[Oct-2021] Dumps Brief Outline Of The Databricks-Certified-Professional-Data-Scientist Exam - Prep4sures
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NEW QUESTION 48
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 integrating(ln case of continuous variable) the joint probability distribution over Y.
- B. This is typically calculated by summing 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 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 49
Digit recognition, is an example of.....
- A. Clustering
- B. None of the above
- C. Unsupervised learning
- D. Classification
Answer: D
Explanation:
Explanation
Supervised learning is fairly common in classification problems because the goal is often to get the computer to learn a classification system that we have created. Digit recognition: once again, is a common example of classification learning. More generally, classification learning is appropriate for any problem where deducing a classification is useful and the classification is easy to determine. In some cases, it might not even be necessary to give pre-determined classifications to every instance of a problem if the agent can work out the classifications for itself. This would be an example of unsupervised learning in a classification context.
NEW QUESTION 50
You are working with the Clustering solution of the customer datasets. There are almost 40 variables are available for each customer and almost 1.00,0000 customer's data is available. You want to reduce the number of variables for clustering, what would you do?
- A. You will randomly reduce the number of variables
- B. You cannot discard any variable for creating clusters.
- C. You can combine several variables in one variable
- D. You will find the correlation among the variables and from their variables are not co-related will be discarded.
- E. You will find the correlation among the variables and from the highly co-related variables, you will be considering only one or two variables from it.
Answer: C,E
Explanation:
Explanation
When you are applying clustering technique and you find that there are quite a huge number of variables are available. Then it is better the find the co-relation among the variables and consider only one or two variables from the highly co-related variables. Because highly co-related variable will have the same effect, while creating the cluster. We can use scatter plot matrix among the variables to find the co-relation.
You can also combine several variables into a single variable. For example if you have two values in the dataset like Asset and Debt than by combining these two values like Debt to Asset ratio and use it while creating the cluster.
NEW QUESTION 51
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(E2)/P(E1)
- B. P(E2)/(P(E1+E2)
- C. P(E1+E2)/P(E1)
- D. P(E1)/P(E2)
Answer: A
NEW QUESTION 52
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 53
What is one modeling or descriptive statistical function in MADlib that is typically not provided in a standard relational database?
- A. Variance
- B. Expected value
- C. Quantiles
- D. Linear regression
Answer: D
NEW QUESTION 54
You are working on a Data Science project and during the project you have been gibe a responsibility to interview all the stakeholders in the project. In which phase of the project you are?
- A. Executing Models
- B. Operationnalise the models
- C. Creating Models
- D. Data Preparations
- E. Creating visuals from the outcome
- F. Discovery
Answer: F
Explanation:
Explanation
During the discovery phase you will be interviewing all the project stakeholders because they would be having quite a good amount of knowledge for the problem domain you will be working and you also interviewing project sponsors you will get to know what all are the expectations once project get completed. Hence, you will be noting down all the expectations from the project as well as you will be using their expertise in the domain.
NEW QUESTION 55
Select the correct statement regarding the naive Bayes classification
- A. Independent variables can be assumed
- B. only the variances of the variables for each class need to be determined
- C. for each class entire covariance matrix need to be determined
- D. it only requires a small amount of training data to estimate the parameters
Answer: A,B,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 56
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. Discovery
- C. Operationalize
- D. Model building
- E. Model planning
Answer: D
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 57
Logistic regression is a model used for prediction of the probability of occurrence of an event. It makes use of several variables that may be......
- A. Both 1 and 2 are correct
- B. Categorical
- C. Numerical
- D. None of the 1 and 2 are correct
Answer: A
Explanation:
Explanation
Logistic regression is a model used for prediction of the probability of occurrence of an event. It makes use of several predictor variables that may be either numerical or categories.
NEW QUESTION 58
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. Recommendation system
- B. Maximum likelihood estimation
- C. Linear Regression
- D. Logistic Regression
- E. Hierarchical linear models
Answer: D
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 59
Select the correct objectives of principal component analysis
- A. All 1, 2 and 3
- B. To identify new meaningful underlying variables
- C. To discover the dimensionality of the data set
- D. To reduce the dimensionality of the data set
- E. Only 1 and 2
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.
Objectives of principal component analysis
1. To discover or to reduce the dimensionality of the data set.
2. To identify new meaningful underlying variables.
NEW QUESTION 60
RMSE measures error of a predicted
- A. For booth Numerical and categorical values
- B. Categorical values
- C. Numerical Value
Answer: C
NEW QUESTION 61
If you are trying to predict or forecast a discrete target value, then which is the correct options
- A. Density estimation algorithm
- B. Un supervised Learning
- C. Supervised Learning regression algorithms
- D. Supervised Learning classification algorithms
Answer: D
Explanation:
Explanation
If you're trying to predict or forecast a target value, then you need to look into supervised learning. If not, then unsupervised learning is the place you want to be. If you've chosen supervised learning, what's your target value? Is it a discrete value like Yes/No: 1/2/3, A/B/C: or Red/Yellow/Black? If so: then you want to look into classification. If the target value can take on a number of values, say any value from 0.00 to 100.00: or-999 to
999, or +_to -_, then you need to look into regression.
NEW QUESTION 62
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
- A. Logistic Regression
- B. Clustering
- C. PCA (Principal component analysis)
- D. Linear Regression
Answer: C
Explanation:
Explanation
Principal component analysis . or PCA, is a technique for taking a dataset that is in the form of a set of tuples representing points in a high-dimensional space and finding the dimensions along which the tuples line up best. The idea is to treat the set of tuples as a matrix M and find the eigenvectors for MMT or M T M . The matrix of these eigenvectors can be thought of as a rigid rotation in a high-dimensional space. When you apply this transformation to the original data, the axis corresponding to the principal eigenvector is the one along which the points are most "spread out,11 More precisely this axis is the one along which the variance of the data is maximized. Put another way, the points can best be viewed as lying along this axis, with small deviations from this axis.
NEW QUESTION 63
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.
- A. All of the above
- B. Support vector machines
- C. Random decision forests
- D. Naive Bayes
- E. Logistic regression
Answer: A
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 64
Which of the following are point estimation methods?
- A. MMSE
- B. MLE
- C. MAP
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 65
You are studying the behavior of a population, and you are provided with multidimensional data at the individual level. You have identified four specific individuals who are valuable to your study, and would like to find all users who are most similar to each individual. Which algorithm is the most appropriate for this study?
- A. Association rules
- B. Linear regression
- C. Decision trees
- D. K-means clustering
Answer: D
Explanation:
Explanation
kmeans uses an iterative algorithm that minimizes the sum of distances from each object to its cluster centroid, over all clusters. This algorithm moves objects between clusters until the sum cannot be decreased further. The result is a set of clusters that are as compact and well-separated as possible. You can control the details of the minimization using several optional input parameters to kmeans, including ones for the initial values of the cluster centroids, and for the maximum number of iterations.
Clustering is primarily an exploratory technique to discover hidden structures of the data: possibly as a prelude to more focused analysis or decision processes. Some specific applications of k-means are image processing^ medical and customer segmentation. Clustering is often used as a lead-in to classification. Once the clusters are identified, labels can be applied to each cluster to classify each group based on its characteristics. Marketing and sales groups use k-means to better identify customers who have similar behaviors and spending patterns.
NEW QUESTION 66
Under which circumstance do you need to implement N-fold cross-validation after creating a regression model?
- A. There are missing values in the data.
- B. There are categorical variables in the model.
- C. The data is unformatted.
- D. There is not enough data to create a test set.
Answer: D
NEW QUESTION 67
As a data scientist consultant at ABC Corp, you are working on a recommendation engine for the learning resources for end user. So Which recommender system technique benefits most from additional user preference data?
- A. Logistic Regression
- B. Naive Bayes classifier
- C. Content-based filtering
- D. Item-based collaborative filtering
Answer: D
Explanation:
Explanation
Item-based scales with the number of items, and user-based scales with the number of users you have. If you have something like a store, you'll have a few thousand items at the most. The biggest stores at the time of writing have around 100,000 items. In the Netflix competition, there were 480,000 users and 17,700 movies. If you have a lot of users: then you'll probably want to go with item-based similarity. For most product-driven recommendation engines, the number of users outnumbers the number of items. There are more people buying items than unique items for sale. Item-based collaborative filtering makes predictions based on users preferences for items. More preference data should be beneficial to this type of algorithm. Content-based filtering recommender systems use information about items or users, and not user preferences, to make recommendations. Logistic Regression, Power iteration and a Naive Bayes classifier are not recommender system techniques.
NEW QUESTION 68
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. 24 0
- B. .48
- C. 2.4
- D. .24
- E. 4.8
Answer: D
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 69
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?
- A. Binomial Distribution
- B. Bays Theorem
- C. Poisson Distribution
- D. None of the above
Answer: B
NEW QUESTION 70
Select the correct statement which applies to K-Nearest Neighbors
- A. Require less memory
- B. No Assumption about the data
- C. Computationally expensive
- D. Works with Numeric Values
Answer: B,C,D
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 71
In statistics, maximum-likelihood estimation (MLE) is a method of estimating the parameters of a statistical model. When applied to a data set and given a statistical model, maximum-likelihood estimation provides estimates for the model's parameters and the normalizing constant usually ignored in MLEs because
- A. The normalizing constant is always very close to 1
- B. The normalizing constant only has a small impact on the maximum likelihood
- C. The normalizing constant doesn't impact the maximizing value
- D. The normalizing constant is often zero and can cause division by zero
Answer: C
Explanation:
Explanation
(Change the explanation even it is correct)A normalizing constant is positive, and multiplying or dividing a series of values by a positive number does not affect which of them is the largest. Maximum likelihood estimation is concerned only with finding a maximum value, so normalizing constants can be ignored.
NEW QUESTION 72
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?
- A. It is hard to understand what classifier is doing
- B. Is a problem with accuracy
- C. It is easy to understand what classifier is doing
- D. Is a problem with accuracy as well as hard to understand what classifier us doing
Answer: A
Explanation:
Explanation
FEATURE HASHING
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. This approach is known as feature hashing. The shoehorning is done by picking one or more locations by using a hash of the name of the variable for continuous variables or a hash of the variable name and the category name or word for categorical, text*like, or word-like data.
This hashed feature approach has the distinct advantage of requiring less memory and one less pass through the training data, but it can make it much harder to reverse engineer vectors to determine which original feature mapped to a vector location. This is because multiple features may hash to the same location. With large vectors or with multiple locations per feature, this isn't a problem for accuracy but it can make it hard to understand what a classifier is doing.
An additional benefit of feature hashing is that the unknown and unbounded vocabularies typical of word-like variables aren't a problem.
NEW QUESTION 73
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