Cisco®
CompTIA
ISC
EC-Council
EXIN
Fortinet
ITIL®
Juniper
Microsoft
VMware
Avaya
SAP
Google
NVIDIA
Salesforce
Amazon
Palo Alto Networks
ISC2
Splunk®
ServiceNow®
All Vendors
  2. Home
  3. Exams
  4. Databricks
  5. DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST

DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST: Databricks Certified Professional Data Scientist Exam
Free Practice Exam Questions (page: 5)
Updated On: 2-Jan-2026

Page 5 of 35
PDF with 138 Questions

Suppose a man told you he had a nice conversation with someone on the train. Not knowing anything about this conversation, the probability that he was speaking to a woman is 50% (assuming the train had an equal number of men and women and the speaker was as likely to strike up a conversation with a man as with a woman). Now suppose he also told you that his conversational partner had long hair. It is now more likely he was speaking to a woman, since women are more likely to have long hair than men.____________
can be used to calculate the probability that the person was a woman.

  1. SVM
  2. MLE
  3. Bayes' theorem
  4. Logistic Regression

Answer(s): C

Explanation:

To see how this is done, let W represent the event that the conversation was held with a woman, and L denote the event that the conversation was held with a longhaired person. It can be assumed that women constitute half the population for this example. So, not knowing anything else, the probability that W occurs is P(W) = 0.5. Suppose it is also known that 75% of women have long hair which we denote as P(L |W) = 0.75 (read: the probability of event L given event W is 0.75, meaning that the probability of a person having long hair (event "L"): given that we already know that the person is a woman ("event W") is 75%). Likewise, suppose it is known that 15% of men have long hair, or P(L |M) = 0.15; where M is the complementary event of W: i.e.; the event that the conversation was held with a man (assuming that every human is either a man or a woman). Our goal is to calculate the probability that the conversation was held with a woman, given the fact that the person had long hair, or, in our notation, P(W |L). Using the formula for Bayes' theorem, we have:



where we have used the law of total probability to expand P(L),
The numeric answer can be obtained by substituting the above values into this formula (the algebraic multiplication is annotated using " *", the centered dot). This yields



i.e., the probability that the conversation was held with a woman, given that the person had long hair is about 83%. More examples are provided below.



Which of the following could be features?

  1. Words in the document
  2. Symptoms of a diseases
  3. Characteristics of an unidentified object
  4. 0nly 1 and 2
  5. All 1,2 and 3 are possible

Answer(s): E

Explanation:

Any dataset that can be turned into lists of features. A feature is simply something that is either present or absent for a given item. In the case of documents, the features are the words in the document but they could also be characteristics of an unidentified object symptoms of a disease, or anything else that can be said to be present of absent.



Refer to image below

  1. Option A
  2. Option B
  3. Option C
  4. Option D

Answer(s): A

Explanation:



A fruit may be considered to be an apple if it is red, round, and about 3" in diameter. A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of the

  1. Presence of the other features.
  2. Absence of the other features.
  3. Presence or absence of the other features
  4. None of the above

Answer(s): C

Explanation:

In simple terms, a naive Bayes classifier assumes that the value of a particular feature is unrelated to the presence or absence of any other feature, given the class variable. For example, a fruit may be considered to be an apple if it is red, round, and about 3" in diameter A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of the presence or absence of the other features.



Viewing page 5 of 35
Viewing questions 17 - 20 out of 138 questions



Post your Comments and Discuss Databricks DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST exam prep with other Community members:

DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Exam Discussions & Posts