[Sep 07, 2022] Latest Databricks-Certified-Professional-Data-Scientist Exam with Accurate Databricks Certified Professional Data Scientist Exam PDF Questions
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NEW QUESTION 13
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 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.
- C. You cannot discard any variable for creating clusters.
- D. You can combine several variables in one variable
- E. You will find the correlation among the variables and from their variables are not co-related will be discarded.
Answer: B,D
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 14
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. Recommendation system
- C. Logistic Regression
- D. Maximum likelihood estimation
- E. Hierarchical linear models
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 15
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. Instead of telling the machine Predict Y for our data X, we're asking What can you tell me about X?
- C. We asks the machine to learn from our data when we specify a target variable.
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 16
Select the correct problems which can be solved using SVMs
- A. Hand-written characters can be recognized using SVM
- B. SVMs are helpful in text and hypertext categorization
- C. Classification of images can also be performed using SVMs
- D. SVMs are also useful in medical science to classify proteins with up to 90% of the compounds classified correctly
Answer: A,B,C,D
Explanation:
Explanation
SVMs can be used to solve various real world problems:
* SVMs are helpful in text and hypertext categorization as their application can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings.
* Classification of images can also be performed using SVMs. Experimental results show that SVMs achieve significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.
* SVMs are also useful in medical science to classify proteins with up to 90% of the compounds classified correctly.
* Hand-written characters can be recognized using SVM
NEW QUESTION 17
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. Linear Regression
- B. Association Rules
- C. Logistic Regression
- D. Discriminant Analysis
Answer: C
NEW QUESTION 18
You are working in a classification model for a book, written by HadoopExam Learning Resources and decided to use building a text classification model for determining whether this book is for Hadoop or Cloud computing. You have to select the proper features (feature selection) hence, to cut down on the size of the feature space, you will use the mutual information of each word with the label of hadoop or cloud to select the 1000 best features to use as input to a Naive Bayes model. When you compare the performance of a model built with the 250 best features to a model built with the 1000 best features, you notice that the model with only 250 features performs slightly better on our test data.
What would help you choose better features for your model?
- A. Include the number of times each of the words appears in the book in your model
- B. Decrease the size of our training data
- C. Include least mutual information with other selected features as a feature selection criterion
- D. Evaluate a model that only includes the top 100 words
Answer: C
Explanation:
Explanation
Correlation measures the linear relationship (Pearson's correlation) or monotonic relationship (Spearman's correlation) between two variables, X and Y.
Mutual information is more general and measures the reduction of uncertainty in Y after observing X.
It is the KL distance between the joint density and the product of the individual densities. So Ml can measure non-monotonic relationships and other more complicated relationships Mutual information is a quantification of the dependency between random variables. It is sometimes contrasted with linear correlation since mutual information captures nonlinear dependence.
Features with high mutual information with the predicted value are good. However a feature may have high mutual information because it is highly correlated with another feature that has already been selected.
Choosing another feature with somewhat less mutual information with the predicted value, but low mutual information with other selected features, may be more beneficial. Hence it may help to also prefer features that are less redundant with other selected features.
NEW QUESTION 19
Refer to image below
- A. Option C
- B. Option A
- C. Option D
- D. Option B
Answer: B
Explanation:
Explanation
Text Description automatically generated
NEW QUESTION 20
In which lifecycle stage are appropriate analytical techniques determined?
- A. Data preparation
- B. Model planning
- C. Model building
- D. Discovery
Answer: B
Explanation:
Explanation
In Phase 3, the data science team identifies candidate models to apply to the data for clustering, classifying, or finding relationships in the data depending on the goal of the project, It is during this phase that the team refers to the hypotheses developed in Phase 1, when they first became acquainted with the data and understanding the business problems or domain area. These hypotheses help the team frame the analytics to execute in Phase
4 and select the right methods to achieve its objectives.
Some of the activities to consider in this phase include the following: Assess the structure of the datasets. The structure of the datasets is one factor that dictates the tools and analytical techniques for the next phase.
Depending on whether the team plans to analyze textual data or transactional data, for example, different tools and approaches are required.
Ensure that the analytical techniques enable the team to meet the business objectives and accept or reject the working hypotheses. Determine if the situation warrants a single model or a series of techniques as part of a larger analytic workflow. A few example models include association rules and logistic regression Other tools, such as Alpine Miner, enable users to set up a series of steps and analyses and can serve as a front-end user interface (Ul) for manipulating Big Data sources in PostgreSQL.
NEW QUESTION 21
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 22
What describes a true property of Logistic Regression method?
- A. It works well with variables that affect the outcome in a discontinuous way.
- B. It is robust with redundant variables and correlated variables.
- C. It handles missing values well.
- D. It works well with discrete variables that have many distinct values.
Answer: B
NEW QUESTION 23
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 C
- B. Option B
- C. Option D
- D. Option A
Answer: C
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 24
A bio-scientist is working on the analysis of the cancer cells. To identify whether the cell is cancerous or not, there has been hundreds of tests are done with small variations to say yes to the problem. Given the test result for a sample of healthy and cancerous cells, which of the following technique you will use to determine whether a cell is healthy?
- A. Linear regression
- B. Naive Bayes
- C. Collaborative filtering
- D. Identification Test
Answer: B
Explanation:
Explanation
In this problem you have been given high-dimensional independent variables like yes, no: 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 25
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. How many products have been sold in a last month?
- D. What happened in last six months?
- E. What happens, if these scenario continues?
Answer: B,E
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 26
A researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don't admit, is a binary variable.
Above is an example of
- A. Linear Regression
- B. Recommendation system
- C. Logistic Regression
- D. Maximum likelihood estimation
- E. Hierarchical linear models
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 27
Reducing the data from many features to a small number so that we can properly visualize it in two or three dimensions. It is done in_______
- A. Support vector machines
- B. supervised learning
- C. un-supervised learning
- D. k-Nearest Neighbors
Answer: C
Explanation:
Explanation
The opposite of supervised learning is a set of tasks known as unsupervised learning. In unsupervised learning, there's no label or target value given for the data. A task where we group similar items together is known as clustering. In unsupervised learning, we may also want to find statistical values that describe the data. This is known as density estimation. Another task of unsupervised learning may be reducing the data from many features to a small number so that we can properly visualize it in two or three dimensions
NEW QUESTION 28
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. Sum of Errors
- B. Both land 2
- C. Information is not good enough.
- D. Mean Absolute Error
- E. Root Mean Square Error
Answer: C
NEW QUESTION 29
What is one modeling or descriptive statistical function in MADlib that is typically not provided in a standard relational database?
- A. Linear regression
- B. Variance
- C. Expected value
- D. Quantiles
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 30
Scenario: Suppose that Bob can decide to go to work by one of three modes of transportation, car, bus, or commuter train. Because of high traffic, if he decides to go by car. there is a 50% chance he will be late. If he goes by bus, which has special reserved lanes but is sometimes overcrowded, the probability of being late is only 20%. The commuter train is almost never late, with a probability of only 1 %, but is more expensive than the bus.
Suppose that Bob is late one day, and his boss wishes to estimate the probability that he drove to work that day by car. Since he does not know Which mode of transportation Bob usually uses, he gives a prior probability of
1 3 to each of the three possibilities. Which of the following method the boss will use to estimate of the probability that Bob drove to work?
- A. None of the above
- B. Random decision forests
- C. Linear regression
- D. Naive Bayes
Answer: D
Explanation:
Explanation
Bayes' theorem (also known as Bayes' rule) is a useful tool for calculating conditional probabilities.
NEW QUESTION 31
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