Most Recent Databricks-Certified-Professional-Data-Scientist Exam Dumps

Prepare for the Databricks Certified Professional Data Scientist Exam exam with our extensive collection of questions and answers. These practice Q&A are updated according to the latest syllabus, providing you with the tools needed to review and test your knowledge.

QA4Exam focus on the latest syllabus and exam objectives, our practice Q&A are designed to help you identify key topics and solidify your understanding. By focusing on the core curriculum, These Questions & Answers helps you cover all the essential topics, ensuring you're well-prepared for every section of the exam. Each question comes with a detailed explanation, offering valuable insights and helping you to learn from your mistakes. Whether you're looking to assess your progress or dive deeper into complex topics, our updated Q&A will provide the support you need to confidently approach the Databricks-Certified-Professional-Data-Scientist exam and achieve success.

The questions for Databricks-Certified-Professional-Data-Scientist were last updated on Jul 18, 2026.

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Question No. 1

Select the correct statement regarding the naive Bayes classification

Ait only requires a small amount of training data to estimate the parameters

BIndependent variables can be assumed

Conly the variances of the variables for each class need to be determined

Dfor each class entire covariance matrix need to be determined
An advantage of naive Bayes is that it only requires a small amount of training data to estimate the parameters (means and variances of the variables) necessary for classification. Because independent variables are assumed, only the variances of the variables for each class need to be determined and not the entire covariance matrix.

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Correct Answer: A, B, C

Question No. 2

Question-3: In machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel trick), is a fast and space-efficient way of vectorizing features (such as the words in a language), i.e., turning arbitrary features into indices in a vector or matrix. It works by applying a hash function to the features and using their hash values modulo the number of features as indices directly, rather than looking the indices up in an associative array. So what is the primary reason of the hashing trick for building classifiers?

AIt creates the smaller models

BIt requires the lesser memory to store the coefficients for the model

CIt reduces the non-significant features e.g. punctuations

DNoisy features are removed
This hashed feature approach has the distinct advantage of requiring less memory and one less pass through the training data, but it can make it much harder to reverse engineer vectors to determine which original feature mapped to a vector location. This is because multiple features may hash to the same location. With large vectors or with multiple locations per feature, this isn't a problem for accuracy but it can make it hard to understand what a classifier is doing.
Models always have a coefficient per feature, which are stored in memory during model building. The hashing trick collapses a high number of features to a small number which reduces the number of coefficients and thus memory requirements. Noisy features are not removed; they are combined with other features and so still have an impact.
The validity of this approach depends a lot on the nature of the features and problem domain; knowledge of the domain is important to understand whether it is applicable or will likely produce poor results. While hashing features may produce a smaller model, it will be one built from odd combinations of real-world features, and so will be harder to interpret.
An additional benefit of feature hashing is that the unknown and unbounded vocabularies typical of word-like variables aren't a problem.

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Correct Answer: B

Question No. 3

In which phase of the data analytics lifecycle do Data Scientists spend the most time in a project?

ADiscovery

BData Preparation

CModel Building

DCommunicate Results

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Correct Answer: B

Question No. 4

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......

ANumerical

BCategorical

CBoth 1 and 2 are correct

DNone of the 1 and 2 are correct
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.

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Correct Answer: C

Question No. 5

Which of the following could be features?

AWords in the document

BSymptoms of a diseases

CCharacteristics of an unidentified object

D0nly 1 and 2

EAll 1,2 and 3 are possible
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.

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Correct Answer: E

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