Online Questions - Valid Practice To your Databricks-Certified-Professional-Data-Scientist Exam (Updated 140 Questions) [Q15-Q30] : Free Exams Dumps Materials : http://exams.dumpsmaterials.com

NO.15 Suppose that we are interested in the factors that influence whether a political candidate wins an election. The outcome (response) variable is binary (0/1); win or lose. The predictor variables of interest are the amount of money spent on the campaign, the amount of time spent campaigning negatively and whether or not the candidate is an incumbent.
Above is an example of

Linear Regression

Logistic Regression

Recommendation system

Maximum likelihood estimation

Hierarchical linear models

NO.16 Projecting a multi-dimensional dataset onto which vector has the greatest variance?

first principal component

first eigenvector

not enough information given to answer

second eigenvector

second principal component

NO.17 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

NO.18 You have used k-means clustering to classify behavior of 100, 000 customers for a retail store. You decide to use household income, age, gender and yearly purchase amount as measures. You have chosen to use 8 clusters and notice that 2 clusters only have 3 customers assigned. What should you do?

Decrease the number of measures used

Increase the number of clusters

Decrease the number of clusters

Identify additional measures to add to the analysis

NO.19 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

NO.20 Which of the following is a Continuous Probability Distributions?

Binomial probability distribution

Negative binomial distribution

Poisson probability distribution

Normal probability distribution

NO.21 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

NO.22 Which of the following statement true with regards to Linear Regression Model?

Ordinary Least Square can be used to estimates the parameters in linear model

In Linear model, it tries to find multiple lines which can approximate the relationship between the outcome and input variables.

Ordinary Least Square is a sum of the individual distance between each point and the fitted line of regression model.

Ordinary Least Square is a sum of the squared individual distance between each point and the fitted line of regression model.

NO.23 Which of the below best describe the Principal component analysis

Dimensionality reduction

Collaborative filtering

Classification

Regression

Clustering

NO.24 Suppose you have made a model for the rating system, which rates between 1 to 5 stars. And you calculated that RMSE value is 1.0 then which of the following is correct

It means that your predictions are on average one star off of what people really think

It means that your predictions are on average two star off of what people really think

It means that your predictions are on average three star off of what people really think

It means that your predictions are on average four star off of what people really think

NO.25 Your customer provided you with 2. 000 unlabeled records three groups. What is the correct analytical method to use?

Semi Linear Regression

Logistic regression

Naive Bayesian classification

Linear regression

K-means clustering

NO.26 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

NO.27 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.

NO.28 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

NO.29 Which of the following is a correct example of the target variable in regression (supervised learning)?

Nominal values like true, false

Reptile, fish, mammal, amphibian, plant, fungi

Infinite number of numeric values, such as 0.100, 42.001, 1000.743..

All of the above

NO.30 Suppose that the probability that a pedestrian will be tul by a car while crossing the toad at a pedestrian crossing without paying attention to the traffic light is lo be computed. Let H be a discrete random variable taking one value from (Hit. Not Hit). Let L be a discrete random variable taking one value from (Red. Yellow.
Green).
Realistically, H will be dependent on L That is, P(H = Hit) and P(H = Not Hit) will take different values depending on whether L is red, yellow or green. A person is. for example, far more likely to be hit by a car when trying to cross while Hie lights for cross traffic are green than if they are red In other words, for any given possible pair of values for Hand L. one must consider the joint probability distribution of H and L to find the probability* of that pair of events occurring together if Hie pedestrian ignores the state of the light Here is a table showing the conditional probabilities of being bit. defending on ibe stale of the lights (Note that the columns in this table must add up to 1 because the probability of being hit oi not hit is 1 regardless of the stale of the light.)

The marginal probability P(H=Hit) is the sum along the H=Hit row of this joint distribution table, as this is the probability of being hit when the lights are red OR yellow OR green.

marginal probability that P(H=Not Hit) is the sum of the H=Not Hit row

marginal probability that P(H=Not Hit) is the sum of the H= Hit row