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

Correct Answer: D

Explanation: Classification : Build models to classify data into different categories credit scoring, tumor detection, image recognition Regression: Build models to predict continuous data, electricity load forecasting, algorithmic trading, drug discovery

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: A

Explanation: Supervised learning is fairly common in classification problems because the goal is often to get the computer to learn a classification system that we have created. Digit recognition: once again, is a common example of classification learning. More generally, classification learning is appropriate for any problem where deducing a classification is useful and the classification is easy to determine. In some cases, it might not even be necessary to give predetermined classifications to every instance of a problem if the agent can work out the classifications for itself. This would be an example of unsupervised learning in a classification context.

Correct Answer: B

Explanation: 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. Models always have a coefficient per feature, which are stored in memory during model building. The hashing trick collapses a high number of features to a small number which reduces the number of coefficients and thus memory requirements. Noisy features are not removed; they are combined with other features and so still have an impact. The validity of this approach depends a lot on the nature of the features and problem domain; knowledge of the domain is important to understand whether it is applicable or will likely produce poor results. While hashing features may produce a smaller model, it will be one built from odd combinations of real-world features, and so will be harder to interpret. An additional benefit of feature hashing is that the unknown and unbounded vocabularies typical of word-like variables aren't a problem.

Correct Answer: ABC

Explanation: 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

Correct Answer: B

Explanation: Decreasing the number of clusters will help in adjusting this outlier cluster to get adjusted in another cluster.

Correct Answer: B

Explanation: : Logistic regression Pros: Computationally inexpensive, easy to implement, knowledge representation easy to interpret Cons: Prone to underfitting, may have low accuracy Works with: Numeric values, nominal values

Correct Answer: B

Correct Answer: AB

Explanation: The marginal probability P(H=Hit) is the sum along the H=Hit row of this joint distribution table, as this is the probability of being hit when the lights are red OR yellow OR green. Similarly, the marginal probability that P(H=Not Hit) is the sum of the H=Not Hit row

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?