Name: Databricks Certified Professional Data Scientist Exam
Brand: ValidExamDumps
SKU: Databricks-Certified-Professional-Data-Scientist
Price: 20 USD
Availability: InStock
Rating: 4.8 (250 reviews)

Free Databricks Databricks-Certified-Professional-Data-Scientist Exam Actual Questions

The questions for Databricks-Certified-Professional-Data-Scientist were last updated On May 5, 2025

At ValidExamDumps, we consistently monitor updates to the Databricks-Certified-Professional-Data-Scientist exam questions by Databricks. Whenever our team identifies changes in the exam questions,exam objectives, exam focus areas or in exam requirements, We immediately update our exam questions for both PDF and online practice exams. This commitment ensures our customers always have access to the most current and accurate questions. By preparing with these actual questions, our customers can successfully pass the Databricks Certified Professional Data Scientist Exam exam on their first attempt without needing additional materials or study guides.

Other certification materials providers often include outdated or removed questions by Databricks in their Databricks-Certified-Professional-Data-Scientist exam. These outdated questions lead to customers failing their Databricks Certified Professional Data Scientist Exam exam. In contrast, we ensure our questions bank includes only precise and up-to-date questions, guaranteeing their presence in your actual exam. Our main priority is your success in the Databricks-Certified-Professional-Data-Scientist exam, not profiting from selling obsolete exam questions in PDF or Online Practice Test.

Question No. 1

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?

Dn/2
The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a frequently used measure of the differences between values predicted by a model or an estimator and the values actually observed. Basically, the RMSD represents the sample standard deviation of the differences between predicted values and observed values. These individual differences are called residuals when the calculations are performed over the data sample that was used for estimation, and are called prediction errors when computed out-of-sample. The RMSD serves to aggregate the magnitudes of the errors in predictions for various times into a single measure of predictive power. RMSD is a good measure of accuracy, but only to compare forecasting errors of different models for a particular variable and not between variables, as it is scale-dependent. RMSE is calculated as the square root of the mean of the squares of the errors. The error in every case in this example is 1. The square of 1 is 1 The average of n items with value 1 is 1 The square root of 1 is 1 The RMSE is therefore 1

Show Answer

Correct Answer: A

Question No. 2

Question-26. There are 5000 different color balls, out of which 1200 are pink color. What is the maximum likelihood estimate for the proportion of "pink" items in the test set of color balls?

A2.4

B24 0

C.24

D.48

E4.8

Show Answer

Correct Answer: C

In general, for a fixed set of data and underlying statistical model the method of maximum likelihood selects the set of values of the model parameters that maximizes the likelihood function. Intuitively, this maximizes the 'agreement' of the selected model with the observed data, and for discrete random variables it indeed maximizes the probability of the observed data under the resulting distribution. Maximum-likelihood estimation gives a unified approach to estimation, which is well-defined in the case of the normal distribution and many other problems. However in some complicated problems, difficulties do occur: in such problems, maximum-likelihood estimators are unsuitable or do not exist.

Question No. 3

Select the correct statement which applies to K-Nearest Neighbors

ANo Assumption about the data

BComputationally expensive

CRequire less memory

DWorks with Numeric Values

Show Answer

Correct Answer: A, B, D

Pros: High accuracy insensitive to outliers, no assumptions about data

Cons: Computationally expensive, requires a lot of memory

Works with: Numeric values, nominal values

Question No. 4

Clustering is a type of unsupervised learning with the following goals

AMaximize a utility function

BFind similarities in the training data

CNot to maximize a utility function

D1 and 2

E2 and 3
type of unsupervised learning is called clustering. In this type of learning, The goal is not to maximize a utility function, but simply to find similarities in the training data.
The assumption is often that the clusters discovered will match reasonably well with an intuitive classification. For instance, clustering individuals based on demographics might result in a clustering of the wealthy in one group and the poor in another. Clustering can be useful when there is enough data to form clusters (though this turns out to be difficult at times) and especially when additional data about members of a cluster can be used to produce further results due to dependencies in the data.

Show Answer

Correct Answer: E

Question No. 5

Which of the following statement true with regards to Linear Regression Model?

AOrdinary Least Square can be used to estimates the parameters in linear model

BIn Linear model, it tries to find multiple lines which can approximate the relationship between the outcome and input variables.

COrdinary Least Square is a sum of the individual distance between each point and the fitted line of regression model.

DOrdinary Least Square is a sum of the squared individual distance between each point and the fitted line of regression model.
Linear regression model are represented using the below equation

Where B(0) is intercept and B(1) is a slope. As B(0) and B(1) changes then fitted line also shifts accordingly on the plot. The purpose of the Ordinary Least Square method is to estimates these parameters B(0) and B(1). And similarly it is a sum of squared distance between the observed point and the fitted line. Ordinary least squares (OLS) regression minimizes the sum of the squared residuals. A model fits the data well if the differences between the observed values and the model's predicted values are small and unbiased.

Show Answer

Correct Answer: A, D