Databricks Databricks Certification DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Questions & Answers

• Question 11:

  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. Works with Numeric values

  D. Only 1 and 3 are correct

  E. All 1, 2 and 3 are correct

  Correct Answer: E

  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

• Question 12:

  What are the key outcomes of the successful analytical projects?

  A. Code of the model

  B. Technical specifications

  C. Presentations for the Analysts

  D. Presentation for Project Sponsors

  Correct Answer: ABCD

  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.

• Question 13:

  What are the advantages of the Hashing Features?

  A. Requires the less memory

  B. Less pass through the training data

  C. Easily reverse engineer vectors to determine which original feature mapped to a vector location

  Correct Answer: AB

  Explanation: 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, text-like, 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.

• Question 14:

  RMSE is a useful metric for evaluating which types of models?

  A. Logistic regression

  B. Naive Bayes classifier

  C. Linear regression

  D. All of the above

  Correct Answer: C

  Explanation: Error calculation allows you to see how well a machine learning method is performing.

  One way of determining this performance is to calculate a numerical error This number is sometimes a percent,

  however it can also be a score or distance. The goal is usually to minimize an error percent or distance:

  however th goal may be to minimize or maximize a score. Encog supports the following error calculation methods.

  Sum of Squares Error (ESS)

  Root Mean Square Error (RMS)

  Mean Square Error (MSE) (default)

  SOM Error (Euclidean Distance Error)

  RMSE measures error of a predicted numeric value, and so applies to contexts like regression and some recommender system techniques, which rely on predicting a numeric value. It is not relevant to classification techniques

  like logistic regression and Naive Bayes, which predict categorical values. It also is not relevant to unsupervied techniques like clustering. The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a frequently used

  measure of the

  differences between values predicted by a model or an estimator and the values actually observed. Basically,

  the RMSD represents the sample standard deviation of the differences between predicted values and observed values.

  These individual differences are called residuals when the calculations are performed over the data sample that was used for estimation, and are called prediction errors when computed out-of-sample. The RMSD serves to aggregate the

  magnitudes

  of the errors in predictions for various times into a single measure of predictive power. RMSD is a good measure of accuracy,

  but only to compare forecasting errors of different models for a particular variable and not between variables, as it is scale-dependent.

• Question 15:

  In statistics, maximum-likelihood estimation (MLE) is a method of estimating the parameters of a statistical model. When applied to a data set and given a statistical model, maximum-likelihood estimation provides estimates for the model's parameters and the normalizing constant usually ignored in MLEs because:

  A. The normalizing constant is always very close to 1

  B. The normalizing constant only has a small impact on the maximum likelihood

  C. The normalizing constant is often zero and can cause division by zero

  D. The normalizing constant doesn't impact the maximizing value

  Correct Answer: D

  Explanation: (Change the explanation even it is correct)A normalizing constant is positive, and multiplying or dividing a series of values by a positive number does not affect which of them is the largest. Maximum likelihood estimation is concerned only with finding a maximum value, so normalizing constants can be ignored.

• Question 16:

  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. Support vector machines

  B. Naive Bayes

  C. Logistic regression

  D. Random decision forests

  E. All of the above

  Correct Answer: E

  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

• Question 17:

  Select the correct statement regarding the naive Bayes classification:

  A. it only requires a small amount of training data to estimate the parameters

  B. Independent variables can be assumed

  C. only the variances of the variables for each class need to be determined

  D. for each class entire covariance matrix need to be determined

  Correct Answer: ABC

  Explanation: An advantage of naive Bayes is that it only requires a small amount of training data to estimate the parameters (means and variances of the variables) necessary for classification. Because independent variables are assumed, only the variances of the variables for each class need to be determined and not the entire covariance matrix.

• Question 18:

  Digit recognition, is an example of.....

  A. Classification

  B. Clustering

  C. Unsupervised learning

  D. None of the above

  Correct Answer: A

  Explanation: Supervised learning is fairly common in classification problems because the goal is often to get the computer to learn a classification system that we have created. Digit recognition: once again, is a common example of classification learning. More generally, classification learning is appropriate for any problem where deducing a classification is useful and the classification is easy to determine. In some cases, it might not even be necessary to give predetermined classifications to every instance of a problem if the agent can work out the classifications for itself. This would be an example of unsupervised learning in a classification context.

• Question 19:

  Select the correct problems which can be solved using SVMs:

  A. SVMs are helpful in text and hypertext categorization

  B. Classification of images can also be performed using SVMs

  C. SVMs are also useful in medical science to classify proteins with up to 90% of the compounds classified correctly

  D. Hand-written characters can be recognized using SVM

  Correct Answer: ABCD

  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

• Question 20:

  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. supervised learning

  B. un-supervised learning

  C. k-Nearest Neighbors

  D. Support vector machines

  Correct Answer: B

  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