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Databricks DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Exam Questions
Databricks Certified Professional Data Scientist Exam (Page 3 )

Updated On: 21-Feb-2026
Page 3 of 29
PDF with 138 Questions

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...

  1. L2 is the sum of the square of the weights, while L1 is just the sum of the weights
  2. L1 is the sum of the square of the weights, while L2 is just the sum of the weights
  3. L1 gives Non-sparse output while L2 gives sparse outputs
  4. None of the above

Answer(s): A

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:



Select the correct option which applies to L2 regularization

  1. Computational efficient due to having analytical solutions
  2. Non-sparse outputs
  3. No feature selection

Answer(s): A,B,C

Explanation:

The difference between their properties can be promptly summarized as follows:



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

  1. The penalty term is not differentiate
  2. The second derivative is not constant
  3. The objective function is not convex
  4. The constraints are quadratic

Answer(s): A

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.



Logistic regression is a model used for prediction of the probability of occurrence of an event. It makes use of several variables that may be......

  1. Numerical
  2. Categorical
  3. Both 1 and 2 are correct
  4. None of the 1 and 2 are correct

Answer(s): C

Explanation:

Logistic regression is a model used for prediction of the probability of occurrence of an event. It makes use of several predictor variables that may be either numerical or categories.



Spam filtering of the emails is an example of

  1. Supervised learning
  2. Unsupervised learning
  3. Clustering
  4. 1 and 3 are correct
  5. 2 and 3 are correct

Answer(s): A

Explanation:

Clustering is an example of unsupervised learning. The clustering algorithm finds groups within the data without being told what to look for upfront. This contrasts with classification, an example of supervised machine learning, which is the process of determining to which class an observation belongs. A common application of classification is spam filtering. With spam filtering we use labeled data to train the classifier: e-mails marked as spam or ham.






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