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 Nov 19, 2024.

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Question No. 1

RMSE is a useful metric for evaluating which types of models?

ALogistic regression

BNaive Bayes classifier

CLinear regression

DAll of the above
Error calculation allows you to see how well a machine learning
method is performing.
One way of determining this performance is to calculate a numerical error This number is sometimes a percent,
however it can also be a score or distance. The goal is usually to minimize an error
percent or distance:
however th goal may be to minimize or maximize a score. Encog supports the
following error calculation methods.
Sum of Squares Error (ESS)
Root Mean Square Error (RMS)
Mean Square Error (MSE) (default)
SOM Error (Euclidean Distance Error)
RMSE measures error of a predicted numeric value, and so applies to contexts like
regression and some recommender system techniques,
which rely on predicting a numeric value. It is not relevant to classification
techniques
like logistic regression and Naive Bayes, which predict categorical values.
It also is not relevant to unsupervied techniques like clustering.
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.

Show Answer

Correct Answer: C

Question No. 2

What describes a true limitation of Logistic Regression method?

AIt does not handle redundant variables well.

BIt does not handle missing values well.

CIt does not handle correlated variables well.

DIt does not have explanatory values.

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Correct Answer: B

Question No. 3

You are asked to create a model to predict the total number of monthly subscribers for a specific magazine. You are provided with 1 year's worth of subscription and payment data, user demographic data, and 10 years worth of content of the magazine (articles and pictures). Which algorithm is the most appropriate for building a predictive model for subscribers?

ALinear regression

BLogistic regression

CDecision trees

DTF-IDF

Show Answer

Correct Answer: A

Before you model the relationship between pairs of quantities, it is a good idea to perform correlation analysis to establish if a linear relationship exists between these quantities. Be aware that variables can have nonlinear relationships, which correlation analysis cannot detect. For more information, see Linear Correlation.

If you need to fit data with a nonlinear model, transform the variables to make the relationship linear. Alternatively try to fit a nonlinear function directly using either the Statistics and Machine Learning Toolbox nlinfit function, the Optimization Toolbox Isqcurvefit function, or by applying functions in the Curve Fitting Toolbox.

Question No. 4

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.

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Correct Answer: A, D

Question No. 5

You are using k-means clustering to classify heart patients for a hospital. You have chosen Patient Sex, Height, Weight, Age and Income as measures and have used 3 clusters. When you create a pair-wise plot of the clusters, you notice that there is significant overlap between the clusters. What should you do?

AIdentify additional measures to add to the analysis

BRemove one of the measures

CDecrease the number of clusters

DIncrease the number of clusters

Show Answer

Correct Answer: C

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