[Oct-2021] Updated Databricks Certification Databricks-Certified-Professional-Data-Scientist Exam Questions BUNDLE PACK
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NEW QUESTION 59
What type of output generated in case of linear regression?
- A. Values between 0 and 1
- B. Any of the Continuous and Discrete variable
- C. Discrete Variable
- D. Continuous variable
Answer: D
Explanation:
Explanation
Linear regression model generate continuous output variable.
NEW QUESTION 60
Which of the following is not a correct application for the Classification?
- A. credit scoring
- B. image recognition
- C. drug discovery
- D. tumor detection
Answer: C
Explanation:
Explanation
Classification : Build models to classify data into different categories credit scoring, tumor detection, image recognition Regression: Build models to predict continuous data, electricity load forecasting, algorithmic trading, drug discovery
NEW QUESTION 61
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. R-squared never decreases upon adding more independent variables.
- C. When R square =1 , all the residuals are equal to 0
- D. When R square =0, all the residual are equal to 1
Answer: A,B,C
NEW QUESTION 62
Which of the following metrics are useful in measuring the accuracy and quality of a recommender system?
- A. Cluster Density
- B. Support Vector Count
- C. Sum of Absolute Errors
- D. Mean Absolute Error
Answer: D
Explanation:
Explanation
The MAE measures the average magnitude of the errors in a set of forecasts, without considering their direction. It measures accuracy for continuous variables. The equation is given in the library references.
Expressed in words, the MAE is the average over the verification sample of the absolute values of the differences between forecast and the corresponding observation. The MAE is a linear score which means that all the individual differences are weighted equally in the average.
The sum of absolute errors is a valid metric, but doesn't give any useful sense of how the recommender system is performing.
Support vector count and cluster density do not apply to recommender systems.
MAE and AUC are both valid and useful metrics for measuring recommender systems.
NEW QUESTION 63
Select the correct statement which applies to Supervised learning
- A. Lesser machine's task to only divining some pattern from the input data to get the target variable
- B. We asks the machine to learn from our data when we specify a target variable.
- C. Instead of telling the machine Predict Y for our data X, we're asking What can you tell me about X?
Answer: A,B,C
Explanation:
Explanation : Supervised learning asks the machine to learn from our data when we specify a target variable.
This reduces the machine's task to only divining some pattern from the input data to get the target variable.
In unsupervised learning we don't have a target variable as we did in classification and regression.
Instead of telling the machine Predict Y for our data X> we're asking What can you tell me about X?
Things we ask the machine to tell us about
X may be What are the six best groups we can make out of X? or What three features occur together most frequently in X?
NEW QUESTION 64
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. Logistic Regression
- B. Naive Bayes
- C. All of the above
- 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 65
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 lowest cost clustering subject to a stability constraint
- C. The lowest cost clustering
- D. The most stable clustering
Answer: B
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 66
Which of the following technique can be used to the design of recommender systems?
- A. Power iteration
- B. Collaborative filtering
- C. 1 and 3
- D. 2 and 3
- E. Naive Bayes classifier
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 67
What are the key outcomes of the successful analytical projects?
- A. Presentation for Project Sponsors
- B. Presentations for the Analysts
- C. Code of the model
- D. Technical specifications
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 68
In which of the scenario you can use the linear regression model?
- A. Predicting demand of the goods and services based on the weather
- B. Predicting sales of the text book based on the number of students in state
- C. Predicting tumor size reduction based on input as number of radiation treatment
- D. Predicting Home Price based on the location and house area
Answer: A,B,C,D
Explanation:
Explanation : You can use the linear regression model for predicting the continuous output variable based on the input variables. In all the cases mentioned in the question option, you can see that output can be predicted based on the input variable.
Option-A: Input: Location, House Area and Output: House Price
Option-B : Input: Weather condition, Output: Demand for the goods and services Option-C : Input: Number of Radiation Session Output: Tumor Size Reduction Option-D : Input: Number of students and Output: Sale quantity of text book
NEW QUESTION 69
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 70
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. Logistic Regression
- B. Linear 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 71
A website is opened 3 times by a user. What is the probability of he clicks 2 times the advertisement, is best calculated by
- A. Binomial
- B. Any of the above
- C. Poisson
- D. Normal
Answer: A
Explanation:
Explanation
In a binomial distribution, only 2 parameters, namely n and p, are needed to determine the probability. Where p is the probability of success and q is the probability of failure in a binomial trial, then the expected number of successes in n trials.
This is a binomial distribution because there are only 2 possible outcomes (we get a 5 or we don't).
NEW QUESTION 72
Which of the following steps you will be using in the discovery phase?
- A. What all are the data sources for the project?
- B. What all tools are required, in the project?
- C. What is the network capacity required
- D. Analyze the Raw data and its format and structure.
- E. What Unix server capacity required?
Answer: A,B,C,D,E
Explanation:
Explanation
During the discovery phase you need to find how much resources are required as early as possible and for that even you can involve various stakeholders like Software engineering team, DBAs, Network engineers, System administrators etc. for your requirement and these resources are already available or you need to procure them. Also, what would be source of the data? What all tools and software's are required to execute the same?
NEW QUESTION 73
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. Linear Regression
- C. Logistic Regression
- D. Association Rules
Answer: C
NEW QUESTION 74
The method based on principal component analysis (PCA) evaluates the features according to
- A. The projection of the smallest eigenvector of the correlation matrix on the initial dimensions
- B. According to the magnitude of the components of the discriminate vector
- C. None of the above
- D. The projection of the largest eigenvector of the correlation matrix on the initial dimensions
Answer: D
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 75
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 76
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 D
- C. Option B
- D. Option C
Answer: B
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 77
Which of the following is a Continuous Probability Distributions?
- A. Negative binomial distribution
- B. Normal probability distribution
- C. Binomial probability distribution
- D. Poisson probability distribution
Answer: B
NEW QUESTION 78
Select the correct algorithm of unsupervised algorithm
- A. K-Means
- B. K-Nearest Neighbors
- C. Naive Bayes
- 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 79
In unsupervised learning which statements correctly applies
- A. telling the machine Predict Y for our data X
- B. Instead of telling the machine Predict Y for our data X, we're asking What can you tell me about X?
- C. It does not have a target variable
Answer: B,C
Explanation:
Explanation
In unsupervised learning we don't have a target variable as we did in
classification and regression.
Instead of telling the machine Predict Y for our data X, we're asking What can you tell me about X?
Things we ask the machine to tell us about
X may be What are the six best groups we can make out of X? or What three features occur together most frequently in X?
NEW QUESTION 80
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 81
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 82
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