[Sep 07, 2022] Databricks Databricks-Certified-Professional-Data-Scientist Real Exam Questions and Answers FREE
Pass Databricks Databricks-Certified-Professional-Data-Scientist Exam Info and Free Practice Test
NEW QUESTION 21
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?
- A. 0
- B. 1
- C. 2
- D. 3
Answer: C
NEW QUESTION 22
You are working as a data science consultant for a gaming company. You have three member team and all other stake holders are from the company itself like project managers and project sponsored, data team etc.
During the discussion project managed asked you that when can you tell me that the model you are using is robust enough, after which step you can consider answer for this question?
- A. Data Preparation
- B. Model building
- C. Model planning
- D. Operationalize
- E. Discovery
Answer: B
Explanation:
Explanation
To answer whether the model you are building is robust enough or not you need to have answer below questions at least
- Model is performing as expected with the test data or not?
- Whatever hypothesis defined in the initial phase is being tested or not?
- Do we need more data?
- Domain experts are convinced or not with the model?
And all these can be answered when you have built the model and tested with the test data sets. Hence, correct option will be Model Building.
NEW QUESTION 23
Which of the following problem you can solve using binomial distribution
- A. It was found that the mean length of 100 parts produced by a lathe was 20.05 mm with a standard deviation of 0.02 mm. Find the probability that a part selected at random would have a length between
20.03 mm and 20.08 mm - B. A manufacturer of metal pistons finds that on the average: 12% of his pistons are rejected because they are either oversize or undersize. What is the probability that a batch of 10 pistons will contain no more than 2 rejects?
- C. A life insurance salesman sells on the average 3 life insurance policies per week. Use Poisson's law to calculate the probability that in a given week he will sell Some policies
- D. Vehicles pass through a junction on a busy road at an average rate of 300 per hour Find the probability that none passes in a given minute.
Answer: B
Explanation:
Explanation
The entire problem can be solved using below method
Binomial: A manufacturer of metal pistons finds that on the average, 12% of his pistons are rejected because they are either oversize or undersize. What is the probability that a batch of 10 pistons will contain no more than 2 rejects?
Poisson: A life insurance salesman sells on the average 3 life insurance policies per week. Use Poisson's law to calculate the probability that in a given week he will sell Some policies Poisson: Vehicles pass through a junction on a busy road at an average rate of 300 per hour Find the probability that none passes in a given minute.
Normal: It was found that the mean length of 100 parts produced by a lathe was 20.05 mm with a standard deviation of 0.02 mm. Find the probability that a part selected at random would have a length between 20 03 mm and 20.08 mm
NEW QUESTION 24
You are creating a model for the recommending the book at Amazon.com, so which of the following recommender system you will use you don't have cold start problem?
- A. User-based collaborative filtering
- B. Item-based collaborative filtering
- C. Naive Bayes classifier
- D. Content-based filtering
Answer: D
Explanation:
Explanation
The cold start problem is most prevalent in recommender systems. Recommender systems form a specific type of information filtering (IF) technique that attempts to present information items (movies, music, books, news, images, web pages) that are likely of interest to the user. Typically, a recommender system compares the user's profile to some reference characteristics. These characteristics may be from the information item (the content-based approach) or the user's social environment (the collaborative filtering approach). In the content-based approach, the system must be capable of matching the characteristics of an item against relevant features in the user's profile. In order to do this, it must first construct a sufficiently-detailed model of the user's tastes and preferences through preference elicitation. This may be done either explicitly (by querying the user) or implicitly (by observing the user's behaviour). In both cases, the cold start problem would imply that the user has to dedicate an amount of effort using the system in its 'dumb' state - contributing to the construction of their user profile - before the system can start providing any intelligent recommendations.
Content-based filtering recommender systems use information about items or users to make recommendations, rather than user preferences, so it will perform well with little user preference data. Item-based and user-based collaborative filtering makes predictions based on users' preferences for items, os they will typically perform poorly with little user preference data. Logistic regression is not recommender system technique.
NEW QUESTION 25
Select the correct statement regarding the naive Bayes classification
- A. only the variances of the variables for each class need to be determined
- B. for each class entire covariance matrix need to be determined
- C. it only requires a small amount of training data to estimate the parameters
- D. Independent variables can be assumed
Answer: A,C,D
Explanation:
Explanation
An advantage of naive Bayes is that it only requires a small amount of training data to estimate the parameters (means and variances of the variables) necessary for classification. Because independent variables are assumed, only the variances of the variables for each class need to be determined and not the entire covariance matrix.
NEW QUESTION 26
Which of the following statement is true for the R square value in the regression model?
- A. R-squared never decreases upon adding more independent variables.
- B. R square can be increased by adding more variables to the model.
- C. When R square =0, all the residual are equal to 1
- D. When R square =1 , all the residuals are equal to 0
Answer: A,B,D
NEW QUESTION 27
Suppose you have been given two Random Variables X and Y, whose joint distribution is already known, the marginal distribution of X is simply the probability distribution of X averaging over information about Y.
It is the probability distribution of X when the value of Y is not known. So how do you calculate the marginal distribution of X
- A. This is typically calculated by summing the joint probability distribution over Y.
- B. This is typically calculated by integrating the joint probability distribution over Y
- C. This is typically calculated by summing (In case of discrete variable) the joint probability distribution over Y
- D. This is typically calculated by integrating(ln case of continuous variable) the joint probability distribution over Y.
Answer: A,B,C,D
Explanation:
Explanation
Given two random variables X and Y whose joint distribution is known, the marginal distribution of X is simply the probability distribution of X averaging over information about Y.
It is the probability distribution of X when the value of Y is not known. This is typically calculated by summing or integrating the joint probability distribution over Y. ' For discrete random variables, the marginal probability mass function can be written as Pr(X = x). This is Text Description automatically generated with low confidence
where Pr(X = x,Y = y) is the joint distribution of X and Y, while Pr(X = x|Y = y) is the conditional distribution of X given Y In this case, the variable Y has been marginalized out.
Bivariate marginal and joint probabilities for discrete random variables are often displayed as two-way tables.
Similarly for continuous random variables, the marginal probability density function can be written as pX(x). This is Diagram Description automatically generated with medium confidence
where pX.Y(x.y) gives the joint distribution of X and Y while pX|Y(x|y) gives the conditional distribution for X given Y Again: the variable Y has been marginalized out.
Note that a marginal probability can always be written as an expected value:
Text, letter Description automatically generated
Intuitively, the marginal probability of X is computed by examining the conditional probability of X given a particular value of Y, and then averaging this conditional probability over the distribution of all values of Y This follows from the definition of expected value, i.e. in general A picture containing diagram Description automatically generated
NEW QUESTION 28
Which of the following skills a data scientists required?
- A. He should possess database administrative skills.
- B. Should be very good at mathematics and statistic
- C. He should be creative
- D. Should possess good programming skills
- E. Web designing to represent best visuals of its results from algorithm.
Answer: B,C,D
Explanation:
Explanation
Yes a data scientists should have combination of skills like to solve the complex problem he should be creative as well as able to find new solutions and use of existing data. And solve the problem skills required are programming as currently we see SAS, R: Python, Spark, Java and SPSS even day by day new technologies are coming.
To apply various existing and new algorithm using Machine Learning, or Al it require good mathematics and statistics skills (Where the programmer feels, weaknesses). Another skill required is using visualization techniques like Qlik, Tableau etc
NEW QUESTION 29
Select the correct algorithm of unsupervised algorithm
- A. Naive Bayes
- B. K-Nearest Neighbors
- C. K-Means
- D. Support Vector Machines
Answer: B
Explanation:
Explanation
Sup Supervised learning tasks
Classification Regression
k-Nearest Neighbors Linear
Naive Bayes Locally weighted linear
Support vector machines Ridge
Decision trees Lasso
Unsupervised learning tasks Clustering Density estimation k-Means Expectation maximization DBSCAN Parzen window
NEW QUESTION 30
In which of the scenario you can use the regression to predict the values
- A. Mobile companies can use it to forecast manufacturing defects
- B. Only 1 and 2
- C. Samsung can use it for mobile sales forecast
- D. Probability of the celebrity divorce
- E. All 1 ,2 and 3
Answer: E
Explanation:
Explanation
Regression is a tool which Companies may use this for things such as sales forecasts or forecasting manufacturing defects. Another creative example is predicting the probability of celebrity divorce.
NEW QUESTION 31
You are using k-means clustering to classify heart patients for a hospital. You have chosen Patient Sex, Height, Weight, Age and Income as measures and have used 3 clusters. When you create a pair-wise plot of the clusters, you notice that there is significant overlap between the clusters. What should you do?
- A. Increase the number of clusters
- B. Remove one of the measures
- C. Identify additional measures to add to the analysis
- D. Decrease the number of clusters
Answer: D
NEW QUESTION 32
Which of the following statement true with regards to Linear Regression Model?
- A. In Linear model, it tries to find multiple lines which can approximate the relationship between the outcome and input variables.
- B. Ordinary Least Square is a sum of the individual distance between each point and the fitted line of regression model.
- C. Ordinary Least Square is a sum of the squared individual distance between each point and the fitted line of regression model.
- D. Ordinary Least Square can be used to estimates the parameters in linear model
Answer: C,D
Explanation:
Explanation
Linear regression model are represented using the below equation
Where B(0) is intercept and B(1) is a slope. As B(0) and B(1) changes then fitted line also shifts accordingly on the plot. The purpose of the Ordinary Least Square method is to estimates these parameters B(0) and B(1).
And similarly it is a sum of squared distance between the observed point and the fitted line. Ordinary least squares (OLS) regression minimizes the sum of the squared residuals. A model fits the data well if the differences between the observed values and the model's predicted values are small and unbiased.
NEW QUESTION 33
What are the key outcomes of the successful analytical projects?
- A. Code of the model
- B. Technical specifications
- C. Presentations for the Analysts
- D. Presentation for Project Sponsors
Answer: A,B,C,D
Explanation:
Explanation
When your analytical project successfully completed they come up with the following at the end of the projects. Presentations- You will be having presentations like for the all the stakeholders, generally these presentation will help seniors executives to make better decisions. Similarly you would be creating presentations for the other teams like analysts various visuals you would be creating like ROC Curves, Heat Maps, and Bar Charts etc.
Whatever tools you have used like SAS, R, or Python then accordingly code was developed and you will get that code as one of the outcome. Also you would have created a technical specifications for implementing the codes.
NEW QUESTION 34
If E1 and E2 are two events, how do you represent the conditional probability given that E2 occurs given that E1 has occurred?
- A. P(E1+E2)/P(E1)
- B. P(E2)/(P(E1+E2)
- C. P(E2)/P(E1)
- D. P(E1)/P(E2)
Answer: C
NEW QUESTION 35
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.
- A. Probability of the customer default on loan repayment
- B. Percentage of the customer loan repayment capability
- C. The output might be a risk class, such as "good", "acceptable", "average", or "unacceptable".
- D. Percentage of the customer should be given loan or not
Answer: C
Explanation:
Explanation
Classification is the process of using several inputs to produce one or more outputs. For example the input might be the income, education and current debt of a customer The output might be a risk class, such as
"good", "acceptable", "average", or "unacceptable". Contrast this to regression where the output is a number not a class.
NEW QUESTION 36
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 ?
- A. Collaborative filtering
- B. Content-based filtering
- C. Naive Bayes classifier
- D. Logistic Regression
Answer: A
Explanation:
Explanation
Another aspect of collaborative filtering systems is 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 help 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
NEW QUESTION 37
A fruit may be considered to be an apple if it is red, round, and about 3" in diameter. A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of the
- A. Presence or absence of the other features
- B. Absence of the other features.
- C. Presence of the other features.
- D. None of the above
Answer: A
Explanation:
Explanation
In simple terms, a naive Bayes classifier assumes that the value of a particular feature is unrelated to the presence or absence of any other feature, given the class variable. For example, a fruit may be considered to be an apple if it is red, round, and about 3" in diameter A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of the presence or absence of the other features.
NEW QUESTION 38
Select the statement which applies correctly to the Naive Bayes
- A. Works with nominal values
- B. Works with a small amount of data
- C. Sensitive to how the input data is prepared
Answer: A,B,C
NEW QUESTION 39
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
- A. Maximum likelihood estimation
- B. Linear Regression
- C. Recommendation system
- D. Hierarchical linear models
- E. Logistic Regression
Answer: E
Explanation:
Explanation
Logistic regression
Pros: Computationally inexpensive, easy to implement, knowledge representation easy to interpret Cons: Prone to underfitting, may have low accuracy Works with: Numeric values, nominal values
NEW QUESTION 40
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
- A. Maximum likelihood estimation
- B. Linear Regression
- C. Recommendation system
- D. Hierarchical linear models
- E. Logistic Regression
Answer: E
Explanation:
Explanation : Logistic regression
Pros: Computationally inexpensive, easy to implement, knowledge representation easy to interpret Cons: Prone to underfitting, may have low accuracy Works with: Numeric values, nominal values
NEW QUESTION 41
Which of the following technique can be used to the design of recommender systems?
- A. Power iteration
- B. Collaborative filtering
- C. 2 and 3
- D. Naive Bayes classifier
- E. 1 and 3
Answer: B
Explanation:
Explanation
One approach to the design of recommender systems that has seen wide use is collaborative filtering.
Collaborative filtering methods are based on collecting and analyzing a large amount of information on users' behaviors, activities or preferences and predicting what users will like based on their similarity to other users.
A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of accurately recommending complex items such as movies without requiring an
"understanding" of the item itself. Many algorithms have been used in measuring user similarity or item similarity in recommender systems. For example the k-nearest neighbor (k-NN) approach and the Pearson Correlation
NEW QUESTION 42
Suppose there are three events then which formula must always be equal to P(E1|E2,E3)?
- A. P(E1,E2;E3)/P(E2,E3)
- B. P(E1,E2|E3)P(E3)
- C. P(E1,E2,E3)P(E1)/P(E2:E3)
- D. P(E1,E2,E3)P(E2)P(E3)
- E. P(E1,E2|E3)P(E2|E3)P(E3)
Answer: A
Explanation:
Explanation
This is an application of conditional probability: P(E1,E2)=P(E1|E2)P(E2). so P(E1|E2) = P(E1.E2)/P(E2) P(E1,E2,E3)/P(E2,E3) If the events are A and B respectively, this is said to be "the probability of A given B" It is commonly denoted by P(A|B): or sometimes PB(A). In case that both "A" and "B" are categorical variables, conditional probability table is typically used to represent the conditional probability.
NEW QUESTION 43 
The figure below shows a plot of the data of a data matrix M that is 1000 x 2. Which line represents the first principal component?
- A. blue
- B. Neither
- C. yellow
Answer: A
Explanation:
Explanation
Principal component analysis (PCA) involves a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible.
The first principal component corresponds to the greatest variance in the data. The blue line is evidently this first principal component, because if we project the data onto the blue line, the data is more spread out (higher variance) than if projected onto any other line, including the yellow one.
NEW QUESTION 44
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