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Free DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Exam Braindumps
Databricks Certified Professional Data Scientist Exam Questions 46–50 (page: 11)

Updated On: 9-Feb-2026
Page 11 of 29
PDF with 138 Questions

Select the correct option from the below

  1. If you're trying to predict or forecast a target value^ then you need to look into supervised learning.
  2. If you've chosen supervised learning, with discrete target value like Yes/No. 1/2/3, A/B/C: or Red/Yellow/Black, then look into classification.
  3. If the target value can take on a number of values, say any value from 0.00 to 100.00, or -999 to 999: or +_to -_, then you need to look unsupervised learning
  4. If you're not trying to predict a target value, then you need to look into unsupervised learning
  5. Are you trying to fit your data into some discrete groups? If so and that's all you need, you should look into clustering.

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

Explanation:

If you re trying to predict or forecast a target value, then you need to look into supervised learning. If not, then unsupervised learning is the place you want to be. If you've chosen supervised learning, what's your target value? Is it a discrete value like Yes/No, 1/2/3, A/B/C: or Red/Yellow/Black? If so, then you want to look into classification. If the target value can take on a number of values, say any value from 0.00 to 100.00, or-999 to 999, or+_to -_, then you need to look into regression. If you're not trying to predict a target value: then you need to look into unsupervised learning. Are you trying to fit your data into some discrete groups? If so and that's all you need, you should look into clustering. Do you need to have some numerical estimate of how strong the fit is into each group? If you answer yes then you probably should look into a density estimation algorithm.



Select the correct statement which applies to K-Nearest Neighbors

  1. No Assumption about the data
  2. Computationally expensive
  3. Require less memory
  4. Works with Numeric Values

Answer(s): A,B,D

Explanation:

k-Nearest Neighbors
Pros: High accuracy insensitive to outliers, no assumptions about data Cons: Computationally expensive, requires a lot of memory Works with: Numeric values, nominal values



Select the statement which applies correctly to the Naive Bayes

  1. Works with a small amount of data
  2. Sensitive to how the input data is prepared
  3. Works with nominal values

Answer(s): A,B,C



Which of the following technique can be used to the design of recommender systems?

  1. Naive Bayes classifier
  2. Power iteration
  3. Collaborative filtering
  4. 1 and 3
  5. 2 and 3

Answer(s): C

Explanation:

One approach to the design of recommender systems that has seen wide use is collaborative filtering. Collaborative filtering methods are based on collecting and analyzing a large amount of information on users' behaviors, activities or preferences and predicting what users will like based on their similarity to other users. A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of accurately recommending complex items such as movies without requiring an "understanding" of the item itself. Many algorithms have been used in measuring user similarity or item similarity in recommender systems. For example the k-nearest neighbor (k-NN) approach and the Pearson Correlation



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?

  1. Naive Bayes
  2. Logistic Regression
  3. Random Decision Forests
  4. Any one of the above

Answer(s): D

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



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