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DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST: Databricks Certified Professional Data Scientist Exam
Free Practice Exam Questions (page: 8)
Updated On: 2-Jan-2026

Page 7 of 35
PDF with 138 Questions

Select the correct problems which can be solved using SVMs

  1. SVMs are helpful in text and hypertext categorization
  2. Classification of images can also be performed using SVMs
  3. SVMs are also useful in medical science to classify proteins with up to 90% of the compounds classified correctly
  4. Hand-written characters can be recognized using SVM

Answer(s): A,B,C,D

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



Which is an example of supervised learning?

  1. PCA
  2. k-means clustering
  3. SVD
  4. EM
  5. SVM

Answer(s): E

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



Which of the following are point estimation methods?

  1. MAP
  2. MLE
  3. MMSE

Answer(s): A,B,C

Explanation:

Point estimators
· minimum-variance mean-unbiased estimator (MVUE), minimizes the risk (expected loss) of the squared-error loss-function.
· best linear unbiased estimator (BLUE)
· minimum mean squared error (MMSE)
· median-unbiased estimator, minimizes the risk of the absolute-error loss function · maximum likelihood (ML)
· method of moments, generalized method of moments



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

  1. The normalizing constant is always very close to 1
  2. The normalizing constant only has a small impact on the maximum likelihood
  3. The normalizing constant is often zero and can cause division by zero
  4. The normalizing constant doesn't impact the maximizing value

Answer(s): 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.



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