Free Databricks Certified Professional Data Scientist Exam Dumps

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Question No: 1

MultipleChoice

You are creating a Classification process where input is the income, education and current debt of a customer, what could be the possible output of this process.

Options

AProbability of the customer default on loan repayment

BPercentage of the customer loan repayment capability

CPercentage of the customer should be given loan or not

DThe output might be a risk class, such as 'good', 'acceptable', 'average', or 'unacceptable'.
Classification is the process of using several inputs to produce one or more outputs. For example the input might be the income, education and current debt of a customer The output might be a risk class, such as 'good', 'acceptable', 'average', or 'unacceptable'. Contrast this to regression where the output is a number not a class.

Question No: 2

MultipleChoice

Feature Hashing approach is "SGD-based classifiers avoid the need to predetermine vector size by simply picking a reasonable size and shoehorning the training data into vectors of that size" now with large vectors or with multiple locations per feature in Feature hashing?

Options

AIs a problem with accuracy

BIt is hard to understand what classifier is doing

CIt is easy to understand what classifier is doing

DIs a problem with accuracy as well as hard to understand what classifier us doing
FEATURE HASHING
SGD-based classifiers avoid the need to predetermine vector size by simply picking a reasonable size and shoehorning the training data into vectors of that size. This approach is known as feature hashing. The shoehorning is done by picking one or more locations by using a hash of the name of the variable for continuous variables or a hash of the variable name and the category name or word for categorical, textlike, or word-like data.
This hashed feature approach has the distinct advantage of requiring less memory and one less pass through the training data, but it can make it much harder to reverse engineer vectors to determine which original feature mapped to a vector location. This is because multiple features may hash to the same location. With large vectors or with multiple locations per feature, this isn't a problem for accuracy but it can make it hard to understand what a classifier is doing.
An additional benefit of feature hashing is that the unknown and unbounded vocabularies typical of word-like variables aren't a problem.

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