Most Recent Databricks-Certified-Professional-Data-Scientist Exam Questions & Answers

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 Dec 22, 2024.

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Question No. 1

Select the statement which applies correctly to the Naive Bayes

AWorks with a small amount of data

BSensitive to how the input data is prepared

CWorks with nominal values

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Correct Answer: A, B, C

Question No. 2

A bio-scientist is working on the analysis of the cancer cells. To identify whether the cell is cancerous or not, there has been hundreds of tests are done with small variations to say yes to the problem. Given the test result for a sample of healthy and cancerous cells, which of the following technique you will use to determine whether a cell is healthy?

ALinear regression

BCollaborative filtering

CNaive Bayes

DIdentification Test
In this problem you have been given high-dimensional independent variables like yes, no: test results etc. and you have to predict either valid or not valid (One of two). So all of the below technique can be applied to this problem.
Support vector machines Naive Bayes Logistic regression Random decision forests

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Correct Answer: C

Question No. 3

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?

AK Means Clustering

BNaive Bayesian

CLogistic Regression

DAssociation Rules
kmeans uses an iterative algorithm that minimizes the sum of distances from each object to its cluster centroid, over all clusters. This algorithm moves objects between clusters until the sum cannot be decreased further. The result is a set of clusters that are as compact and well-separated as possible. You can control the details of the minimization using several optional input parameters to kmeans, including ones for the initial values of the cluster centroids, and for the maximum number of iterations.
Clustering is primarily an exploratory technique to discover hidden structures of the data: possibly as a prelude to more focused analysis or decision processes. Some specific applications of k-means are image processing^ medical and customer segmentation. Clustering is often used as a lead-in to classification. Once the clusters are identified,
labels can be applied to each cluster to classify each group based on its characteristics. Marketing and sales groups use k-means to better identify customers who have similar behaviors and spending patterns.

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Correct Answer: A

Question No. 4

If E1 and E2 are two events, how do you represent the conditional probability given that E2 occurs given that E1 has occurred?

AP(E1)/P(E2)

BP(E1+E2)/P(E1)

CP(E2)/P(E1)

DP(E2)/(P(E1+E2)

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Correct Answer: C

Question No. 5

Which of the below best describe the Principal component analysis

ADimensionality reduction

BCollaborative filtering

CClassification

DRegression

EClustering

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Correct Answer: A

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