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Free DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Exam Braindumps
Databricks Certified Professional Data Scientist Exam Questions 36–40 (page: 9)

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

Select the correct statement which applies to logistic regression

  1. Computationally inexpensive, easy to implement knowledge representation easy to interpret
  2. May have low accuracy
  3. Works with Numeric values

Answer(s): A,B,C



Suppose that we are interested in the factors that influence whether a political candidate wins an election. The outcome (response) variable is binary (0/1); win or lose. The predictor variables of interest are the amount of money spent on the campaign, the amount of time spent campaigning negatively and whether or not the candidate is an incumbent.

Above is an example of

  1. Linear Regression
  2. Logistic Regression
  3. Recommendation system
  4. Maximum likelihood estimation
  5. Hierarchical linear models

Answer(s): B

Explanation:

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



A researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don't admit, is a binary variable.
Above is an example of

  1. Linear Regression
  2. Logistic Regression
  3. Recommendation system
  4. Maximum likelihood estimation
  5. Hierarchical linear models

Answer(s): B

Explanation:

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



In unsupervised learning which statements correctly applies

  1. It does not have a target variable
  2. Instead of telling the machine Predict Y for our data X, we're asking What can you tell me about X?
  3. telling the machine Predict Y for our data X

Answer(s): A,B

Explanation:

In unsupervised learning we don't have a target variable as we did in classification and regression.
Instead of telling the machine Predict Y for our data X, we're asking What can you tell me about X?
Things we ask the machine to tell us about
X may be What are the six best groups we can make out of X? or What three features occur together most frequently in X?



Select the correct statement which applies to Supervised learning

  1. We asks the machine to learn from our data when we specify a target variable.
  2. Lesser machine's task to only divining some pattern from the input data to get the target variable
  3. Instead of telling the machine Predict Y for our data X, we're asking What can you tell me about X?

Answer(s): A,B,C

Explanation:

Supervised learning asks the machine to learn from our data when we specify a target variable.
This reduces the machine's task to only divining some pattern from the input data to get the target variable.
In unsupervised learning we don't have a target variable as we did in classification and regression.
Instead of telling the machine Predict Y for our data X> we're asking What can you tell me about X?
Things we ask the machine to tell us about
X may be What are the six best groups we can make out of X? or What three features occur together most frequently in X?



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