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Basic Machine learning MCQ

1. When is a decision tree most commonly used?

With big data products

For supervised machine learning binary classification challenges

To find thd best data cluster

To determine

Answer

Correct Answer: For supervised machine learning binary classification challenges

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2. Which project might be best suited for supervised machine learning?

Data scrubbing

Predicting a risk score

Tax filing software

Spreadsheet consolidation

Answer

Correct Answer: Predicting a risk score

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3. What is the best definition for bias in your data model?

Bias is when your predicted values are scattered.

Bias is the gap between your predicted value and the outcome.

Bias is when your data is wrong for differnet reasons.

Bias is when your values are always off by the same percentage.

Answer

Correct Answer: Bias is the gap between your predicted value and the outcome.

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4. What is ensemble modeling?

When you create an ensemble of your training and test data set

When you create an ensemble of different servers to run the algorithms

When you find the one best algorithm for your ensemble

When you use several ensembles of machine learning algorithms

Answer

Correct Answer: When you use several ensembles of machine learning algorithms

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5. K-means clustering is what type of machine learning algorithm?

Reinforcement

Supervised

Unsupervised

Classification

Answer

Correct Answer: Unsupervised

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6. What is conditional probability?

The probability that doing one thing has an impact on another thing

The probability that certain conditions are met

The probability that, based on certain conditions, something will always be incorrect

The probability of something being the correct answer

Answer

Correct Answer: The probability that doing one thing has an impact on another thing

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7. In supervised machine learning, data scientist often have the challenge of balancing between underfitting or overfitting their data model. They often have to adjust the training set to make better predictions. What is this balance called?

The under/over challenge

Balance between clustering classification

Bias-variance trade-off

The multiclass training set challenge

Answer

Correct Answer: Bias-variance trade-off

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8. You work for a startup that is trying to develop a software tool that will scan the internet for pictures of people using specific tools. The chief executive is very interested in using machine learning algorithms. What would you recommend as the best place to start?

Using an unsupervised machine learning algorithm to cluster together all the photographs.

Crate a data lake with an unsupervised machine learning algorithm.

Use a combination of unsupervised and supervised machine learning to create machine-defined data clusters.

Use supervised machine learning to classify photographs based on a predetermined training set.

Answer

Correct Answer: Use supervised machine learning to classify photographs based on a predetermined training set.

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9. What is the difference between unstructured and structured data?

Unstructured data is always text.

Unstructured data is much easier to store.

Structured data has clearly defined data types.

Sturctured data is much more popular.

Answer

Correct Answer: Structured data has clearly defined data types.

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10. What is one of the most effective way to correct for underfitting your model to the data?

Create training clusters

Remove predictors

Use reinforcement learning

Add more predictors

Answer

Correct Answer: Add more predictors

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11. In the 1983 movie WarGames, the computer learns how to master the game of chess by playing against itself. What machine learning method was the computer using?

Binary learning

Supervised learning

Unsupervised learning

Reinforcement learning

Answer

Correct Answer: Reinforcement learning

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12. The data in your model has low bias and low variance. How would you expect the data points to be grouped together on the diagram?

They would be grouped tightly together in the predicted outcome.

They would be grouped tightly together but far from the predicted.

They would be scattered around the predict outcome.

They would be scattered far away from the predicted outcome.

Answer

Correct Answer: They would be grouped tightly together in the predicted outcome.

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13. What is lazy learning?

When the machine learning algorithms do most of the programming

When you don't do any data scrubbing

When the learning happens continuously

When you run your computation in one big instance at the beginning

Answer

Correct Answer: When the learning happens continuously

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14. In statistics, what is defined as the probability of a hypothesis test of finding an effect - if there is an effect to be found?

Confidence

Alpha

Power

Significance

Answer

Correct Answer: Power

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15. Which choice is the best example of labeled data?

A spreadsheet

20,000 recorded voicemail messages

100,000 images of automobiles

Hundreds of gigabytes of audio files

Answer

Correct Answer: A spreadsheet

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16. You need to select a machine learning process to run a distributed neural network on a mobile application. Which would you choose?

Scikit-learn

PyTorch

Tensowflow Lite

Tensorflow

Answer

Correct Answer: Tensowflow Lite

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17. You need to quickly label thousands of images to train a model. What should you do?

Set up a cluster of machines to label the images

Create a subset of the images and label then yourself

Use naive Bayes to automatically generate labels.

Hire people to manually label the images

Answer

Correct Answer: Use naive Bayes to automatically generate labels.

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18. Which loss function would fit best in a categorical (discrete) supervised learning ?

Kullback-leibler (KL) loss

Binary Crossentropy

Mean Squared Error (MSE)

Any L2 loss

Answer

Correct Answer: Binary Crossentropy

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19. What is the main motivation for using activation functions in ANN?

Capturing complex non-linear patterns

Transforming continuous values into

Help avoiding the vanishing/exploding gradient problem

Their ability to activate each neurons individually.

Answer

Correct Answer: Capturing complex non-linear patterns

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20. Which of the following methods can use either as an unsupervised learning or as a dimensionality reduction technique?

SVM

PCA

LDA

TSNE

Answer

Correct Answer: PCA

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21. The new dataset you have just scraped seems to exhibit lots of missing values. What action will help you minimizing that problem?

Wise fill-in of controlled random values

Replace missing values with averaging across all samples

Remove defective samples

Imputation

Answer

Correct Answer: Imputation

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22. (Mostly) whenever we see kernel visualizations online (or some other reference) we are actually seeing:

What kernels extract

Feature Maps

How kernels Look

Answer

Correct Answer: What kernels extract

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23. Asian user complains that your company's facial recognition model does not properly identify their facial expressions. What should you do?

Include Asian faces in your test data and retrain your model.

Retrain your model with updated hyperparameter values.

Retrain your model with smaller batch sizes.

Include Asian faces in your training data and retrain your model.

Answer

Correct Answer: Include Asian faces in your training data and retrain your model.

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24. What does it mean to underfit your data model?

There is too little data in your training set.

There is too much data in your training set.

There is not a lot of variance but there is a high bias.

Your model has low bias but high variance.

Answer

Correct Answer: There is not a lot of variance but there is a high bias.

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25. ___ refers to a model that can neither model the training data nor generalize to new data.

Good fitting

Overfitting

Underfitting

All of the above

Answer

Correct Answer: Underfitting

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26. Compared to the variance of the Maximum Likelihood Estimate (MLE), the variance of the Maximum A Posteriori (MAP) estimate is ___

Higher

Same

Lower

It could be any of the above

Answer

Correct Answer: Lower

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27. The error function most suited for gradient descent using logistic regression is

The entropy function.

The squared error.

The cross-entropy function.

The number of mistakes.

Answer

Correct Answer: The cross-entropy function.

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28. Suppose we would like to perform clustering on spatial data such as the geometrical locations of houses. We wish to produce clusters of many different sizes and shapes. Which of the following methods is the most appropriate?

Decision Trees

K-means clustering

Density-based clustering

Model-based clustering

Answer

Correct Answer: Density-based clustering

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29. Which of the following is NOT supervised learning?

Decision Tree

Linear Regression

PCA

Naive Bayesian

Answer

Correct Answer: PCA

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30. The model will be trained with data in one single batch is known as ?

Batch learning

Offline learning

Both A and B

None of the above

Answer

Correct Answer: Both A and B

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31. Your data science team is working on a machine learning product that can act as an artificial opponent in video games. The team is using a machine learning algorithm that focuses on rewards: If the machine does some things well, then it improves the quality of the outcome. How would you describe this type of machine learning algorithm?

Semi-supervised machine learning

Supervised machine learning

Unsupervised machine learning

Reinforcement learning

Answer

Correct Answer: Reinforcement learning

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32. You and your data science team have 1 TB of example data. What do you typically do with that data?

You use it as your training set.

You label it big data.

You split it into a training set and test set.

You use it as your test set.

Answer

Correct Answer: You label it big data.

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33. You work for a website that enables customers see all images of themselves on the internet by uploading one self-photo. Your data model uses 5 characteristics to match people to their foto: color, eye, gender, eyeglasses and facial hair. Your customers have been complaining that get tens of thousands of photos without them. What is the problem?

You are overfitting the model to the data

You need a smaller training set

You are underfitting the model to the data

You need a larger training set

Answer

Correct Answer: You are underfitting the model to the data

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34. Your data science team wants to use machine learning to better filter out spam messages. The team has gathered a database of 100,000 messages that have been identified as spam or not spam. If you are using supervised machine learning, what would you call this data set?

Machine learning algorithm

Training set

Big data test set

Data cluster

Answer

Correct Answer: Training set

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35. Someone of your data science team recommends that you use decision trees, naive Bayes and K-nearest neighbor, all at the same time, on the same training data, and then average the results. What is this an example of?

Regression analysis

Unsupervised learning

High -variance modeling

Ensemble modeling

Answer

Correct Answer: Ensemble modeling

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36. Many of the advances in machine learning have come from improved ___.

Statistics

Structured data

Availability

Algorithms

Answer

Correct Answer: Algorithms

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37. You work for a hospital that is tracking the community spread of a virus. The hospital created a smartwatch application that uploads body temperature data from hundreds of thousands of participants. What is the best technique to analyze the data?

Use reinforcement learning to reward the system when a new person participates.

Use unsupervised machine learning to cluster together people based on patterns the machine discovers.

Use Supervised machine learning to sort people by demographic data.

Use Supervised machine learning to classify people by body temperature.

Answer

Correct Answer: Use Supervised machine learning to classify people by body temperature.

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38. The security company you work for is thinking about adding machine learning algorithms to their computer network threat detection appliance. What is one advantage of using machine learning?

It could better protect against undiscovered threats.

It would very likely lower the hardware requirements.

It would substantially shorten your development time.

It would increase the speed of the appliance.

Answer

Correct Answer: It could better protect against undiscovered threats.

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39. In 2015, Google created a machine learning system that could beat a human in the game of Go. This extremely complex game is thought to have more gameplay possibilities than there are atoms of the universe. The first version of the system won by observing hundreds of thousands of hours of human gameplay; the second version learned how to play by getting rewards while playing against itself. How would you describe this transition to different machine learning approaches?

The system went from supervised learning to reinforcement learning.

The system evolved from supervised learning to unsupervised learning.

The system evolved from unsupervised learnin9 to supervised learning.

The system evolved from reinforcement learning to unsupervised learning.

Answer

Correct Answer: The system went from supervised learning to reinforcement learning.

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40. You work for a large pharmaceutical company whose data science team wants to use unsupervised learning machine algorithms to help discover new drugs. What is an advantage to this approach?

You will be able to prioritize different classes of drugs, such as antibiotics.

You can create a training set of drugs you would like to discover.

The algorithms will cluster together drugs that have similar traits.

Human experts can create classes of drugs to help guide discovery.

Answer

Correct Answer: The algorithms will cluster together drugs that have similar traits.

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41. In the HBO show Silicon Valley, one of the characters creates a mobile application called Not Hot Dog. It works by having the user take a photograph of food with their mobile device. Then the app says whether the food is a hot dog. To create the app, the software developer uploaded hundreds of thousands of pictures of hot dogs. How would you describe this type of machine learning?

Reinforcement machine learning

Unsupervised machine learning

Supervised machine learning

Semi-supervised machine learning

Answer

Correct Answer: Supervised machine learning

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42. In K-nearest neighbor, the closer you are to neighbor, the more likely you are to

Share common characteristics

Be part of the root node

Have a Euclidean connection

Be part of the same cluster

Answer

Correct Answer: Share common characteristics

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43. Why is it important for machine learning algorithms to have access to high-quality data?

It will take too long for programmers to scrub poor data.

If the data is high quality, the algorithms will be easier to develop.

Low-quality data requires much more processing power than high-quality data.

If the data is low quality, you will get inaccurate results.

Answer

Correct Answer: If the data is low quality, you will get inaccurate results.

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44. With traditional programming, the programmer typically inputs commands. With machine learning, the programmer inputs

Supervised learning

Data

Unsupervised learning

Algorithms

Answer

Correct Answer: Data

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45. Which choice is best for binary classification?

K-means

Logistic regression

Linear regression

Principal Component Analysis (PCA)

Answer

Correct Answer: Logistic regression

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46. Are data model bias and variance a challenge with unsupervised learning?

No, data model bias and variance are only a challenge with reinforcement learning.

Yes, data model bias is a challenge when the machine creates clusters.

Yes, data model variance trains the unsupervised machine learning algorithm.

No, data model bias and variance involve supervised learning.

Answer

Correct Answer: Yes, data model bias is a challenge when the machine creates clusters.

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47. You are using K-nearest neighbor and you have a K of 1. What are you likely to see when you train the model?

High variance and low bias

Low bias and low variance

Low variance and high bias

High bias and high variance

Answer

Correct Answer: High variance and low bias

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48. You work for a large credit card processing company that wants to create targeted promotions for its customers. The data science team created a machine learning system that groups together customers who made similar purchases, and divides those customers based on customer loyalty. How would you describe this machine learning approach?

It uses unsupervised learning to cluster together transactions and unsupervised learning to classify the customers.

It uses only unsupervised machine learning.

It uses supervised learning to create clusters and unsupervised learning for classification.

It uses reinforcement learning to classify the customers.

Answer

Correct Answer: It uses unsupervised learning to cluster together transactions and unsupervised learning to classify the customers.

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49. Your machine learning system is attempting to describe a hidden structure from unlabeled data. How would you describe this machine learning method?

Supervised learning

Unsupervised learning

Reinforcement learning

Semi-unsupervised learning

Answer

Correct Answer: Unsupervised learning

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50. Your data science team wants to use the K-nearest neighbor classification algorithm. Someone on your team wants to use a K of 25. What are the challenges of this approach?

Higher K values will produce noisy data.

Higher K values lower the bias but increase the variance.

Higher K values need a larger training set.

Higher K values lower the variance but increase the bias.

Answer

Correct Answer: Higher K values lower the variance but increase the bias.

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51. Your data science team must build a binary classifier, and the number one criterion is the fastest possible scoring at deployment. It may even be deployed in real time. Which technique will produce a model that will likely be fastest for the deployment team use to new cases?

Random forest

Logistic regression

KNN

Deep neural network

Answer

Correct Answer: Logistic regression

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52. You created machine learning system that interacts with its environment and responds to errors and rewards. What type of machine learning system is it?

Supervised learning

Semi-supervised learning

Reinforcement learning

Unsupervised learning

Answer

Correct Answer: Reinforcement learning

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53. Which statement about K-means clustering is true?

In K-means clustering, the initial centroids are sometimes randomly selected.

K-means clustering is often used in supervised machine learning.

The number of clusters are always randomly selected.

To be accurate, you want your centroids outside of the cluster.

Answer

Correct Answer: In K-means clustering, the initial centroids are sometimes randomly selected.

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54. Self-organizing maps are specialized neural network for which type of machine learning?

Semi-supervised learning

Supervised learning

Reinforcement learning

Unsupervised learning

Answer

Correct Answer: Semi-supervised learning

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55. Random forest is modified and improved version of which earlier technique?

Aggregated trees

Boosted trees

Bagged trees

Stacked trees

Answer

Correct Answer: Bagged trees

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56. Your organization allows people to create online professional profiles. A key feature is the ability to create clusters of people who are professionally connected to one another. What type of machine learning method is used to create these clusters?

Unsupervised machine learning

Binary classification

Supervised machine learning

Reinforcement learning

Answer

Correct Answer: Unsupervised machine learning

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57. Your company wants you to build an internal email text prediction model to speed up the time that employees spend writing emails. What should you do?

Include training email data from all employees.

Include training email data from new employees.

Include training email data from seasoned employees.

Include training email data from employees who write the majority of internal emails.

Answer

Correct Answer: Include training email data from all employees.

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58. You are working on a project that involves clustering together images of different dogs. You take image and identify it as your centroid image. What type machine learning algorithm are you using?

Centroid reinforcement

K-nearest neighbor

Binary classification

K-means clustering

Answer

Correct Answer: K-means clustering

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59. What is stacking?

The predictions of one model become the inputs another.

You use different versions of machine learning algorithms.

You use several machine learning algorithms to boost your results.

You stack your training set and testing set together.

Answer

Correct Answer: The predictions of one model become the inputs another.

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60. What is one reason not to use the same data for both your training set and your testing set?

You will almost certainly underfit the model.

You will pick the wrong algorithm.

You might not have enough data for both.

You will almost certainly overfit the model.

Answer

Correct Answer: You will almost certainly overfit the model.

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61. You work for an insurance company. Which machine learning project would add the most value for the company!

Create an artificial neural network that would host the company directory.

Use machine learning to better predict risk.

Create an algorithm that consolidates all of your Excel spreadsheets into one data lake.

Use machine learning and big data to research salary requirements.

Answer

Correct Answer: Use machine learning to better predict risk.

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62. How do machine learning algorithms make more precise predictions?

The algorithms are typically run more powerful servers.

The algorithms are better at seeing patterns in the data.

Machine learning servers can host larger databases.

The algorithms can run on unstructured data.

Answer

Correct Answer: The algorithms are better at seeing patterns in the data.

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63. How is machine learning related to artificial intelligence?

Artificial intelligence focuses on classification, while machine learning is about clustering data.

Machine learning is a type of artificial intelligence that relies on learning through data.

Artificial intelligence is form of unsupervised machine learning.

Machine learning and artificial intelligence are the same thing.

Answer

Correct Answer: Machine learning is a type of artificial intelligence that relies on learning through data.

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64. Why is naive Bayes called naive?

It naively assumes that you will have no data.

It does not even try to create accurate predictions.

It naively assumes that the predictors are independent from one another.

It naively assumes that all the predictors depend on one another.

Answer

Correct Answer: It naively assumes that the predictors are independent from one another.

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65. To predict a quantity value. use ___.

Regression

Clustering

Classification

Dimensionality reduction

Answer

Correct Answer: Regression

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66. You work for a power company that owns hundreds of thousands of electric meters. These meters are connected to the internet and transmit energy usage data in real-time. Your supervisor asks you to direct project to use machine learning to analyze this usage data. Why are machine learning algorithms ideal in this scenario?

The algorithms would help the meters access the internet.

The algorithms will improve the wireless connectivity.

The algorithms would help your organization see patterns of the data.

By using machine learning algorithms, you are creating an IoT device.

Answer

Correct Answer: The algorithms would help your organization see patterns of the data.

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67. What is an example of a commercial application for a machine learning system?

A data entry system

A data warehouse system

A massive data repository

A product recommendation system

Answer

Correct Answer: A product recommendation system

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68. ____ looks at the relationship between predictors and your outcome.

Regression analysis

K-means clustering

Big data

Unsupervised learning

Answer

Correct Answer: Regression analysis

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69. You work in a data science team that wants to improve the accuracy of its K-nearest neighbor result by running on top of a naive Bayes result. What is this an example of?

Regression

Boosting

Bagging

Stacking

Answer

Correct Answer: Stacking

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70. You want to identify global weather patterns that may have been affected by climate change. To do so, you want to use machine learning algorithms to find patterns that would otherwise be imperceptible to a human meteorologist. What is the place to start?

Find labeled data of sunny days so that the machine will learn to identify bad weather.

Use unsupervised learning have the machine look for anomalies in a massive weather database.

Create a training set of unusual patterns and ask the machine learning algorithms to classify them.

Create a training set of normal weather and have the machine look for similar patterns.

Answer

Correct Answer: Use unsupervised learning have the machine look for anomalies in a massive weather database.

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71. Your company wants to predict whether existing automotive insurance customers are more likely to buy homeowners insurance. It created a model to better predict the best customers contact about homeowners insurance, and the model had a low variance but high bias. What does that say about the data model?

It was consistently wrong.

It was inconsistently wrong.

It was consistently right.

It was equally right end wrong.

Answer

Correct Answer: It was consistently wrong.

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72. In traditional computer programming, you input commands. What do you input with machine learning?

Patterns

Programs

Rules

Data

Answer

Correct Answer: Data

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73. You work for an organization that sells a spam filtering service to large companies. Your organization wants to transition its product to use machine learning. It currently a list Of 250,00 keywords. If a message contains more than few of these keywords, then it is identified as spam. What would be one advantage of transitioning to machine learning?

The product would look for new patterns in spam messages.

The product could go through the keyword list much more quickly.

The product could have a much longer keyword list.

The product could find spam messages using far fewer keywords.

Answer

Correct Answer: The product could find spam messages using far fewer keywords.

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74. You are part of data science team that is working for a national fast-food chain. You create a simple report that shows trend: Customers who visit the store more often and buy smaller meals spend more than customers who visit less frequently and buy larger meals. What is the most likely diagram that your team created?

Multiclass classification diagram

Linear regression and scatter plots

Pivot table

K-means cluster diagram

Answer

Correct Answer: Linear regression and scatter plots

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75. Which of the following features or algorithms are supported by the Scikit—Learn tool?

Data preparation

Classif‌ication

Model Selection

Visualization

Answer

Correct Answer: Classif‌ication
Model Selection

Note: This question has more than 1 correct answers

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76. In relation to machine learning classif‌ication, which two of the following are the correct features of ID3 (Iterative Dischotomiser 3) algorithm?

It supports global optimization.

It is easy to understand.

It supports back tracking search.

It cannot handle missing values.

Answer

Correct Answer: It supports back tracking search.

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77. Which of the following solutions can be used for the noisy data classification problem of ID3 and C45 decision tree algorithm?

Use Gaussian kernel function

Use Credal-C4.5 tree

Use enhanced algorithm with Taylor formula

Use greedy learning algorithm

Answer

Correct Answer: Use Credal-C4.5 tree
Use enhanced algorithm with Taylor formula

Note: This question has more than 1 correct answers

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78. The Amazon Machine Learning tool supports which of the following types of models?

Binary classification

Multi-class classification

Regression

Multi-level classification

Answer

Correct Answer: Binary classification

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79. Which of the following are the advantages of the Naive Bayes algorithm used for classification?

Only a small amount of training data is required for estimating the necessary parameters.

It is well suited for spam f‌iltering

It is fast as compared to sophisticated methods.

It is an excellent estimator.

Answer

Correct Answer: It is fast as compared to sophisticated methods.

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80. Which of the following statements are correct about the Gradient Boosting classif‌ication technique?

In this technique, training is done in parallel.

It is hard to tune.

It is easy to overf‌it.

It implements unweighted voting for final prediction

Answer

Correct Answer: In this technique, training is done in parallel.
It is hard to tune.

Note: This question has more than 1 correct answers

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81. In relation to machine learning, which of the following statements are correct about the TensorFIow library?

It can work eff‌iciently with mathematical expressions that involve multi-dimensional arrays.

TensorFlow is only supported by Google.

It is not efficient in multi-GPU setting.

It runs on a single CPU systems. GPUs. and mobile devices.

Answer

Correct Answer: TensorFlow is only supported by Google.

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82. Which of the following are the correct features of the Random Forest algorithm used for classif‌ication?

It increases the over-f‌itting.

The real time prediction is fast.

It is diff‌icult to implement.

It is a complex algorithm.

Answer

Correct Answer: The real time prediction is fast.

Note: This Question is unanswered, help us to find answer for this one

83. In relation to machine learning classif‌ication algorithm, which of the following are the linear classif‌iers?

Logistic regression

Native Bayes classif‌ier

Least square support vector machine

Random Forest

Answer

Correct Answer: Least square support vector machine
Random Forest

Note: This question has more than 1 correct answers

Note: This Question is unanswered, help us to find answer for this one

84. Which of the following options are the advantages of K-Nearest Neighbor (KNN) classif‌ication technique?

It provides excellent run-time performance.

Its computation complexity is very low.

It is easy to implement.

It is robust to noisy training data.

Answer

Correct Answer: Its computation complexity is very low.
It is easy to implement.
It is robust to noisy training data.

Note: This question has more than 1 correct answers

Note: This Question is unanswered, help us to find answer for this one

85. Which two of the following are the machine learning classification problems?

Predicting the price of a shop based on area.

Predicting a person’s gender by analyzing his/her handwriting style.

Predicting if snowfall will be normal this year.

Predicting the number of copies of a book that will be sold next fortnight

Answer

Correct Answer: Predicting the price of a shop based on area.
Predicting the number of copies of a book that will be sold next fortnight

Note: This question has more than 1 correct answers

Note: This Question is unanswered, help us to find answer for this one

86. Which of the following options are the machine learning tools for a command line interface?

Waff‌les

WEKA Machine Learning Workbench

Pylearn2

Deeplearning4j

Answer

Correct Answer: Waff‌les
WEKA Machine Learning Workbench

Note: This question has more than 1 correct answers

Note: This Question is unanswered, help us to find answer for this one

87. Which of the following options are the advantages of the Multilayer Perceptron?

It can learn non-linear models.

It can learn models in real-time.

It does not require the tuning of any parameter.

It is indifferent to feature scaling.

Answer

Correct Answer: It can learn non-linear models.
It can learn models in real-time.

Note: This question has more than 1 correct answers

Note: This Question is unanswered, help us to find answer for this one

88. Which of the following statements are correct about the Shogun machine learning library?

It is open-source.

It is cross-platform and API oriented.

It has a maintained library with core implementation in Java.

it has detailed documentation.

Answer

Correct Answer: It is open-source.
It is cross-platform and API oriented.

Note: This question has more than 1 correct answers

Note: This Question is unanswered, help us to find answer for this one

89. In relation to machine learning, which of the following options are the toolkits for working with human language data?

Gensim

SciPy

NLTK

Pandas

Answer

Correct Answer: Gensim
NLTK

Note: This question has more than 1 correct answers

Note: This Question is unanswered, help us to find answer for this one

90. APls are included for which of the following options in TensorFlow?

C++

Java

Python

Answer

Correct Answer: C++
Python

Note: This question has more than 1 correct answers

Note: This Question is unanswered, help us to find answer for this one

91. Which of the following frameworks can be used for cross-platform solutions?

TensorFlow

PyTorch

Caffe2

Try 1-2

Answer

Correct Answer: TensorFlow
Caffe2

Note: This question has more than 1 correct answers

Note: This Question is unanswered, help us to find answer for this one

92. In Convolutional Neural Networks, the input can be in which of the following formats?

One-dimensional

Two-dimensional

Three-dimensional

Five-dimensional

Answer

Correct Answer: One-dimensional
Two-dimensional

Note: This question has more than 1 correct answers

Note: This Question is unanswered, help us to find answer for this one

93. In relation to machine learning classif‌ication algorithm, which of the following options are used by the decision tree for constructing a decision tree?

Entropy

Information Gain

Probability

Try 1-2

Answer

Correct Answer: Entropy
Information Gain
Try 1-2

Note: This question has more than 1 correct answers

Note: This Question is unanswered, help us to find answer for this one

94. Which of the following are the correct features of the KNIME tool for machine learning?

It can integrate the code of programming languages such as C, C++, R, and Java, etc.

It can build complicated models very easily.

It provides very high visualization and exporting capabilities.

Its deployment and installation is very easy.

Answer

Correct Answer: It can integrate the code of programming languages such as C, C++, R, and Java, etc.
Its deployment and installation is very easy.

Note: This question has more than 1 correct answers

Note: This Question is unanswered, help us to find answer for this one

95. In machine learning, which of the following statements are correct about optical coherence tomography (OCT)?

It has a very limited OCT imaging depth.

It is hard to interpret an OCT image.

El It is easy to obtain an OCT image.

Answer

Correct Answer: It has a very limited OCT imaging depth.
It is hard to interpret an OCT image.

Note: This question has more than 1 correct answers

Note: This Question is unanswered, help us to find answer for this one

96. The LibLinear library is written in which of the following languages?

C++

Python

Answer

Correct Answer: C
C++

Note: This question has more than 1 correct answers

Note: This Question is unanswered, help us to find answer for this one

97. Which two of the following statements are correct regarding classif‌ication, which is a machine learning technique?

It uses unknown data in order to determine how new data is classif‌ied into a set of existing categories.

It uses known data in order to determine how new data is classif‌ied into a set of existing categories.

It is a form of supervised learning.

It is a form of unsupervised learning.

Answer

Correct Answer: It uses known data in order to determine how new data is classif‌ied into a set of existing categories.
It is a form of supervised learning.

Note: This question has more than 1 correct answers

Note: This Question is unanswered, help us to find answer for this one

98. In relation to machine learning classif‌ication, which of the following are the correct features of the Stochastic Gradient Descent algorithm?

It supports many loss functions and penalties for classif‌ication.

It is easy and eff‌icient to implement.

It requires a very small number of hyper-parameters.

It is indifferent to feature scaling.

Answer

Correct Answer: It supports many loss functions and penalties for classif‌ication.
It is easy and eff‌icient to implement.

Note: This question has more than 1 correct answers

Note: This Question is unanswered, help us to find answer for this one

99. Which of the following options are the correct features of the PyTorch framework?

It supports the data parallelism.

It supports distributed learning model.

It is only suitable for large projects or prototypes.

It does not contain any pre—trained model.

Answer

Correct Answer: It supports the data parallelism.
It supports distributed learning model.

Note: This question has more than 1 correct answers

Note: This Question is unanswered, help us to find answer for this one

100. Which of the following types of artif‌icial neural networks is/ are best suited for mapping the image data to an output variable?

Multilayer Perceptrons (MLPs)

Convolutional Neural Networks (CNNS)

Recurrent Neural Networks (RNNS)

Both options b and c

Answer

Correct Answer: Convolutional Neural Networks (CNNS)

Note: This Question is unanswered, help us to find answer for this one

101.
Which of the following classif‌ication techniques should be used for the given tasks?
1. Selection of question pools
2. Prediction of software defects
3. Analysis of coal logistics customer
4. Thrombosis collagen diseases

|D3

C45

K—Nearest neighbors

SVM

Answer

Correct Answer:

C45

Note: This Question is unanswered, help us to find answer for this one

102. Which of the following supervised learning algorithms is/are implemented by Apache Mahout?

Naive Bayes classifiers

Support Vector Machines (SVMs)

Neural Networks

Both a and c

Answer

Correct Answer: Naive Bayes classifiers

Note: This Question is unanswered, help us to find answer for this one

103. Which of the following classif‌ication algorithms should be used for the given scenario? The data is labeled and the number of samples is greater than 100k.

SGD Classif‌ier

Kernel Approximation

Linear SVC

None of the above.

Answer

Correct Answer: Linear SVC

Note: This Question is unanswered, help us to find answer for this one

104. In relation to SVM classification algorithm. which of the following options is used for the binary classification?

Polynomial kernel

Radial Basis Function kernel

Sigmoid kernel

None of the above.

Answer

Correct Answer: Sigmoid kernel

Note: This Question is unanswered, help us to find answer for this one

105. In data mining, which of the following clustering methods reflects spatial distribution of the data points?

Grid-based method

Model-based method

Partitioning method

Hierarchical method

Answer

Correct Answer: Model-based method

Note: This Question is unanswered, help us to find answer for this one

106. Which of the following formulae is used for correctly calculating the accuracy of the classification algorithms?

Accuracy: 2(True Positive + True Negative) I Total Population

Accuracy: (True Positive + True Negative) / Total Population

Accuracy: (True Positive + True Negative) / 2(Total Population)

Accuracy: (True Positive x True Negative) / Total Population

Answer

Correct Answer: Accuracy: (True Positive + True Negative) / Total Population

Note: This Question is unanswered, help us to find answer for this one

107. Which of the following interfaces is/are supported by Scikit—Learn tool?

GPU

API

Both GPU and API

Answer

Correct Answer: API

Note: This Question is unanswered, help us to find answer for this one

108. What is the FI-Score of Random Forest algorithm used for classification?

0.6737

0.5180

0.5224

0.6275

Answer

Correct Answer: 0.5224

Note: This Question is unanswered, help us to find answer for this one

109. What is the correct F1 score of the Random Forest algorithm of classif‌ication?

0.5924

0.5171

0.6518

0.6275

Answer

Correct Answer: 0.6275

Note: This Question is unanswered, help us to find answer for this one

110. Which of the following simulation model types contain(s)probability?

Deterministic simulations

Stochastic simulations

Both a and b

Neither a nor 0

Answer

Correct Answer: Stochastic simulations

Note: This Question is unanswered, help us to find answer for this one

111. Can decision trees over f‌it data?

Yes

Answer

Correct Answer: Yes

Note: This Question is unanswered, help us to find answer for this one

112.
Which of the following algorithms has the given applications?
1. Scene classification
2. Induction motors fault diagnosis
3. Analog circuit fault diagnosis
4. Corporate financial distress prediction

04.5

SVM

ID3

Bayesian Network

Answer

Correct Answer:

SVM

Note: This Question is unanswered, help us to find answer for this one

113. Which of the following boosting algorithms uses the level-wise tree growth?

XGBoost

Gradient Boosting

AdaBoost

Light GB

Answer

Correct Answer: Light GB

Note: This Question is unanswered, help us to find answer for this one

114. With respect to multilayer neural network, what does the neurons in the hidden layer correspond to?

Properties

Descriptors

Non-linear latent variables

Both a and c

Answer

Correct Answer: Non-linear latent variables

Note: This Question is unanswered, help us to find answer for this one

115. In data mining, which of the following is NOT a data reduction technique?

Clustering

Sampling

Histograms

Huffman

Answer

Correct Answer: Huffman

Note: This Question is unanswered, help us to find answer for this one

116.
Which of the following statements is/are correct?
Statement 1: In stochastic simulation environments, machine learning is performed by combining multiple transmutations.
Statement 2: Stochastic simulation is used for modelling a system whose operation can be directly captured by deterministic rules.

Statement 1 is correct.

Statement 2 is correct.

Both statements 1 and 2 are correct.

Both statements 1 and 2 are incorrect.

Answer

Correct Answer:

Statement 1 is correct.

Note: This Question is unanswered, help us to find answer for this one

117.
Find the output of the following code, when executed in MATLAB.
A = [8 4 7 5 3]
isinteger(A)
isf‌loat(A)
isvector(A)
isscalar(A)

A =

8 4 7 5 3

ans = 0

ans = 1

ans =1

ans =

A =

8 4 7 5 3

ans =1

ans = 0

ans =1

8 4 7 5 3

ans=0

ans=1

ans=0

ans=1

8 4 7 5 3

ans=1

ans=0

Answer

Correct Answer:

A =

8 4 7 5 3

ans = 0

ans = 1

ans =1

ans =

Note: This Question is unanswered, help us to find answer for this one

118. The EIasticNet regression technique:

cannot suffer with double shrinkage.

discourages group effect in case of highly correlated variables.

is a hybrid of lasso and linear regression techniques.

does not have any limitations on the number of selected variables.

Answer

Correct Answer: does not have any limitations on the number of selected variables.

Note: This Question is unanswered, help us to find answer for this one

119.
Consider the following code to be executed in MATLAB.
m = roots([?., 8])
What will be the output?

m = 4

m = -4

m =16

m = -16

Answer

Correct Answer:

m = -4

Note: This Question is unanswered, help us to find answer for this one

120.
Which Of the following statistical data mining techniques is used to predict a categorical response variable?

Mixed-effect model

Factor analysis

Regression

Generalized linear model

Answer

Correct Answer:

Factor analysis

Note: This Question is unanswered, help us to find answer for this one

121.
What will be the output of the following code, when executed in MATLAB?
z = [6 9 4 3 5]:
polyval(z,3)

ans= 779

ans= 773

ans = 821

ans= 782

Answer

Correct Answer:

ans= 779

Note: This Question is unanswered, help us to find answer for this one

122.
In relation to machine learning classif‌ication, which of following options refers to the graphical model for probability associations between a set of variables?

Bayesian Network

K—Nearest Neighbors

lbs

SVM

Answer

Correct Answer:

Bayesian Network

Note: This Question is unanswered, help us to find answer for this one

123.
What will be the output of the following code, when executed in MATLAB?
X = 45;
Y = 21;
Z = bitor(X, Y)
Z = bitxor(X, Y)
Z = bitshift(X,-3)
Z = bitshift(X,4)

2:63

2:59

2:6

2:71

Z=62

2: 59

2:8

2:73

2:69

2: 54

2:7

2:720

2:61

2:56

2:5

2:72

Answer

Correct Answer:

2:61

2:56

2:5

2:72

Note: This Question is unanswered, help us to find answer for this one

124.
In relation to classif‌ication in machine learning, which Of the following Options is the correct way for def‌ining the F1-Score?

F1-Score: (Precision x Recall) / ((2 x Precision) + Recall)

F1-Score: (2 x Precision x Recall) / (Precision + Recall)

F1-Score: (Precision x Recall) / 2(Precision + Recall)

F1-Score: (Precision x Recall) / (Precision 1‘ (2 x Recal|))

Answer

Correct Answer:

F1-Score: (2 x Precision x Recall) / (Precision + Recall)

Note: This Question is unanswered, help us to find answer for this one

125.
A target in machine learning is known as alan:

label

dependent variable.

object

feature

Answer

Correct Answer:

label

Note: This Question is unanswered, help us to find answer for this one

126.
In machine learning, random forests method is an example of which of the following feature selection methods?

Wrapper method

Embedded method

Filter method

Answer

Correct Answer:

Embedded method

Note: This Question is unanswered, help us to find answer for this one

127. An artificial neuron device consists of how many inputs and outputs?

One input and many outputs.

Many inputs and one output

One input and one output.

Many inputs and many outputs.

Answer

Correct Answer: Many inputs and one output

Note: This Question is unanswered, help us to find answer for this one

128.
Which of the following are the applications of data mining?

(i)Science Exploration

(ii)Fraud Detection

(iii)Customer Retention

Only (i)and (ii)

Only (ii)and (iii)

Only (i)and (iii)

All (i), (ii)and (iii)

Answer

Correct Answer:

All (i), (ii)and (iii)

Note: This Question is unanswered, help us to find answer for this one

129.
What will be the output of the following code, when executed in MATLAB?
Z=[8471;7196;6429]
Z(3:3,1:3)

8 4 7 1

7 1 9 6

6 4 2 9

ans=

8 4 7

8 4 7 1

7 1 9 6

6 4 2 9

ans:
7 1 9

8 4 7 1

7 1 9 6

6 4 2 9

ans:
6 4 2

OZ:
8 4 71
7 1 9 6
6 4 2 9
ans=
4 7 1

Answer

Correct Answer:

8 4 7 1

7 1 9 6

6 4 2 9

ans:
6 4 2

Note: This Question is unanswered, help us to find answer for this one

130.
Which of the following machine learning tools works with large data volume and supports text mining and image mining through plugins?

KNIME

Weka

Colab

TensorFIow

Answer

Correct Answer:

KNIME

Note: This Question is unanswered, help us to find answer for this one

131. The machine learning framework RapidMiner is written in which of the following programming languages?

C++

Java

Python

Answer

Correct Answer: Java

Note: This Question is unanswered, help us to find answer for this one

132. Which of the following is incorrect about the feedforward artificial neural network topology?

Feedback loops are not allowed.

Flow of information is unidirectional.

They do not have fixed inputs and outputs.

They are used in pattern generation.

Answer

Correct Answer: They do not have fixed inputs and outputs.

Note: This Question is unanswered, help us to find answer for this one

133. Which of the following is incorrect about the feedback artif‌icial neural network?

They are used in content addressable memories.

Feedback loops are allowed.

Flow of information is bi-directional.

Feedback networks are static.

Answer

Correct Answer: Feedback networks are static.

Note: This Question is unanswered, help us to find answer for this one

134.
Any layer that the user wants to use.
In agent-based simulation, which of the following Inferential Theory of Learning (ITL)operations is used to modify knowledge by narrowing the reference set of a description?

Concretion

Generalization

Specialization

Abstraction

Answer

Correct Answer:

Specialization

Note: This Question is unanswered, help us to find answer for this one

135. In Multilayer Perceptrons, the predictions are made in which of the following layers?

Input layer

First hidden layer

Last hidden layer

Output layer

Answer

Correct Answer: Output layer

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136. In relation to machine learning. the XGBoost works for which of the following options?

Computer vision

Tabular data

NLP

All of the above.

Answer

Correct Answer: Tabular data

Note: This Question is unanswered, help us to find answer for this one

137. Which of the following statements is incorrect about Recurrent Neural Networks (RNNs)?

They are non-deterministic.

They can settle to point attractors.

They can oscillate.

They have at least one feed-back connection.

Answer

Correct Answer: They are non-deterministic.

Note: This Question is unanswered, help us to find answer for this one

138. Which of following is incorrect about modelica?

It is a language

It is a tool.

It is declarative.

It is object-oriented.

Answer

Correct Answer: It is a tool.

Note: This Question is unanswered, help us to find answer for this one

139. Which of the following is incorrect about linear regression?

Independent variables in linear regression can be continuous or discrete.

A dependent variable in linear regression is discrete.

Linear regression is sensitive to outliers.

It estimates the real values that are based on continuous variables.

Answer

Correct Answer: A dependent variable in linear regression is discrete.

Note: This Question is unanswered, help us to find answer for this one

140. In relation to machine learning framework, which of the following languages is used by Veles for performing automation and coordination between the nodes?

C++

Java

Python

Answer

Correct Answer: Python

Note: This Question is unanswered, help us to find answer for this one

141. Which of the following is an advantage in logistic regression?

Natural probabilistic view of class predictions.

Linear decision boundary.

Independent observations requirement.

Overfitting the Model.

Answer

Correct Answer: Natural probabilistic view of class predictions.

Note: This Question is unanswered, help us to find answer for this one

142.
In the knowledge discovery process, which of the following steps is involved in retrieving data, relevant to the analysis task, from the database?

Data selection

Data mining

Data transformation

Knowledge presentation

Answer

Correct Answer:

Data selection

Note: This Question is unanswered, help us to find answer for this one

143.
In relation to K-Nearest Neighbors (K-NN) algorithm, what effect does the small k value (neighborhood size) have on bias and variance?

High Bias, High Variance

Low Bias, Low Variance

High Bias, Low Variance

Low Bias, High Variance

Answer

Correct Answer:

Low Bias, High Variance

Note: This Question is unanswered, help us to find answer for this one

144. Which of the following types Of data analysis models is/are used to conclude continuous valued functions?

Prediction

Classif‌ication

Both a and b

Neither a nor b

Answer

Correct Answer: Prediction

Note: This Question is unanswered, help us to find answer for this one

145. Which of the following machine learning frameworks works at the higher level of abstraction?

Tensorf‌low

PyTorch

Theano

Keras

Answer

Correct Answer: Keras

Note: This Question is unanswered, help us to find answer for this one

146. Which of the following machine learning tools provides API for the neural networks?

Keras.io

Accors.net

Rapid Miner

Shogun

Answer

Correct Answer: Keras.io

Note: This Question is unanswered, help us to find answer for this one

147. Simple linear regression is characterized by how many independent variables?

One

Two

Three

Four

Answer

Correct Answer: One

Note: This Question is unanswered, help us to find answer for this one

148.
Which of the following machine learning tools supports vector machines, dimensionality reduction, and online learning, etc.?

Colab

Shogun

Accors.Net

Weka

Answer

Correct Answer:

Shogun

Note: This Question is unanswered, help us to find answer for this one

149.
Which of the following methods is used to generate non-uniform random variates and uses multiple uniform [0,1] variables?

Convolution

Composition

Inverse Transform

Acceptance-Rejection

Answer

Correct Answer:

Acceptance-Rejection

Note: This Question is unanswered, help us to find answer for this one

150. Which of the following is NOT an example of bounded probability distribution?

Uniform distribution

Triangular distribution

Logistic distribution

Binomial distribution

Answer

Correct Answer: Logistic distribution

Note: This Question is unanswered, help us to find answer for this one

151. A multi-layer perceptron (MLP)is a:

finite cyclic graph.

inf‌inite cyclic graph

inf‌inite acyclic graph

finite acyclic graph.

Answer

Correct Answer: finite acyclic graph.

Note: This Question is unanswered, help us to find answer for this one

152. Which of the following is NOT a multi-class classif‌ier?

Classif‌ication of spam and non-spam emails

Classif‌ication of crop types

Classif‌ication of mood

All of the above are multi-class identif‌iers.

Answer

Correct Answer: Classif‌ication of spam and non-spam emails

Note: This Question is unanswered, help us to find answer for this one

153.
Which of the following tools is used for processing, analyzing and visualizing the large data sets and can provide native support for Apache Spark distributed computing?

Zeppelin

Jupyter

Kibana

Tableau

Answer

Correct Answer:

Zeppelin

Note: This Question is unanswered, help us to find answer for this one

154. Which of the following defines the F-score measure to assess the quality of text retrieval?

F-score = recall x precision / (recall - precision)/ 2

F-score = |[Relevant} f‌l {Retrieved}l / l[Retrieved]l

F-score = |{Relevant} fl [Retrieved}l / l[Relevant]|

F-score = recall x precision / (recall + precision)/ 2

Answer

Correct Answer: F-score = recall x precision / (recall + precision)/ 2

Note: This Question is unanswered, help us to find answer for this one

155.
In the SVM classif‌ication approach, which of the following options is used for solving the issue of the low-sparse SVM classifier?

Risk area SVM

Fuzzy SVMs

Cluster Support Vector Machine

Prototype selection

Answer

Correct Answer:

Cluster Support Vector Machine

Note: This Question is unanswered, help us to find answer for this one

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