Databricks-Certified-Professional-Data-Scientist Questions Pass on Your First Attempt Dumps for Databricks Certification Certified
Databricks-Certified-Professional-Data-Scientist Practice Test Pdf Exam Material
Databricks Databricks-Certified-Professional-Data-Scientist Exam Syllabus Topics:
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NEW QUESTION 76
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. None of the above
- B. Bays Theorem
- C. Poisson Distribution
- D. Binomial Distribution
Answer: B
NEW QUESTION 77
Classification and regression are examples of___________.
- A. Clustering
- B. Density estimation
- C. un-supervised learning
- D. supervised learning
Answer: D
Explanation:
Explanation
In classification, our job is to predict what class an instance of data should fall into. Another task in machine learning is regression. Regression is the prediction of a numeric value. Most people have probably seen an example of regression with a best-fit line drawn through some data points to generalize the data points.
Classification and regression are examples of supervised learning. This set of problems is known as supervised because we're telling the algorithm what to predict.
NEW QUESTION 78
Spam filtering of the emails is an example of
- A. 1 and 3 are correct
- B. Clustering
- C. Unsupervised learning
- D. Supervised learning
- E. 2 and 3 are correct
Answer: D
Explanation:
Explanation
Clustering is an example of unsupervised learning. The clustering algorithm finds groups within the data without being told what to look for upfront. This contrasts with classification, an example of supervised machine learning, which is the process of determining to which class an observation belongs. A common application of classification is spam filtering. With spam filtering we use labeled data to train the classifier:
e-mails marked as spam or ham.
NEW QUESTION 79
Let's say you have two cases as below for the movie ratings
1. You recommend to a user a movie with four stars and he really doesn't like it and he'd rate it two stars
2. You recommend a movie with three stars but the user loves it (he'd rate it five stars). So which statement correctly applies?
- A. In both cases, the contribution to the RMSE is the same
- B. None of the above
- C. In both cases, the contribution to the RMSE is the different
- D. In both cases, the contribution to the RMSE, could varies
Answer: A
NEW QUESTION 80
Which of the following statement is true for the R square value in the regression model?
- A. R square can be increased by adding more variables to the model.
- B. When R square =0, all the residual are equal to 1
- C. When R square =1 , all the residuals are equal to 0
- D. R-squared never decreases upon adding more independent variables.
Answer: A,C,D
Explanation:
Explanation
R square can be made high, it means when we add more variables R-square will increase. And R-square will never decreases if you add more independent variables. Higher R square value can have lower the residuals.
NEW QUESTION 81
A data scientist is asked to implement an article recommendation feature for an on-line magazine.
The magazine does not want to use client tracking technologies such as cookies or reading history. Therefore, only the style and subject matter of the current article is available for making recommendations. All of the magazine's articles are stored in a database in a format suitable for analytics.
Which method should the data scientist try first?
- A. Naive Bayesian
- B. Association Rules
- C. K Means Clustering
- D. Logistic Regression
Answer: C
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 82
Which of the following true with regards to the K-Means clustering algorithm?
- A. It discovers the center of each cluster.
- B. Labels are not pre-assigned to each objects in the cluster.
- C. It find each objects fall in which particular cluster
- D. It classify the data based on the labels.
- E. Labels are pre-assigned to each objects in the cluster.
Answer: A,B,C
Explanation:
Explanation
Clustering does not require any predefined labels on the object, rather it consider the attributes on the object.
Hence, option-B is out. Clustering is different than classification technique.
Hence you can discard the option-C as well. It does not use the pre-defined labels, hence it is called unsupervised learning and option-Ais correct. Main purpose of the Clustering technique is to determine the center of each Cluster and then find the distance from that center. If object is near the center than it would fall in that particular cluster. Hence, finally you will have group or clusters created and get to know that objects fall in which particular cluster.
NEW QUESTION 83
Consider flipping a coin for which the probability of heads is p, where p is unknown, and our goa is to estimate p. The obvious approach is to count how many times the coin came up heads and divide by the total number of coin flips. If we flip the coin 1000 times and it comes up heads 367 times, it is very reasonable to estimate p as approximately 0.367. However, suppose we flip the coin only twice and we get heads both times.
Is it reasonable to estimate p as 1.0? Intuitively, given that we only flipped the coin twice, it seems a bit rash to conclude that the coin will always come up heads, and____________is a way of avoiding such rash conclusions.
- A. Naive Bayes
- B. Laplace Smoothing
- C. Linear Regression
- D. Logistic Regression
Answer: B
Explanation:
Explanation
Smooth the estimates: consider flipping a coin for which the probability of heads is p, where p is unknown, and our goal is to estimate p. The obvious approach is to count how many times the coin came up heads and divide by the total number of coin flips. If we flip the coin 1000 times and it comes up heads 367 times, it is very reasonable to estimate p as approximately 0.367. However, suppose we flip the coin only twice and we get heads both times. Is it reasonable to estimate p as 1.0? Intuitively, given that we only flipped the coin twice, it seems a bit rash to conclude that the coin will always come up heads, and smoothing is a way of avoiding such rash conclusions. A simple smoothing method, called Laplace smoothing (or Laplace's law of succession or add-one smoothing in R&N), is to estimate p by (one plus the number of heads) / (two plus the total number of flips). Said differently, if we are keeping count of the number of heads and the number of tails, this rule is equivalent to starting each of our counts at one, rather than zero. Another advantage of Laplace smoothing is that it avoids estimating any probabilities to be zero, even for events never observed in the data.
Laplace add-one smoothing now assigns too much probability to unseen words
NEW QUESTION 84
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 often zero and can cause division by zero
- B. The normalizing constant doesn't impact the maximizing value
- C. The normalizing constant is always very close to 1
- D. The normalizing constant only has a small impact on the maximum likelihood
Answer: B
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 85
What is the best way to evaluate the quality of the model found by an unsupervised algorithm like k-means clustering, given metrics for the cost of the clustering (how well it fits the data) and its stability (how similar the clusters are across multiple runs over the same data)?
- A. The most stable clustering subject to a minimal cost constraint
- B. The most stable clustering
- C. The lowest cost clustering
- D. The lowest cost clustering subject to a stability constraint
Answer: D
Explanation:
Explanation
There is a tradeoff between cost and stability in unsupervised learning. The more tightly you fit the data, the less stable the model will be, and vice versa. The idea is to find a good balance with more weight given to the cost. Typically a good approach is to set a stability threshold and select the model that achieves the lowest cost above the stability threshold.
NEW QUESTION 86
While working with Netflix the movie rating websites you have developed a recommender system that has produced ratings predictions for your data set that are consistently exactly 1 higher for the user-item pairs in your dataset than the ratings given in the dataset. There are n items in the dataset. What will be the calculated RMSE of your recommender system on the dataset?
- A. 0
- B. 1
- C. n/2
- D. 2
Answer: A
Explanation:
Explanation
The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a frequently used measure of the differences between values predicted by a model or an estimator and the values actually observed.
Basically, the RMSD represents the sample standard deviation of the differences between predicted values and observed values. These individual differences are called residuals when the calculations are performed over the data sample that was used for estimation, and are called prediction errors when computed out-of-sample.
The RMSD serves to aggregate the magnitudes of the errors in predictions for various times into a single measure of predictive power. RMSD is a good measure of accuracy, but only to compare forecasting errors of different models for a particular variable and not between variables, as it is scale-dependent. RMSE is calculated as the square root of the mean of the squares of the errors. The error in every case in this example is
1. The square of 1 is 1 The average of n items with value 1 is 1 The square root of 1 is 1 The RMSE is therefore 1
NEW QUESTION 87
You are working in an ecommerce organization, where you are designing and evaluating a recommender system, you need to select which of the following metric wilt always have the largest value?
- A. Root Mean Square Error
- B. Both land 2
- C. Sum of Errors
- D. Information is not good enough.
- E. Mean Absolute Error
Answer: D
NEW QUESTION 88
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. 4.8
- B. 24 0
- C. .24
- D. 2.4
- E. .48
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 89
Select the choice where Regression algorithms are not best fit
- A. Employee status
- B. Weight of the person is given
- C. Temperature in the atmosphere
- D. When the dimension of the object given
Answer: A
Explanation:
Explanation
Regression algorithms are usually employed when the data points are inherently numerical variables (such as the dimensions of an object the weight of a person, or the temperature in the atmosphere) but unlike Bayesian algorithms, they're not very good for categorical data (such as employee status or credit score description).
NEW QUESTION 90
What is one modeling or descriptive statistical function in MADlib that is typically not provided in a standard relational database?
- A. Expected value
- B. Quantiles
- C. Linear regression
- D. Variance
Answer: C
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 91
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?
- A. Increase the number of clusters
- B. Decrease the number of measures used
- C. Decrease the number of clusters
- D. Identify additional measures to add to the analysis
Answer: C
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 92
In which of the following scenario we can use naTve Bayes theorem for classification
- A. To identify whether a fruit is an orange or not based on features like diameter, color and shape
- B. To classify whether an email is spam or not spam
- C. Classify whether a given person is a male or a female based on the measured features. The features include height, weight and foot size.
Answer: A,B,C
Explanation:
Explanation
naive Bayes classifiers have worked quite well in many real-world situations, famously document classification and spam filtering. They requires a small amount of training data to estimate the necessary parameters
NEW QUESTION 93
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. Decision trees
- B. Linear regression
- C. K-means clustering
- D. Association rules
Answer: C
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 94
What is the considerable difference between L1 and L2 regularization?
- A. L2-regularization can be of vital importance when the application is deployed in resource-tight environments such as cell-phones.
- B. Size of the model can be much smaller in L1 regularization than that produced by L2-regularization
- C. All of the above are correct
- D. L1 regularization has more accuracy of the resulting model
Answer: B
Explanation:
Explanation
The two most common regularization methods are called L1 and L2 regularization. L1 regularization penalizes the weight vector for its L1-norm (i.e. the sum of the absolute values of the weights), whereas L2 regularization uses its L2-norm. There is usually not a considerable difference between the two methods in terms of the accuracy of the resulting model (Gao et al 2007), but L1 regularization has a significant advantage in practice. Because many of the weights of the features become zero as a result of L1-regularized training, the size of the model can be much smaller than that produced by L2-regularization. Compact models require less space on memory and storage, and enable the application to start up quickly. These merits can be of vital importance when the application is deployed in resource-tight environments such as cell-phones.
Regularization works by adding the penalty associated with the coefficient values to the error of the hypothesis. This way, an accurate hypothesis with unlikely coefficients would be penalized whila a somewhat less accurate but more conservative hypothesis with low coefficients would not be penalized as much.
81
NEW QUESTION 95
Select the correct objectives of principal component analysis
- A. To identify new meaningful underlying variables
- B. Only 1 and 2
- C. To discover the dimensionality of the data set
- D. To reduce the dimensionality of the data set
- E. All 1, 2 and 3
Answer: E
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 96
You are creating a regression model with the input income, education and current debt of a customer, what could be the possible output from this model.
- A. 1 and 3 are correct
- B. expressed as a percent, that the customer will default on a loan
- C. Customer fit as acceptable or average category
- D. Customer fit as a good
- E. 2 and 3 are correct
Answer: B
Explanation:
Explanation
Regression 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 the probability, expressed as a percent that the customer will default on a loan. Contrast this to classification where the output is not a number, but a class.
NEW QUESTION 97
Which of the following is a Continuous Probability Distributions?
- A. Poisson probability distribution
- B. Negative binomial distribution
- C. Normal probability distribution
- D. Binomial probability distribution
Answer: C
NEW QUESTION 98
Google Adwords studies the number of men, and women, clicking the advertisement on search engine during the midnight for an hour each day.
Google find that the number of men that click can be modeled as a random variable with distribution Poisson(X), and likewise the number of women that click as Poisson(Y).
What is likely to be the best model of the total number of advertisement clicks during the midnight for an hour
?
- A. Poisson(X+Y)
- B. Binomial(X+Y,X+Y)
- C. Normal(X+Y(M+Y)1/2)
- D. Poisson(X/Y)
Answer: A
Explanation:
Explanation
The total number of clicks is the sum of the number of men and
women. The sum of two Poisson random variables also follows a Poisson distribution with rate equal to the sum of their rates.
The Normal and Binomial distribution can approximate the Poisson distribution in certain cases, but the expressions above do not approximate Poisson(X+Y).
NEW QUESTION 99
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