[Mar-2022] Exam Databricks-Certified-Professional-Data-Scientist: New Brain Dump Professional - Exams-boost
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Databricks Databricks-Certified-Professional-Data-Scientist Exam Syllabus Topics:
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NEW QUESTION 17
What type of output generated in case of linear regression?
- A. Continuous variable
- B. Values between 0 and 1
- C. Any of the Continuous and Discrete variable
- D. Discrete Variable
Answer: A
Explanation:
Explanation
Linear regression model generate continuous output variable.
NEW QUESTION 18
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. Identification Test
- B. Linear regression
- C. Naive Bayes
- D. Collaborative filtering
Answer: C
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 19
Select the correct problems which can be solved using SVMs
- A. Classification of images can also be performed using SVMs
- B. SVMs are also useful in medical science to classify proteins with up to 90% of the compounds classified correctly
- C. SVMs are helpful in text and hypertext categorization
- D. Hand-written characters can be recognized using SVM
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 20
Select the correct statement which applies to Supervised learning
- A. Instead of telling the machine Predict Y for our data X, we're asking What can you tell me about X?
- B. Lesser machine's task to only divining some pattern from the input data to get the target variable
- 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 21
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. Binomial Distribution
- C. Bays Theorem
- D. Poisson Distribution
Answer: C
NEW QUESTION 22
In which phase of the data analytics lifecycle do Data Scientists spend the most time in a project?
- A. Discovery
- B. Data Preparation
- C. Model Building
- D. Communicate Results
Answer: B
NEW QUESTION 23
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. Logistic regression
- B. Support vector machines
- C. All of the above
- D. Random decision forests
- E. Naive Bayes
Answer: C
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 24
Select the correct statement which applies to logistic regression
- A. Computationally inexpensive, easy to implement knowledge representation easy to interpret
- B. May have low accuracy
- C. All 1, 2 and 3 are correct
- D. Works with Numeric values
- E. Only 1 and 3 are correct
Answer: C
Explanation:
Explanation
Depending on the size of the data you are uploading, Amazon S3 offers the following options:
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 25
The method based on principal component analysis (PCA) evaluates the features according to
- A. None of the above
- B. The projection of the smallest eigenvector of the correlation matrix on the initial dimensions
- C. The projection of the largest eigenvector of the correlation matrix on the initial dimensions
- D. According to the magnitude of the components of the discriminate vector
Answer: C
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 26 
The figure below shows a plot of the data of a data matrix M that is 1000 x 2. Which line represents the first principal component?
- A. Neither
- B. yellow
- C. blue
Answer: C
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.
The first principal component corresponds to the greatest variance in the data. The blue line is evidently this first principal component, because if we project the data onto the blue line, the data is more spread out (higher variance) than if projected onto any other line, including the yellow one.
NEW QUESTION 27
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. None of the above
- B. In both cases, the contribution to the RMSE, could varies
- C. In both cases, the contribution to the RMSE is the different
- D. In both cases, the contribution to the RMSE is the same
Answer: D
NEW QUESTION 28
Consider the following confusion matrix for a data set with 600 out of 11,100 instances positive:
In this case, Precision = 50%, Recall = 83%, Specificity = 95%, and Accuracy = 95%.
Select the correct statement
- A. Precision is low, which means the classifier is predicting positives best
- B. problem domain has a major impact on the measures that should be used to evaluate a classifier within it
- C. 1 and 3
- D. 2 and 3
- E. Precision is low, which means the classifier is predicting positives poorly
Answer: D
Explanation:
Explanation
In this case, Precision = 50%, Recall = 83%, Specificity = 95%: and Accuracy = 95%. In this case, Precision is low, which means the classifier is predicting positives poorly. However, the three other measures seem to suggest that this is a good classifier. This just goes to show that the problem domain has a major impact on the measures that should be used to evaluate a classifier within it, and that looking at the 4 simple cases presented is not sufficient.
NEW QUESTION 29
Which of the following true with regards to the K-Means clustering algorithm?
- A. Labels are pre-assigned to each objects in the cluster.
- B. It discovers the center of each cluster.
- C. Labels are not pre-assigned to each objects in the cluster.
- D. It find each objects fall in which particular cluster
- E. It classify the data based on the labels.
Answer: B,C,D
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 30
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. Classify whether a given person is a male or a female based on the measured features. The features include height, weight and foot size.
- C. To classify whether an email is spam or not spam
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 31
Question-18. What is the best way to ensure that the k-means algorithm will find a good clustering of a collection of vectors?
- A. Choose the initial centroids so that they are far away from each other
- B. Run at least log(N) iterations of Lloyd's algorithm, where N is the number of observations in the data set
- C. Only consider values of k larger than log(N), where N is the number of observations in the data set
- D. Choose the initial centroids so that they all He along different axes
Answer: A
Explanation:
Explanation
k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining, k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
The problem is computationally difficult (NP-hard); however there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both algorithms. Additionally, they both use cluster centers to model the data; however k-means clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes This Question-is about the properties that make k-means an effective clustering heuristic which primarily deal with ensuring that the initial centers are far away from each other. This is how modern k-means algorithms like k-means++ guarantee that with high probability Lloyd's algorithm will find a clustering within a constant factor of the optimal possible clustering for each k.
NEW QUESTION 32
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. Decrease the number of measures used
- B. Identify additional measures to add to the analysis
- C. Increase the number of clusters
- D. Decrease the number of clusters
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 33
Select the statement which applies correctly to the Naive Bayes
- A. Sensitive to how the input data is prepared
- B. Works with a small amount of data
- C. Works with nominal values
Answer: A,B,C
NEW QUESTION 34
Which of the following is not a correct application for the Classification?
- A. image recognition
- B. drug discovery
- C. tumor detection
- D. credit scoring
Answer: B
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 35
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. Logistic Regression
- C. Association Rules
- D. Linear Regression
Answer: B
NEW QUESTION 36
Refer to the exhibit.
You are building a decision tree. In this exhibit, four variables are listed with their respective values of info-gain.
Based on this information, on which attribute would you expect the next split to be in the decision tree?
- A. Income
- B. Age
- C. Credit Score
- D. Gender
Answer: C
NEW QUESTION 37
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