[Jan 08, 2022] Databricks-Certified-Professional-Data-Scientist Dumps PDF and Test Engine Exam Questions - Pass4suresVCE
Verified Databricks-Certified-Professional-Data-Scientist exam dumps Q&As with Correct 140 Questions and Answers
Databricks Databricks-Certified-Professional-Data-Scientist Exam Syllabus Topics:
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NEW QUESTION 40
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?
- A. Discriminant Analysis
- B. Logistic Regression
- C. Association Rules
- D. Linear Regression
Answer: B
NEW QUESTION 41
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?
- A. Income
- B. Age
- C. Credit Score
- D. Gender
Answer: C
NEW QUESTION 42
RMSE measures error of a predicted
- A. For booth Numerical and categorical values
- B. Numerical Value
- C. Categorical values
Answer: B
NEW QUESTION 43
Which method is used to solve for coefficients bO, b1, ... bn in your linear regression model:
- A. Integer programming
- B. Apriori Algorithm
- C. Ridge and Lasso
- D. Ordinary Least squares
Answer: D
Explanation:
Explanation : RY = b0 + b1x1+b2x2+ .... +bnxn
In the linear model, the bi's represent the unknown p parameters. The estimates for these unknown parameters are chosen so that, on average, the model provides a reasonable estimate of a person's income based on age and education. In other words, the fitted model should minimize the overall error between the linear model and the actual observations. Ordinary Least Squares (OLS) is a common technique to estimate the parameters
NEW QUESTION 44
Select the correct statement which applies to K-Nearest Neighbors
- A. No Assumption about the data
- B. Works with Numeric Values
- C. Computationally expensive
- D. Require less memory
Answer: A,B,C
Explanation:
Explanation : k-Nearest Neighbors
Pros: High accuracy insensitive to outliers, no assumptions about data
Cons: Computationally expensive, requires a lot of memory
Works with: Numeric values, nominal values
NEW QUESTION 45
You have data of 10.000 people who make the purchasing from a specific grocery store. You also have their income detail in the data. You have created 5 clusters using this data. But in one of the cluster you see that only 30 people are falling as below 30, 2400, 2600, 2700, 2270 etc." What would you do in this case?
- A. You will remove that 30 people from dataset
- B. You will be decreasing the number of clusters.
- C. You will be multiplying standard deviation with the 100
- D. You will be increasing number of clusters.
Answer: B
Explanation:
Explanation
Decreasing the number of clusters will help in adjusting this outlier cluster to get adjusted in another cluster.
NEW QUESTION 46
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. Naive Bayes classifier
- B. Content-based filtering
- C. Item-based collaborative filtering
- D. User-based collaborative filtering
Answer: B
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 47
Your company has organized an online campaign for feedback on product quality and you have all the responses for the product reviews, in the response form people have check box as well as text field. Now you know that people who do not fill in or write non-dictionary word in the text field are not considered valid feedback. People who fill in text field with proper English words are considered valid response. Which of the following method you should not use to identify whether the response is valid or not?
- A. Random Decision Forests
- B. Logistic Regression
- C. Any one of the above
- D. Naive Bayes
Answer: C
Explanation:
Explanation
In this problem you have been given high-dimensional independent variables like yeS; nO; no English words , test results etc. and you have to predict either valid or not valid (One of two). So all of the below technique can be applied to this problem.
* Support vector machines
* Naive Bayes
* Logistic regression
* Random decision forests
NEW QUESTION 48
Which of the following are point estimation methods?
- A. MLE
- B. MAP
- C. MMSE
Answer: A,B,C
Explanation:
Explanation
Point estimators
* minimum-variance mean-unbiased estimator (MVUE), minimizes the risk (expected loss) of the squared-error loss-function.
* best linear unbiased estimator (BLUE)
* minimum mean squared error (MMSE)
* median-unbiased estimator, minimizes the risk of the absolute-error loss function
* maximum likelihood (ML)
* method of moments, generalized method of moments
NEW QUESTION 49
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
- A. Precision is low, which means the classifier is predicting positives best
- B. problem domain has a major impact on the measures that should be used to evaluate a classifier within it
- C. 1 and 3
- D. 2 and 3
- E. Precision is low, which means the classifier is predicting positives poorly
Answer: D
Explanation:
Explanation
In this case, Precision = 50%, Recall = 83%, Specificity = 95%: and Accuracy = 95%. In this case, Precision is low, which means the classifier is predicting positives poorly. However, the three other measures seem to suggest that this is a good classifier. This just goes to show that the problem domain has a major impact on the measures that should be used to evaluate a classifier within it, and that looking at the 4 simple cases presented is not sufficient.
NEW QUESTION 50
Refer to image below
- A. Option D
- B. Option C
- C. Option B
- D. Option A
Answer: D
Explanation:
Explanation
Text Description automatically generated
NEW QUESTION 51
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. Percentage of the customer loan repayment capability
- B. Percentage of the customer should be given loan or not
- C. Probability of the customer default on loan repayment
- D. The output might be a risk class, such as "good", "acceptable", "average", or "unacceptable".
Answer: D
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 52
Which of the following question statement falls under data science category?
- A. Where is a problem for sales?
- B. Which is the optimal scenario for selling this product?
- C. What happened in last six months?
- D. What happens, if these scenario continues?
- E. How many products have been sold in a last month?
Answer: B,D
Explanation:
Explanation
This question wants to check your understanding about Bl and Data Science. Bl was already existing and analytics team already using it. They need to improve and learn data science technique to solve some problems. If you check the option given in the question, it will confuse you. But if you have worked in Bl or as a Data Scientist then it is easy to answer. First 3 option can be easily answered using reporting solution, what sales happened in last six month, what was the problem etc.
But for the last two option you need to apply data science techniques like which all scenarios are optimal for product sales, you need to collect the data and applying various techniques for that. Hence, last two option can only be answered using Data Science technique And for this you need to apply techniques like Optimization, predictive modeling, statistical analysis on structured and un-structured data.
NEW QUESTION 53
A denote the event 'student is female' and let B denote the event 'student is French'. In a class of 100 students suppose 60 are French, and suppose that 10 of the French students are females. Find the probability that if I pick a French student, it will be a girl, that is, find P(A|B).
- A. 2/6
- B. 1/3
- C. 2/3
- D. 1/6
Answer: D
Explanation:
Explanation
Since 10 out of 100 students are both French and female, then
P(AandB)=10100
Also. 60 out of the 100 students are French, so
P(B)=60100
So the required probability is:
P(A|B)=P(AandB)P(B)=10/10060/100=16
NEW QUESTION 54
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?
- A. 2.4
- B. .48
- C. .24
- D. 24 0
- E. 4.8
Answer: C
Explanation:
Explanation
Given no additional information, the MLE for the probability of an item in the test set is exactly its frequency in the training set. The method of maximum likelihood corresponds to many well-known estimation methods in statistics. For example, one may be interested in the heights of adult female penguins, but be unable to measure the height of every single penguin in a population due to cost or time constraints. Assuming that the heights are normally (Gaussian) distributed with some unknown mean and variance, the mean and variance can be estimated with MLE while only knowing the heights of some sample of the overall population. MLE would accomplish this by taking the mean and variance as parameters and finding particular parametric values that make the observed results the most probable (given the model).
In general, for a fixed set of data and underlying statistical model the method of maximum likelihood selects the set of values of the model parameters that maximizes the likelihood function. Intuitively, this maximizes the "agreement" of the selected model with the observed data, and for discrete random variables it indeed maximizes the probability of the observed data under the resulting distribution. Maximum-likelihood estimation gives a unified approach to estimation, which is well-defined in the case of the normal distribution and many other problems. However in some complicated problems, difficulties do occur: in such problems, maximum-likelihood estimators are unsuitable or do not exist.
NEW QUESTION 55
You are asked to create a model to predict the total number of monthly subscribers for a specific magazine.
You are provided with 1 year's worth of subscription and payment data, user demographic data, and 10 years worth of content of the magazine (articles and pictures). Which algorithm is the most appropriate for building a predictive model for subscribers?
- A. Linear regression
- B. Logistic regression
- C. Decision trees
- D. TF-IDF
Answer: A
Explanation:
Explanation : A data model explicitly describes a relationship between predictor and response variables.
Linear regression fits a data model that is linear in the model coefficients. The most common type of linear regression is a least-squares fit, which can fit both lines and polynomials, among other linear models.
Before you model the relationship between pairs of quantities, it is a good idea to perform correlation analysis to establish if a linear relationship exists between these quantities. Be aware that variables can have nonlinear relationships, which correlation analysis cannot detect. For more information, see Linear Correlation.
If you need to fit data with a nonlinear model, transform the variables to make the relationship linear.
Alternatively try to fit a nonlinear function directly using either the Statistics and Machine Learning Toolbox nlinfit function, the Optimization Toolbox Isqcurvefit function, or by applying functions in the Curve Fitting Toolbox.
79
NEW QUESTION 56
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?
- A. Naive Bayes Classifier
- B. Association Rules
- C. Grouping
- D. Linear Regression
- E. K-means Clustering
Answer: E
Explanation:
Explanation
Using K means clustering you can create group of objects based on their properties. Where K is number of the groups. In this case, in each group you determine the center of the group and then find the how far each object characteristics from the center. If it is near the center than it can be part of the group. Suppose we have 100 objects and we need to determine 4 groups. Hence, here K=4. Now we determine 4 center values and based on that center value we determine the distance of each object from the center.
NEW QUESTION 57
In which of the following scenario you should apply the Bay's Theorem
- A. In all above cases
- B. The sample space is partitioned into a set of mutually exclusive events {A1, A2, . .., An }.
- C. Within the sample space, there exists an event B, for which P(B) > 0.
- D. The analytical goal is to compute a conditional probability of the form: P(Ak | B ).
Answer: A
NEW QUESTION 58
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?
- A. All of the above
- B. Logistic Regression
- C. Laplace Smoothing
- D. Naive Bayes
Answer: C
Explanation:
Explanation
Laplace smoothing is a technique for parameter estimation which accounts for unobserved events. It is more robust and will not fail completely when data that has never been observed in training shows up.
NEW QUESTION 59
What is one modeling or descriptive statistical function in MADlib that is typically not provided in a standard relational database?
- A. Linear regression
- B. Expected value
- C. Quantiles
- D. Variance
Answer: A
Explanation:
Explanation
Linear regression models a linear relationship of a scalar dependent variable y to one or more explanatory independent variables x to build a model of coefficients.
NEW QUESTION 60
The method based on principal component analysis (PCA) evaluates the features according to
- A. None of the above
- B. The projection of the smallest eigenvector of the correlation matrix on the initial dimensions
- C. The projection of the largest eigenvector of the correlation matrix on the initial dimensions
- D. According to the magnitude of the components of the discriminate vector
Answer: C
Explanation:
Explanation
Feature Selection:
The method based on principal component analysis (PCA) evaluates the features according to the projection of the largest eigenvector of the correlation matrix on the initial dimensions, the method based on Fisher's linear discriminate analysis evaluates. Them according to the magnitude of the components of the discriminate vector.
NEW QUESTION 61
You are analyzing data in order to build a classifier model. You discover non-linear data and discontinuities that will affect the model. Which analytical method would you recommend?
- A. Decision Trees
- B. Logistic Regression
- C. Linear Regression
- D. ARIMA
Answer: A
Explanation:
Explanation
A decision tree is a flowchart-like structure in which each internal node represents a "test" on an attribute (e.g.
whether a coin flip comes up heads or tails), each branch represents the outcome of the test and each leaf node represents a class label (decision taken after computing all attributes). The paths from root to leaf represents classification rules.
In decision analysis a decision tree and the closely related influence diagram are used as a visual and analytical decision support tool, where the expected values (or expected utility) of competing alternatives are calculated.
A decision tree consists of 3 types of nodes:
1. Decision nodes - commonly represented by squares
2. Chance nodes - represented by circles
3. End nodes - represented by triangles
Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal. If in practice decisions have to be taken online with no recall under incomplete knowledge, a decision tree should be paralleled by a probability model as a best choice model or online selection model algorithm. Another use of decision trees is as a descriptive means for calculating conditional probabilities.
Decision trees, influence diagrams, utility functions, and other decision analysis tools and methods are taught to undergraduate students in schools of business, health economics, and public health, and are examples of operations research or management science methods.
NEW QUESTION 62
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.)
- A. marginal probability that P(H=Not Hit) is the sum of the H= Hit row
- B. marginal probability that P(H=Not Hit) is the sum of the H=Not Hit row
- C. 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.
Answer: B,C
Explanation:
Explanation
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. Similarly, the marginal probability that P(H=Not Hit) is the sum of the H=Not Hit row
NEW QUESTION 63
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