Sep-2021 Latest ITExamSimulator Databricks-Certified-Professional-Data-Scientist Exam Dumps with PDF and Exam Engine Free Updated Today!
Following are some new Databricks-Certified-Professional-Data-Scientist Real Exam Questions!
NEW QUESTION 49
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 50
Regularization is a very important technique in machine learning to prevent over fitting. And Optimizing with a L1 regularization term is harder than with an L2 regularization term because
- A. The second derivative is not constant
- B. The constraints are quadratic
- C. The objective function is not convex
- D. The penalty term is not differentiate
Answer: D
Explanation:
Explanation
Regularization is a very important technique in machine learning to prevent overfitting. Mathematically speaking, it adds a regularization term in order to prevent the coefficients to fit so perfectly to overfit. The difference between the L1 and L2 is just that L2 is the sum of the square of the weights, while L1 is just the sum of the weights.
Much of optimization theory has historically focused on convex loss functions because they're much easier to optimize than non-convex functions: a convex function over a bounded domain is guaranteed to have a minimum, and it's easy to find that minimum by following the gradient of the function at each point no matter where you start. For non-convex functions, on the other hand, where you start matters a great deal; if you start in a bad position and follow the gradient, you're likely to end up in a local minimum that is not necessarily equal to the global minimum.
You can think of convex functions as cereal bowls: anywhere you start in the cereal bowl, you're likely to roll down to the bottom. A non-convex function is more like a skate park: lots of ramps, dips, ups and downs. It's a lot harder to find the lowest point in a skate park than it is a cereal bowl.
NEW QUESTION 51
Which analytical method is considered unsupervised?
may have a trend component that is quadratic in nature. Which pattern of data will indicate that the trend in the time series data is quadratic in nature?
- A. Linear regression
- B. K-means clustering
- C. Decision tree
- D. Naive Bayesian classifier
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 52
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 53 
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. blue
- B. yellow
- C. Neither
Answer: A
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 54
You are creating a Classification process where input is the income, education and current debt of a customer, what could be the possible output of this process.
- A. Percentage of the customer loan repayment capability
- B. Percentage of the customer should be given loan or not
- C. Probability of the customer default on loan repayment
- D. The output might be a risk class, such as "good", "acceptable", "average", or "unacceptable".
Answer: D
Explanation:
Explanation
Classification 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 a risk class, such as
"good", "acceptable", "average", or "unacceptable". Contrast this to regression where the output is a number not a class.
NEW QUESTION 55
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. Association rules
- B. Linear regression
- C. Decision trees
- D. K-means clustering
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 56
Select the correct statement which applies to logistic regression
- A. May have low accuracy
- B. Works with Numeric values
- C. Computationally inexpensive, easy to implement knowledge representation easy to interpret
Answer: A,B,C
NEW QUESTION 57
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 one star off of what people really think
- B. It means that your predictions are on average three star off of what people really think
- C. It means that your predictions are on average two star off of what people really think
- D. It means that your predictions are on average four star off of what people really think
Answer: A
NEW QUESTION 58
You are using k-means clustering to classify heart patients for a hospital. You have chosen Patient Sex, Height, Weight, Age and Income as measures and have used 3 clusters. When you create a pair-wise plot of the clusters, you notice that there is significant overlap between the clusters. What should you do?
- A. Identify additional measures to add to the analysis
- B. Remove one of the measures
- C. Increase the number of clusters
- D. Decrease the number of clusters
Answer: D
NEW QUESTION 59
Your company has organized an online campaign for feedback on product quality and you have all the responses for the product reviews, in the response form people have check box as well as text field. Now you know that people who do not fill in or write non-dictionary word in the text field are not considered valid feedback. People who fill in text field with proper English words are considered valid response. Which of the following method you should not use to identify whether the response is valid or not?
- A. Logistic Regression
- B. Naive Bayes
- C. Random Decision Forests
- D. Any one of the above
Answer: D
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 60
You are working on a problem where you have to predict whether the claim is done valid or not. And you find that most of the claims which are having spelling errors as well as corrections in the manually filled claim forms compare to the honest claims. Which of the following technique is suitable to find out whether the claim is valid or not?
- A. Logistic Regression
- B. Naive Bayes
- C. Random Decision Forests
- D. Any one of the above
Answer: D
Explanation:
Explanation
In this problem you have been given high-dimensional independent variables like texts, corrections, 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 61
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 62
You are working as a data science consultant for a gaming company. You have three member team and all other stake holders are from the company itself like project managers and project sponsored, data team etc.
During the discussion project managed asked you that when can you tell me that the model you are using is robust enough, after which step you can consider answer for this question?
- A. Data Preparation
- B. Discovery
- C. Operationalize
- D. Model building
- E. Model planning
Answer: D
Explanation:
Explanation
To answer whether the model you are building is robust enough or not you need to have answer below questions at least
- Model is performing as expected with the test data or not?
- Whatever hypothesis defined in the initial phase is being tested or not?
- Do we need more data?
- Domain experts are convinced or not with the model?
And all these can be answered when you have built the model and tested with the test data sets. Hence, correct option will be Model Building.
NEW QUESTION 63
Regularization is a very important technique in machine learning to prevent overfitting. Mathematically speaking, it adds a regularization term in order to prevent the coefficients to fit so perfectly to overfit. The difference between the L1 and L2 is...
- A. L1 gives Non-sparse output while L2 gives sparse outputs
- B. L1 is the sum of the square of the weights, while L2 is just the sum of the weights
- C. None of the above
- D. L2 is the sum of the square of the weights, while L1 is just the sum of the weights
Answer: D
Explanation:
Explanation
Regularization is a very important technique in machine learning to prevent overfitting. Mathematically speaking, it adds a regularization term in order to prevent the coefficients to fit so perfectly to overfit. The difference between the L1 and L2 is just that L2 is the sum of the square of the weights, while L1 is just the sum of the weights. As follows: L1 regularization on least squares:
A picture containing text Description automatically generated
NEW QUESTION 64
......
Resources From:
- 2021 Latest ITExamSimulator Databricks-Certified-Professional-Data-Scientist Exam Dumps (PDF & Exam Engine) Free Share: https://www.itexamsimulator.com/Databricks-Certified-Professional-Data-Scientist-brain-dumps.html
Free Resources from ITExamSimulator, We Devoted to Helping You 100% Pass All Exams!
