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MCQOPTIONS
This section includes 940 Mcqs, each offering curated multiple-choice questions to sharpen your Artificial Intelligence knowledge and support exam preparation. Choose a topic below to get started.
| 201. |
scikit-learn also provides a class for per-sample normalization, Normalizer. It can apply________to each element of a dataset |
| A. | max, l0 and l1 norms |
| B. | max, l1 and l2 norms |
| C. | max, l2 and l3 norms |
| D. | max, l3 and l4 norms |
| Answer» C. max, l2 and l3 norms | |
| 202. |
How it's possible to use a different placeholder through the parameter_______. |
| A. | regression |
| B. | classification |
| C. | random_state |
| D. | missing_values |
| Answer» E. | |
| 203. |
________is much more difficult because it's necessary to determine a supervised strategy to train a model for each feature and, finally, to predict their value |
| A. | Removing the whole line |
| B. | Creating sub-model to predict those features |
| C. | Using an automatic strategy to input them according to the other known values |
| D. | All above |
| Answer» C. Using an automatic strategy to input them according to the other known values | |
| 204. |
What is ‘Test set’? |
| A. | Test set is used to test the accuracy of the hypotheses generated by the learner. |
| B. | It is a set of data is used to discover the potentially predictive relationship. |
| C. | Both A & B |
| D. | None of above |
| Answer» B. It is a set of data is used to discover the potentially predictive relationship. | |
| 205. |
What is ‘Overfitting’ in Machine learning? |
| A. | when a statistical model describes random error or noise instead of underlying relationship ‘overfitting’ occurs. |
| B. | Robots are programed so that they can perform the task based on data they gather from sensors. |
| C. | While involving the process of learning ‘overfitting’ occurs. |
| D. | a set of data is used to discover the potentially predictive relationship |
| Answer» B. Robots are programed so that they can perform the task based on data they gather from sensors. | |
| 206. |
During the last few years, many ______ algorithms have been applied to deepneural networks to learn the best policy for playing Atari video games and to teach an agent how to associate the right action with an input representing the state. |
| A. | Logical |
| B. | Classical |
| C. | Classification |
| D. | None of above |
| Answer» E. | |
| 207. |
if there is only a discrete number of possible outcomes (called categories),the process becomes a______. |
| A. | Regression |
| B. | Classification. |
| C. | Modelfree |
| D. | Categories |
| Answer» C. Modelfree | |
| 208. |
Reinforcement learning is particularly efficient when______________. |
| A. | the environment is not completely deterministic |
| B. | it\s often very dynamic |
| C. | it\s impossible to have a precise error measure |
| D. | All above |
| Answer» E. | |
| 209. |
Common deep learning applications include____ |
| A. | Image classification,Real-time visual tracking |
| B. | Autonomous car driving,Logistic optimization |
| C. | Bioinformatics,Speech recognition |
| D. | All above |
| Answer» E. | |
| 210. |
What is ‘Training set’? |
| A. | Training set is used to test the accuracy of the hypotheses generated by the learner. |
| B. | A set of data is used to discover the potentially predictive relationship. |
| C. | Both A & B |
| D. | None of above |
| Answer» C. Both A & B | |
| 211. |
According to____ , it’s a key success factor for the survival and evolution of all species. |
| A. | Claude Shannon\s theory |
| B. | Gini Index |
| C. | Darwin’s theory |
| D. | None of above |
| Answer» D. None of above | |
| 212. |
In reinforcement learning if feedback is negative one it is defined as____. |
| A. | Penalty |
| B. | Overlearning |
| C. | Reward |
| D. | None of above |
| Answer» B. Overlearning | |
| 213. |
In order to assess how much information is brought by each component, and the correlation among them, a useful tool is the . |
| A. | concuttent matrix |
| B. | convergance matrix |
| C. | supportive matrix |
| D. | covariance matrix |
| Answer» E. | |
| 214. |
dataset with many features contains information proportional to the independence of all features and their variance. |
| A. | normalized |
| B. | unnormalized |
| C. | both a & b |
| D. | none of the mentioned |
| Answer» C. both a & b | |
| 215. |
scikit-learn also provides a class for per- sample normalization, |
| A. | normalizer |
| B. | imputer |
| C. | classifier |
| D. | all above |
| Answer» B. imputer | |
| 216. |
Which of the following scale data by removing elements that don't belong to a given range or by considering a maximum absolute value. |
| A. | minmaxscaler |
| B. | maxabsscaler |
| C. | both a & b |
| D. | none of the mentioned |
| Answer» D. none of the mentioned | |
| 217. |
scikit-learn offers the class , which is responsible for filling the holes using a strategy based on the mean, median, or frequency |
| A. | labelencoder |
| B. | labelbinarizer |
| C. | dictvectorizer |
| D. | imputer |
| Answer» E. | |
| 218. |
produce sparse matrices of real numbers that can be fed into any machine learning model. |
| A. | dictvectorizer |
| B. | featurehasher |
| C. | both a & b |
| D. | none of the mentioned |
| Answer» D. none of the mentioned | |
| 219. |
While using all labels are turned into sequential numbers. |
| A. | labelencoder class |
| B. | labelbinarizer class |
| C. | dictvectorizer |
| D. | featurehasher |
| Answer» B. labelbinarizer class | |
| 220. |
provides some built-in datasets that can be used for testing purposes. |
| A. | scikit-learn |
| B. | classification |
| C. | regression |
| D. | none of the above |
| Answer» B. classification | |
| 221. |
Which of the following are several models |
| A. | regression |
| B. | classification |
| C. | none of the above |
| Answer» D. | |
| 222. |
While using feature selection on the data, is the number of features decreases. |
| A. | no |
| B. | yes |
| Answer» C. | |
| 223. |
Can we extract knowledge without apply feature selection |
| A. | yes |
| B. | no |
| Answer» B. no | |
| 224. |
Which of the following model model include a backwards elimination feature selection routine? |
| A. | mcv |
| B. | mars |
| C. | mcrs |
| D. | all above |
| Answer» C. mcrs | |
| 225. |
Which of the following is an example of a deterministic algorithm? |
| A. | pca |
| B. | k-means |
| C. | none of the above |
| Answer» B. k-means | |
| 226. |
overlearning causes due to an excessive . |
| A. | capacity |
| B. | regression |
| C. | reinforcement |
| D. | accuracy |
| Answer» B. regression | |
| 227. |
A supervised scenario is characterized by the concept of a . |
| A. | programmer |
| B. | teacher |
| C. | author |
| D. | farmer |
| Answer» C. author | |
| 228. |
According to , its a key success factor for the survival and evolution of all species. |
| A. | claude shannon\s theory |
| B. | gini index |
| C. | darwins theory |
| D. | none of above |
| Answer» D. none of above | |
| 229. |
In reinforcement learning if feedback is negative one it is defined as . |
| A. | penalty |
| B. | overlearning |
| C. | reward |
| D. | none of above |
| Answer» B. overlearning | |
| 230. |
Techniques involve the usage of both labeled and unlabeled data is called . |
| A. | supervised |
| B. | semi-supervised |
| C. | unsupervised |
| D. | none of the above |
| Answer» C. unsupervised | |
| 231. |
When it is necessary to allow the model to develop a generalization ability and avoid a common problem called . |
| A. | overfitting |
| B. | overlearning |
| C. | classification |
| D. | regression |
| Answer» B. overlearning | |
| 232. |
What does learning exactly mean? |
| A. | robots are programed so that they can perform the task based on data they gather from sensors. |
| B. | a set of data is used to discover the potentially predictive relationship. |
| C. | learning is the ability to change according to external stimuli and remembering most of all previous experiences. |
| D. | it is a set of data is used to discover the potentially predictive relationship. |
| Answer» D. it is a set of data is used to discover the potentially predictive relationship. | |
| 233. |
Even if there are no actual supervisors learning is also based on feedback provided by the environment |
| A. | supervised |
| B. | reinforcement |
| C. | unsupervised |
| D. | none of the above |
| Answer» C. unsupervised | |
| 234. |
Which are two techniques of Machine Learning ? |
| A. | genetic programming and inductive learning |
| B. | speech recognition and regression |
| C. | both a & b |
| D. | none of the mentioned |
| Answer» B. speech recognition and regression | |
| 235. |
What is Model Selection in Machine Learning? |
| A. | the process of selecting models among different mathematical models, which are used to describe the same data set |
| B. | when a statistical model describes random error or noise instead of underlying relationship |
| C. | find interesting directions in data and find novel observations/ database cleaning |
| D. | all above |
| Answer» B. when a statistical model describes random error or noise instead of underlying relationship | |
| 236. |
Which of the following is not Machine Learning? |
| A. | artificial intelligence |
| B. | rule based inference |
| C. | both a & b |
| D. | none of the mentioned |
| Answer» C. both a & b | |
| 237. |
What is the standard approach to supervised learning? |
| A. | split the set of example into the training set and the test |
| B. | group the set of example into the training set and the test |
| C. | a set of observed instances tries to induce a general rule |
| D. | learns programs from data |
| Answer» B. group the set of example into the training set and the test | |
| 238. |
What are the different Algorithm techniques in Machine Learning? |
| A. | supervised learning and semi-supervised learning |
| B. | unsupervised learning and transduction |
| C. | both a & b |
| D. | none of the mentioned |
| Answer» D. none of the mentioned | |
| 239. |
The linearSVMclassifier works by drawing a straight line between two classes |
| A. | true |
| B. | false |
| Answer» B. false | |
| 240. |
SVM is a learning |
| A. | supervised |
| B. | unsupervised |
| C. | both |
| D. | none |
| Answer» B. unsupervised | |
| 241. |
SVM is a algorithm |
| A. | classification |
| B. | clustering |
| C. | regression |
| D. | all |
| Answer» B. clustering | |
| 242. |
Solving a non linear separation problem with a hard margin Kernelized SVM (Gaussian RBF Kernel) might lead to overfitting |
| A. | true |
| B. | false |
| Answer» B. false | |
| 243. |
Any linear combination of the components of a multivariate Gaussian is a univariate Gaussian. |
| A. | true |
| B. | false |
| Answer» B. false | |
| 244. |
SVMs directly give us the posterior probabilities P(y = 1jx) and P(y = ??1jx) |
| A. | true |
| B. | false |
| Answer» C. | |
| 245. |
Gaussian distribution when plotted, gives a bell shaped curve which is symmetric about the of the feature values. |
| A. | mean |
| B. | variance |
| C. | discrete |
| D. | random |
| Answer» B. variance | |
| 246. |
Binarize parameter in BernoulliNB scikit sets threshold for binarizing of sample features. |
| A. | true |
| B. | false |
| Answer» B. false | |
| 247. |
Gaussian Nave Bayes Classifier is distribution |
| A. | continuous |
| B. | discrete |
| C. | binary |
| Answer» B. discrete | |
| 248. |
Multinomial Nave Bayes Classifier is distribution |
| A. | continuous |
| B. | discrete |
| C. | binary |
| Answer» C. binary | |
| 249. |
Bernoulli Nave Bayes Classifier is distribution |
| A. | continuous |
| B. | discrete |
| C. | binary |
| Answer» D. | |
| 250. |
Bayes theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event. |
| A. | true |
| B. | false |
| Answer» B. false | |