Free Databricks Certified Professional Data Scientist Exam Databricks-Certified-Professional-Data-Scientist Exam Practice Test

Projecting a multi-dimensional dataset onto which vector has the greatest variance?

• first principal component
  
• first eigenvector
  
• not enough information given to answer
  
• second eigenvector
  
• second principal component
  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 discriminant analysis evaluates. Them according to the magnitude of the components of the discriminant vector.
  The first principal component corresponds to the greatest variance in the data, by definition. If we project the data onto the first principal component line, the data is more spread out (higher variance) than if projected onto any other line, including other principal components.
  

Question-34. Stories appear in the front page of Digg as they are 'voted up' (rated positively) by the community. As the community becomes larger and more diverse, the promoted stories can better reflect the average interest of the community members. Which of the following technique is used to make such recommendation engine?

• Naive Bayes classifier
  
• Collaborative filtering
  
• Logistic Regression
  
• Content-based filtering
  One scenario of collaborative filtering application is to recommend interesting or popular information as judged by the community. As a typical example, stories appear in the front page of Digg as they are 'voted up' (rated positively) by the community. As the community becomes larger and more diverse, the promoted stories can better reflect the average interest of the community members.
  

Which of the following is not a correct application for the Classification?

• credit scoring
  
• tumor detection
  
• image recognition
  
• drug discovery
  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
  

You are working in a classification model for a book, written by HadoopExam Learning Resources and decided to use building a text classification modelfor 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?

• Include least mutual information with other selected features as a feature selection criterion
  
• Include the number of times each of the words appears in the book in your model
  
• Decrease the size of our training data
  
• Evaluate a model that only includes the top 100 words
  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.
  

Feature Hashing approach is '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' now with large vectors or with multiple locations per feature in Feature hashing?

• Is a problem with accuracy
  
• It is hard to understand what classifier is doing
  
• It is easy to understand what classifier is doing
  
• Is a problem with accuracy as well as hard to understand what classifier us doing
  FEATURE HASHING
  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, textlike, 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.
  

Select the sequence of the developing machine learning applicationsA) Analyze the input dataB) Prepare the input dataC) Collect dataD) Train the algorithmE) Test the algorithmF) Use It

• A, B, C, D, E, F
  
• C, B, A, D, E, F
  
• C, A, B, D, E, F
  
• C, B, A, D, E, F
  1 Collect data. You could collect the samples by scraping a website and extracting data: or you could get information from an RSS feed or an API. You could have a device collect wind speed measurements and send them to you, or blood glucose levels, or anything you can measure. The number of options is endless. To save some time and effort you could use publicly available data
  2 Prepare the input data. Once you have this data, you need to make sure it's in a useable format. The format we'll be using in this book is the Python list. We'll talk about Python more in a little bit, and lists are reviewed in appendix A. The benefit of having this standard format is that you can mix and match algorithms and data sources. You may need to do some algorithm-specific formatting here. Some algorithms need features in a special format, some algorithms can deal with target variables and features as strings, and some need them to be integers. We'll get to this later but the algorithm-specific formatting is usually trivial compared to collecting data.
  3 Analyze the input data. This is looking at the data from the previous task. This could be as simple as looking at the data you've parsed in a text editor to make sure steps 1 and 2 are actually working and you don't have a bunch of empty values. You can also look at the data to see if you can recognize any patterns or if there's anything obvious^ such as a few data points that are vastly different from the rest of the set. Plotting data in one: two, or three dimensions can also help. But most of the time you'll have more than three features, and you can't easily plot the data across all features at one time. You could, however use some advanced methods we'll talk about later to distill multiple dimensions down to two or three so you can visualize the data.
  4 If you're working with a production system and you know what the data should look like, or you trust its source: you can skip this step. This step takes human involvement, and for an automated system you don't want human involvement. The value of this step is that it makes you understand you don't have garbage coming in.
  5 Train the algorithm. This is where the machine learning takes place. This step and the next step are where the 'core' algorithms lie, depending on the algorithm.You feed the algorithm good clean data from the first two steps andextract knowledge or information. This knowledge you often store in a formatthat's readily useable by a machine for the next two steps.In the case of unsupervised learning, there's no training step because youdon't have a target value. Everything is used in the next step.
  6 Test the algorithm. This is where the information learned in the previous step isput to use. When you're evaluating an algorithm, you'll test it to see how well itdoes. In the case of supervised learning, you have some known values you can use to evaluate the algorithm. In unsupervised learning, you may have to use some other metrics to evaluate the success. In either case, if you're not satisfied, you can go back to step 4, change some things, and try testing again. Often thecollection or preparation of the data may have been the problem, and you'll have to go back to step 1.
  7 Use it. Here you make a real program to do some task, and once again you see if all the previous steps worked as you expected. You might encounter some new data and have to revisit steps 1-5.
  

Which of the following are advantages of the Support Vector machines?

• Effective in high dimensional spaces.
  
• it is memory efficient
  
• possible to specify custom kernels
  
• Effective in cases where number of dimensions is greater than the number of samples
  
• Number of features is much greater than the number of samples, the method still give good performances
  
• SVMs directly provide probability estimates
  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.
  

In which of the scenario you can use the regression to predict the values

• Samsung can use it for mobile sales forecast
  
• Mobile companies can use it to forecast manufacturing defects
  
• Probability of the celebrity divorce
  
• Only 1 and 2
  
• All 1 ,2 and 3
  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.
  

What is one modeling or descriptive statistical function in MADlib that is typically not provided in a standard relational database?

• Expected value
  
• Variance
  
• Linear regression
  
• Quantiles
  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.
  

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?

• Naive Bayes
  
• Logistic Regression
  
• Random Decision Forests
  
• Any one of the above
  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