensionality reduction

Collaborative filtering

Classification

Regression

Clustering

NEW QUESTION 30
In which lifecycle stage are test and training data sets created?

Model planning

Discovery

Model building

Data preparation

Explanation
In Phase 1, the team learns the business domain, including relevant history such as whether the organization or business unit has attempted similar projects in the past from which they can learn. The team assesses the resources available to support the project in terms of people, technology time, and data. Important activities in this phase include framing the business problem as an analytics challenge that can be addressed in subsequent phases and formulating initial hypotheses (IHs) to test and begin learning the data. Data preparation: Phase 2 requires the presence of an analytic sandbox, in which the team can work with data and perform analytics for the duration of the project. The team needs to execute extract, load, and transform (ELT) or extract, transform and load (ETL) to get data into the sandbox. The ELT and ETL are sometimes abbreviated as ETLT Data should be transformed in the ETLT process so the team can work with it and analyze it. In this phase, the team also needs to familiarize itself with the data thoroughly and take steps to condition the data Model planning:
Phase 3 is model planning, where the team determines the methods, techniques, and workflow it intends to follow for the subsequent model building phase. The team explores the data to learn about the relationships between variables and subsequently selects key variables and the most suitable models.
Model building: In Phase 4, the team develops datasets for testing, training, and production purposes. In addition, in this phase the team builds and executes models based on the work done in the model planning phase. The team also considers whether its existing tools will suffice for running the models, or if it will need a more robust environment for executing models and workflows (for example, fast hardware and parallel processing, if applicable).
Communicate results: In Phase 5, the team, in collaboration with major stakeholders, determines if the results of the project are a success or a failure based on the criteria developed in Phase 1. The team should identify key findings, quantify the business value, and develop a narrative to summarize and convey findings to stakeholders.
Operationalize: In Phase 6, the team delivers final reports, briefings, code, and technical documents. In addition, the team may run a pilot project to implement the models in a production environment.

NEW QUESTION 31
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 32
Feature Hashing approach is “SGD-based classifiers avoid the need to predetermine vector size by simply picking a reasonable size and shoehorning the training data into vectors of that size” now with large vectors or with multiple locations per feature in Feature hashing?

Is a problem with accuracy

It is hard to understand what classifier is doing

It is easy to understand what classifier is doing

Is a problem with accuracy as well as hard to understand what classifier us doing

[2022] Databricks-Certified-Professional-Data-Scientist Exam Dumps, Test Engine Practice Test Questions [Q13-Q32]

admin

Leave a Reply Cancel reply

Related posts:

admin

You might also like

Leave a Reply Cancel reply