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 Apr 22, 2026.

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Question No. 1

What is the probability that the total of two dice will be greater than 8, given that the first die is a 6?

A1/3

B2/3

C1/6

D2/6

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Correct Answer: B

Question No. 2

Regularization is a very important technique in machine learning to prevent over fitting. And Optimizing with a L1 regularization term is harder than with an L2 regularization term because

AThe penalty term is not differentiate

BThe second derivative is not constant

CThe objective function is not convex

DThe constraints are quadratic
Regularization is a very important technique in machine learning to prevent overfitting. Mathematically speaking, it adds a regularization term in order to prevent the coefficients to fit so perfectly to overfit. The difference between the L1 and L2 is just that L2 is the sum of the square of the weights, while L1 is just the sum of the weights.
Much of optimization theory has historically focused on convex loss functions because they're much easier to optimize than non-convex functions: a convex function over a bounded domain is guaranteed to have a minimum, and it's easy to find that minimum by following the gradient of the function at each point no matter where you start. For non-convex functions, on the other hand, where you start matters a great deal; if you start in a bad position and follow the gradient, you're likely to end up in a local minimum that is not necessarily equal to the global minimum.
You can think of convex functions as cereal bowls: anywhere you start in the cereal bowl, you're likely to roll down to the bottom. A non-convex function is more like a skate park: lots of ramps, dips, ups and downs. It's a lot harder to find the lowest point in a skate park than it is a cereal bowl.

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Correct Answer: A

Question No. 3

Which of the following question statement falls under data science category?

AWhat happened in last six months?

BHow many products have been sold in a last month?

CWhere is a problem for sales?

DWhich is the optimal scenario for selling this product?

EWhat happens, if these scenario continues?
This question wants to check your understanding about Bl and Data Science. Bl was already existing and analytics team already using it. They need to improve and learn data science technique to solve some problems. If you check the option given in the question, it will confuse you. But if you have worked in Bl or as a Data Scientist then it is easy to answer. First 3 option can be easily answered using reporting solution, what sales happened in last six month, what was the problem etc.
But for the last two option you need to apply data science techniques like which all scenarios are optimal for product sales, you need to collect the data and applying various techniques for that. Hence, last two option can only be answered using Data Science technique And for this you need to apply techniques like Optimization, predictive modeling, statistical analysis on structured and un-structured data.

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Correct Answer: D, E

Question No. 4

Select the correct algorithm of unsupervised algorithm

AK-Nearest Neighbors

BK-Means

CSupport Vector Machines

DNaive Bayes
Sup Supervised learning tasks
Classification Regression
k-Nearest Neighbors Linear
Naive Bayes Locally weighted linear
Support vector machines Ridge
Decision trees Lasso
Unsupervised learning tasks Clustering Density estimation k-Means Expectation maximization DBSCAN Parzen window

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Correct Answer: A

Question No. 5

Which analytical method is considered unsupervised?

may have a trend component that is quadratic in nature. Which pattern of data will indicate that the trend in the time series data is quadratic in nature?

ANaive Bayesian classifier

BDecision tree

CLinear regression

DK-means clustering
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: D

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