Databricks-Certified-Professional-Data-Scientist 無料問題集「Databricks Certified Professional Data Scientist」

（A）Quantiles

（B）Linear regression

（C）Variance

（D）Expected value

（A）It does not handle redundant variables well.

（B）It does not handle correlated variables well.

（C）It does not have explanatory values.

（D）It does not handle missing values well.

（A）What all tools are required, in the project?

（B）What all are the data sources for the project?

（C）What Unix server capacity required?

（D）Analyze the Raw data and its format and structure.

（E）What is the network capacity required

（A）Unsupervised

（B）None of the above

（C）Regression

（D）Supervised

（A）for each class entire covariance matrix need to be determined

（B）Independent variables can be assumed

（C）only the variances of the variables for each class need to be determined

（D）it only requires a small amount of training data to estimate the parameters

（A）Ordinary Least Square is a sum of the individual distance between each point and the fitted line of regression model.

（B）Ordinary Least Square can be used to estimates the parameters in linear model

（C）Ordinary Least Square is a sum of the squared individual distance between each point and the fitted line of regression model.

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

（A）Web designing to represent best visuals of its results from algorithm.

（B）He should be creative

（C）He should possess database administrative skills.

（D）Should possess good programming skills

（E）Should be very good at mathematics and statistic

（A）It does not have a target variable

（B）telling the machine Predict Y for our data X

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

（A）Logistic regression

（B）Naive Bayes

（C）Support vector machines

（D）All of the above

（E）Random decision forests

（A）Tree A

（B）Tree B

（C）Tree D

（D）Tree C