Databricks Certified Professional Data Scientist can feel demanding because it measures both knowledge recall and practical decision-making. With 140 Databricks-Certified-Professional-Data-Scientist practice questions from Actual4dump, you can turn a broad syllabus into repeated, manageable review sessions.

Choose Your Databricks-Certified-Professional-Data-Scientist Study Format

• Printable PDF: prepared by experts, available for instant download, suitable for study anywhere, and supported by 365 days of free updates.
• Desktop Test Engine: installable Windows software with two practice modes, offline access, and an environment that simulates the exam experience.
• Online Test Engine: instant access through major web browsers on Windows, Mac, Android, and iOS, with test history and performance review.

Order Support from Actual4dump

• Security and privacy protection backed by McAfee.
• A free PDF demo is available so you can review the format and quality before purchasing.
• Every purchase includes 365 days of free updates, with renewal available at a 50% discount after expiration.
• Products are delivered by instant download and by email within one minute after payment. Contact customer support if the message does not arrive within two hours.
• There is no restriction on the number of computers where the product can be installed.

Databricks Databricks-Certified-Professional-Data-Scientist Exam Overview:

Databricks Databricks-Certified-Professional-Data-Scientist Exam Syllabus Topics:

Databricks Databricks-Certified-Professional-Data-Scientist Exam Questions Answered

Databricks Certified Professional Data Scientist Sample Questions:

Question 1

Consider the following confusion matrix for a data set with 600 out of 11,100 instances positive:
In this case, Precision = 50%, Recall = 83%, Specificity = 95%, and Accuracy = 95%.
Select the correct statement

A. 2 and 3
B. problem domain has a major impact on the measures that should be used to evaluate a classifier within it
C. 1 and 3
D. Precision is low, which means the classifier is predicting positives best
E. Precision is low, which means the classifier is predicting positives poorly

Question 2

A data scientist is asked to implement an article recommendation feature for an on-line magazine.
The magazine does not want to use client tracking technologies such as cookies or reading history. Therefore, only the style and subject matter of the current article is available for making recommendations. All of the magazine's articles are stored in a database in a format suitable for analytics.
Which method should the data scientist try first?

A. Naive Bayesian
B. K Means Clustering
C. Logistic Regression
D. Association Rules

Question 3

Suppose that the probability that a pedestrian will be tul by a car while crossing the toad at a pedestrian crossing without paying attention to the traffic light is lo be computed. Let H be a discrete random variable taking one value from (Hit. Not Hit). Let L be a discrete random variable taking one value from (Red. Yellow.
Green).
Realistically, H will be dependent on L That is, P(H = Hit) and P(H = Not Hit) will take different values depending on whether L is red, yellow or green. A person is. for example, far more likely to be hit by a car when trying to cross while Hie lights for cross traffic are green than if they are red In other words, for any given possible pair of values for Hand L. one must consider the joint probability distribution of H and L to find the probability* of that pair of events occurring together if Hie pedestrian ignores the state of the light Here is a table showing the conditional probabilities of being bit. defending on ibe stale of the lights (Note that the columns in this table must add up to 1 because the probability of being hit oi not hit is 1 regardless of the stale of the light.)

A. marginal probability that P(H=Not Hit) is the sum of the H=Not Hit row
B. marginal probability that P(H=Not Hit) is the sum of the H= Hit row
C. The marginal probability P(H=Hit) is the sum along the H=Hit row of this joint distribution table, as this is the probability of being hit when the lights are red OR yellow OR green.

Question 4

Suppose there are three events then which formula must always be equal to P(E1|E2,E3)?

A. P(E1,E2,E3)P(E1)/P(E2:E3)
B. P(E1,E2|E3)P(E2|E3)P(E3)
C. P(E1,E2,E3)P(E2)P(E3)
D. P(E1,E2;E3)/P(E2,E3)
E. P(E1,E2|E3)P(E3)

Question 5

While working with Netflix the movie rating websites you have developed a recommender system that has produced ratings predictions for your data set that are consistently exactly 1 higher for the user-item pairs in your dataset than the ratings given in the dataset. There are n items in the dataset. What will be the calculated RMSE of your recommender system on the dataset?

A. 2
B. n/2
C. 0
D. 1

Solutions: