DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Online Practice Questions and Answers

Correct Answer: D

Explanation: In this problem you have been given high-dimensional independent variables like texts, corrections, 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

Correct Answer: E

Explanation: In this problem you have been given high-dimensional independent variables like yeS; nO; no English words , 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

Correct Answer: A

Explanation: In statistics, linear regression is an approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables (or independent variable) denoted X. The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression. (This term should be distinguished from multivariate linear regression^ where multiple correlated dependent variables are predicted, rather than a single scalar variable.) In linear regression data are modeled using linear predictor functions, and unknown model parameters are estimated from the data. Such models are called linear models. Most commonly, linear regression refers to a model in which the conditional mean of y given the value of X is an affine function of X. Less commonly: linear regression could refer to a model in which the median, or some other quantile of the conditional distribution of y given X is expressed as a linear function of X. Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of y given X, rather than on the joint probability distribution of y and X: which is the domain of multivariate analysis.

Correct Answer: C

Explanation: The MAE measures the average magnitude of the errors in a set of forecasts, without considering their direction. It measures accuracy for continuous variables. The equation is given in the library references. Expressed in words,

the MAE is the average over the verification sample of the absolute values of the differences between forecast and the corresponding observation. The MAE is a linear score which means that all the individual differences are weighted equally

in the average. The sum of absolute errors is a valid metric, but doesn't give any useful sense of how the recommender system is performing.

Support vector count and cluster density do not apply to recommender systems. MAE and AUC are both valid and useful metrics for measuring recommender systems.

Correct Answer: D

Explanation: If multiple variables are highly co-related then it is better you consider using the either of the variable which correlates more (which is not in the given option) or go for the new variable which is a function of the both the variable in this case it could be BMI (Body Mass Index). Because it is a function of both weight and height as per the below formula. BMI = Weight/(Height * Height)

Correct Answer: B

Correct Answer: ABC

Explanation: : Supervised learning asks the machine to learn from our data when we specify a target variable.

This reduces the machine's task to only divining some pattern from the input data to get the target variable.

In unsupervised learning we don't have a target variable as we did in classification and regression.

Instead of telling the machine Predict Y for our data X> we're asking What can you tell me about X?

Things we ask the machine to tell us about X may be What are the six best groups we can make out of X? or What three features occur together most frequently in X?

Correct Answer: A

Correct Answer: A

Explanation: Linear regression model generate continuous output variable.

Correct Answer: BC

Explanation: When you see the data age in years would have values like 50, 60r 70 90 years etc. And while calculating distance from centroid maximum possible value can be 90- 0 and its square will be 8100.

While using heights in meter can be 2-0.5(1.5) meters and its square will be 2.25 only. So you can see age has more effect than height. Hence bringing the height on same level you can convert it into centimeters. Can bring data upto 200

centimeters and then it be more effective like square of 200 maximum.

However there is another approach is to divide the each value with its standard deviation, which will not have impact of the units e.g. age/sd of the age, which results in value without unit. This can also help in reducing the effect of units.