Databricks Databricks-Certified-Professional-Data-Scientist - Databricks Certified Professional Data Scientist Exam

During the discovery phase you need to find how much resources are required as early as possible and for that even you can involve various stakeholders like Software engineering team, DBAs,

Network engineers, System administrators etc. for your requirement and these resources are already available or you need to procure them. Also, what would be source of the data? What all tools and software's are required to execute the same?

naive Bayes classifiers have worked quite well in many real-world situations, famously document classification and spam filtering. They requires a small amount of training data to estimate the necessary parameters

To answer whether the model you are building is robust enough or not you need to

have answer below questions at least

- Model is performing as expected with the test data or not?

- Whatever hypothesis defined in the initial phase is being tested or not?

- Do we need more data?

- Domain experts are convinced or not with the model?

And all these can be answered when you have built the model and tested with the test data sets. Hence, correct option will be Model Building.

Linear regression models a linear relationship of a scalar dependent variable y to one or more explanatory independent variables x to build a model of coefficients.

In a binomial distribution, only 2 parameters, namely n and p, are needed to determine the probability. Where p is the probability of success and q is the probability of failure in a binomial trial, then the expected number of successes in n trials.

This is a binomial distribution because there are only 2 possible outcomes (we get a 5 or we don't).

Feature Selection:

The method based on principal component analysis (PCA) evaluates the features

according to the projection of the largest eigenvector of the correlation matrix on the

initial dimensions, the method based on Fisher's linear discriminate analysis

evaluates. Them according to the magnitude of the components of the discriminate

vector.

The method based on principal component analysis (PCA) evaluates the features according to the projection of the largest eigenvector of the correlation matrix on the initial dimensions, the method based on Fisher's linear discriminant analysis evaluates. Them according to the magnitude of the components of the discriminant vector.

The first principal component corresponds to the greatest variance in the data, by definition. If we project the data onto the first principal component line, the data is more spread out (higher variance) than if projected onto any other line, including other principal components.