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Databricks Databricks-Certified-Professional-Data-Scientist Exam Syllabus Topics:
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NEW QUESTION 10
In which of the scenario you can use the regression to predict the values
- A. All 1 ,2 and 3
- B. Samsung can use it for mobile sales forecast
- C. Probability of the celebrity divorce
- D. Mobile companies can use it to forecast manufacturing defects
- E. Only 1 and 2
Answer: A
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 11
What are the advantages of the Hashing Features?
- A. Requires the less memory
- B. Less pass through the training data
- C. Easily reverse engineer vectors to determine which original feature mapped to a vector location
Answer: A,B
Explanation:
Explanation
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 12
Which method is used to solve for coefficients bO, b1, ... bn in your linear regression model:
- A. Apriori Algorithm
- B. Ordinary Least squares
- C. Ridge and Lasso
- D. Integer programming
Answer: B
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 13
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. n/2
- B. 0
- C. 1
- D. 2
Answer: D
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 14
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. 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 square can be increased by adding more variables to the model.
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 15
You are doing advanced analytics for the one of the medical application using the regression and you have two variables which are weight and height and they are very important input variables, which cannot be ignored and they are also highly co-related. What is the best solution for that?
- A. You will take square root of weight
- B. You would consider using BMI (Body Mass Index)
- C. You will take cube root of height
- D. You will take square of the height.
Answer: B
Explanation:
Explanation
If multiple variables are highly co-related then it is better you consider using the either of the variable which correlates more (which is not in the given option) or go for the new variable which is a function of the both the variable in this case it could be BMI (Body Mass Index). Because it is a function of both weight and height as per the below formula. BMI = Weight/(Height * Height)
NEW QUESTION 16
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 17
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. 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.
- B. marginal probability that P(H=Not Hit) is the sum of the H=Not Hit row
- C. marginal probability that P(H=Not Hit) is the sum of the H= Hit row
Answer: A,B
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 18
Which of the following true with regards to the K-Means clustering algorithm?
- A. It discovers the center of each cluster.
- B. It find each objects fall in which particular cluster
- C. Labels are pre-assigned to each objects in the cluster.
- D. It classify the data based on the labels.
- E. Labels are not pre-assigned to each objects in the cluster.
Answer: A,B,E
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 19
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. PCA (Principal component analysis)
- B. Linear Regression
- C. Logistic Regression
- D. Clustering
Answer: A
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 20
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. Linear Regression
- B. Maximum likelihood estimation
- C. Logistic Regression
- D. Hierarchical linear models
- E. Recommendation system
Answer: C
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 21
Marie is getting married tomorrow, at an outdoor ceremony in the desert. In recent years, it has rained only 5 days each year. Unfortunately, the weatherman has predicted rain for tomorrow. When it actually rains, the weatherman correctly forecasts rain 90% of the time. When it doesn't rain, he incorrectly forecasts rain 10% of the time. Which of the following will you use to calculate the probability whether it will rain on the day of Marie's wedding?
- A. All of the above
- B. Naive Bayes
- C. Logistic Regression
- D. Random Decision Forests
Answer: B
Explanation:
Explanation
The sample space is defined by two mutually-exclusive events - it rains or it does not rain. Additionally, a third event occurs when the weatherman predicts rain. You should consider Bayes' theorem when the following conditions exist.
* The sample space is partitioned into a set of mutually exclusive events {A1, A2,... :An}.
* Within the sample space, there exists an event B: for which P(B) > 0.
* The analytical goal is to compute a conditional probability of the form: P( Ak B).
NEW QUESTION 22
Which of the following question statement falls under data science category?
- A. What happened in last six months?
- B. Where is a problem for sales?
- C. Which is the optimal scenario for selling this product?
- D. What happens, if these scenario continues?
- E. How many products have been sold in a last month?
Answer: C,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 23
Select the correct statement which applies to K-Nearest Neighbors
- A. Works with Numeric Values
- B. Computationally expensive
- C. No Assumption about the data
- 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 24
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 clusters
- C. Decrease the number of measures used
- D. Identify additional measures to add to the analysis
Answer: B
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 25
Clustering is a type of unsupervised learning with the following goals
- A. 1 and 2
- B. Not to maximize a utility function
- C. 2 and 3
- D. Find similarities in the training data
- E. Maximize a utility function
Answer: C
Explanation:
Explanation
type of unsupervised learning is called clustering. In this type of learning, The goal is not to maximize a utility function, but simply to find similarities in the training data.
The assumption is often that the clusters discovered will match reasonably well with an intuitive classification.
For instance, clustering individuals based on demographics might result in a clustering of the wealthy in one group and the poor in another. Clustering can be useful when there is enough data to form clusters (though this turns out to be difficult at times) and especially when additional data about members of a cluster can be used to produce further results due to dependencies in the data.
NEW QUESTION 26
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. Independent variables can be assumed
- D. it only requires a small amount of training data to estimate the parameters
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 27
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. Linear Regression
- B. Logistic Regression
- C. ARIMA
- D. Decision Trees
Answer: D
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 28
RMSE is a useful metric for evaluating which types of models?
- A. Naive Bayes classifier
- B. All of the above
- C. Logistic regression
- D. Linear regression
Answer: D
Explanation:
Explanation
Error calculation allows you to see how well a machine learning
method is performing.
One way of determining this performance is to calculate a numerical error This number is sometimes a percent, however it can also be a score or distance. The goal is usually to minimize an error percent or distance:
however th goal may be to minimize or maximize a score. Encog supports the following error calculation methods.
Sum of Squares Error (ESS)
Root Mean Square Error (RMS)
Mean Square Error (MSE) (default)
SOM Error (Euclidean Distance Error)
RMSE measures error of a predicted numeric value, and so applies to contexts like regression and some recommender system techniques, which rely on predicting a numeric value. It is not relevant to classification techniques like logistic regression and Naive Bayes, which predict categorical values.
It also is not relevant to unsupervied techniques like clustering.
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.
NEW QUESTION 29
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
- A. It means that your predictions are on average three star off of what people really think
- B. It means that your predictions are on average two star off of what people really think
- C. It means that your predictions are on average four star off of what people really think
- D. It means that your predictions are on average one star off of what people really think
Answer: D
NEW QUESTION 30
Assume some output variable "y" is a linear combination of some independent input variables "A" plus some independent noise "e". The way the independent variables are combined is defined by a parameter vector B y=AB+e where X is an m x n matrix. B is a vector of n unknowns, and b is a vector of m values. Assuming that m is not equal to n and the columns of X are linearly independent, which expression correctly solves for B?
- A. Option A
- B. Option C
- C. Option B
- D. Option D
Answer: D
Explanation:
Explanation
This is the standard solution of the normal equations for linear regression. Because A is not square, you cannot simply take its inverse.
NEW QUESTION 31
Which of the following are advantages of the Support Vector machines?
- A. Effective in cases where number of dimensions is greater than the number of samples
- B. Number of features is much greater than the number of samples, the method still give good performances
- C. SVMs directly provide probability estimates
- D. Effective in high dimensional spaces.
- E. possible to specify custom kernels
- F. it is memory efficient
Answer: A,D,E,F
Explanation:
Explanation
Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection.
The advantages of support vector machines are:
Effective in high dimensional spaces.
Still effective in cases where number of dimensions is greater than the number of samples.
Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.
Versatile: different Kernel functions can be specified for the decision function.
Common kernels are provided, but it is also possible to specify custom kernels.
The disadvantages of support vector machines include:
If the number of features is much greater than the number of samples, the method is likely to give poor performances.
SVMs do not directly provide probability estimates, these are calculated using an expensive five-fold cross-validation.
NEW QUESTION 32
You have modeled the datasets with 5 independent variables called A,B,C,D and E having relationships which is not dependent each other, and also the variable A,B and C are continuous and variable D and E are discrete (mixed mode).
Now you have to compute the expected value of the variable let say A, then which of the following computation you will prefer
- A. Transformation
- B. Differentiation
- C. Integration
- D. Generalization
Answer: C
Explanation:
Explanation
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NEW QUESTION 33
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