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

Correct Answer: E

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.

Correct Answer: C

Explanation: 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.

Correct Answer: ABCD

Explanation: Given two random variables X and Y whose joint distribution is known, the marginal distribution of X is simply the probability distribution of X averaging over information about Y. It is the probability distribution of X when the value of Y is not known. This is typically calculated by summing or integrating the joint probability distribution over Y. ' For discrete random variables, the marginal probability mass function can be written as Pr(X = x). This is

Text

Description automatically generated with low confidence where Pr(X = x,Y = y) is the joint distribution of X and Y, while Pr(X = x|Y = y) is the conditional distribution of X given Y In this case, the variable Y has been marginalized out. Bivariate marginal and joint probabilities for discrete random variables are often displayed as two-way tables. Similarly for continuous random variables, the marginal probability density function can be written as pX(x). This is

Diagram Description automatically generated with medium confidence where pX.Y(x.y) gives the joint distribution of X and Y while pX|Y(x|y) gives the conditional distribution for X given Y Again: the variable Y has been marginalized out.

Note that a marginal probability can always be written as an expected value:

Text, letter Intuitively, the marginal probability of X is computed by examining the conditional probability of X given a particular value of Y, and then averaging this conditional probability over the distribution of all values of Y This follows from the definition of expected value, i.e. in general

A picture containing diagram

Correct Answer: E

Explanation: Any dataset that can be turned into lists of features. A feature is simply something that is either present or absent for a given item. In the case of documents, the features are the words in the document but they could also be characteristics of an unidentified object symptoms of a disease, or anything else that can be said to be present of absent.

Correct Answer: B

Correct Answer: A

Correct Answer: D

Explanation: This is the standard solution of the normal equations for linear regression. Because A is not square, you cannot simply take its inverse.

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: B

Explanation: 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, text-like, 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.