Q1. 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✔️
K-means cluster diagram
Q2. 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.✔️
Q3. You work for a music streaming service and want to use supervised machine learning to classify music into different genres. Your service has collected thousands of songs in each genre, and you used this as your training data. Now you pull out a small random subset of all the songs in your service. What is this subset called?
Q4. In traditional computer programming, you input commands. What do you input with machine learning?
Q5. 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.
Q6. 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.
Q7. 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?
Q8. ____ looks at the relationship between predictors and your outcome.
Q9. 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✔️
Q10. What does this image illustrate?
a decision tree✔️
a clear trendline
Q11. 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.
Q12. To predict a quantity value. use ___.
Q13. 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.
Q14. You work for an ice cream shop and created the chart below, which shows the relationship between the outside temperature and ice cream sales. What is the best description of this chart?
It is a linear regression chart.✔️
It is a supervised trendline chart.
It is a decision tree.
It is a clustering trend chart.
Q15. 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.
Q16. 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.
Q17. 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.
Q18. What is the missing information in this diagram?
Q19. 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.✔️
Q20. Your university wants to use machine learning algorithms to help sort through incoming student applications. An administrator asks if the admissions decisions might be biased against any particular group, such as women. What would be the best answer?
Machine learning algorithms are based on math and statistics, and so by definition will be unbiased.
There is no way to identify bias in the data.
Machine learning algorithms are powerful enough to eliminate bias from the data.
All human-created data is biased, and data scientists need to account for that.✔️
Q21. 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.
Q22. You want to create a supervised machine learning system that identifies pictures of kittens on social media. To do this, you have collected more than 100,000 images of kittens. What is this collection of images called?
Q23. 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?
Q24. 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.
Q25. 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✔️
supervised machine learning
Q26. What is this diagram a good example of?
a decision tree
a linear regression
a K-means cluster✔️
Q27. Random forest is modified and improved version of which earlier technique?
Q28. Self-organizing maps are specialized neural network for which type of machine learning?
Q29. 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.
Q30. You created machine learning system that interacts with its environment and responds to errors and rewards. What type of machine learning system is it?
Q31. 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?
deep neural network
Q32. 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.✔️
Q33. Your machine learning system is attempting to describe a hidden structure from unlabeled data. How would you describe this machine learning method?
Q34. 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.
Q35. 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
Q36. 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.
Q37. Which choice is best for binary classification?
Principal Component Analysis (PCA)
Q38. With traditional programming, the programmer typically inputs commands. With machine learning, the programmer inputs
Q39. 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.✔️
Q40. 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
Q41. 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
Q42. 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.
Q43. 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.
Q44. 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.
Q45. 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.✔️
Q46. Many of the advances in machine learning have come from improved ___.
Q47. What is this diagram a good example of?
Q48. The supervisor asks to create a machine learning system that will help your hr dep. classify job applicants into well-defined groups.What type of system are more likely to recommend?
deep learning artificial neural network that relies on petabytes of data
unsupervised machine learning system that clusters together the best candidates
Not recommend machine learning for this project
supervised machine learning system that classifies applicants into existing groups // we do not need to classify best candidates we just need to classify job applicants in to existing categories✔️
Q49. 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?
high -variance modeling
Q50. 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
big data test set
Q51. 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 fotos 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
Q52. Your supervisor asks you to create a machine learning system that will help your human resources department classify jobs applicants into well-defined groups. What type of system are you more likely to recommend?
an unsupervised machine learning system that clusters together the best candidates.
you would not recommend a machine learning system for this type of project.
a deep learning artificial neural network that relies on petabytes of employment data.
a supervised machine learning system that classifies applicants into existing groups.✔️
Q53. 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.
Q54. 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
Q55. The model will be trained with data in one single batch is known as ?
Both A and B✔️
None of the above
Q56. Which of the following is NOT supervised learning?
Q57. 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?
Q58. 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.
Q59. Compared to the variance of the Maximum Likelihood Estimate (MLE), the variance of the Maximum A Posteriori (MAP) estimate is ___
it could be any of the above
Q60. ___ refers to a model that can neither model the training data nor generalize to new data.
all of the above
Q61. How would you describe this type of classification challenge?
This is a multiclass classification challenge.✔️
This is a multi-binary classification challenge.
This is a binary classification challenge.
This is a reinforcement classification challenge.
Q62. 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.
Q63. 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.✔️
Q64. You work for a website that helps match people up for lunch dates. The website boasts that it uses more than 500 predictors to find customers the perfect date, but many costumers complain that they get very few matches. What is a likely problem with your model?
Your training set is too large.
You are underfitting the model to the data.
You are overfitting the model to the data.✔️
Your machine is creating inaccurate clusters.
Q65. (Mostly) whenever we see kernel visualizations online (or some other reference) we are actually seeing:
What kernels extract✔️
How kernels Look
Q66. The activations for class A, B and C before softmax were 10,8 and 3. The different in softmax values for class A and class B would be :
Q67. 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
Q68. Which of the following methods can use either as an unsupervised learning or as a dimensionality reduction technique?
Q69. What is the main motivation for using activation functions in ANN?
Capturing complex non-linear patterns✔️
Transforming continuous values into “ON” (1) or “OFF” (0) values
Help avoiding the vanishing/exploding gradient problem
Their ability to activate each neurons individually.
Q70. Which loss function would fit best in a categorical (discrete) supervised learning ?
kullback-leibler (KL) loss
Mean Squared Error (MSE)
Any L2 loss
Q71. What is the correct option?
Q72. You create a decision tree to show whether someone decides to go to the beach. There are three factors in this decision: rainy, overcast, and sunny. What are these three factors called?
Q73. 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
Q74. The fit line and data in the figure exhibits which pattern?
low bias, high variance
high bias, low variance
high bias, high variance
low bias, low variance✔️
Q75. Many of the advances in machine learning have come from improved?
Q76. You need to select a machine learning process to run a distributed neural network on a mobile application. Which would you choose?
Q77. Which choice is the best example of labeled data?
20,000 recorded voicemail messages
100,000 images of automobiles
hundreds of gigabytes of audio files
Q78. In statistics, what is defined as the probability of a hypothesis test of finding an effect – if there is an effect to be found?
Q79. You want to create a machine learning algorithm to identify food recipes on the web. To do this, you create an algorithm that looks at different conditional probabilities. So if the post includes the word flour, it has a slightly stronger probability of being a recipe. If it contains both flour and sugar, it even more likely a recipe. What type of algorithm are you using?
naive Bayes classifier✔️
Q80. 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
Q81. What is Q-learning reinforcement learning?
supervised machine learning with rewards
a type of unsupervised learning that relies heavily on a well-established model
a type of reinforcement learning where accuracy degrades over time
a type of reinforcement learning that focuses on rewards✔️
Q82. 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 predeicted outcome.✔️
Q83. Your machine learning system is using labeled examples to try to predict future data, compare that data to the predicted result, and then the model. What is the best description of this machine learning method?
Q84. 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?
Q85. What is one of the most effective way to correct for underfitting your model to the data?
Create training clusters
Use reinforcement learning
Add more predictors✔️
Q86. Your data science team is often criticized for creating reports that are boring or too obvious. What could you do to help improve the team?
Suggest that the team is probably underfitting the model to the data.✔️
Suggest that unsupervised learning will lead to more interesting results.
Make sure that they are picking the correct machine learning algorithms.
Encourage the team to ask more interesting questions.
Q87. 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.
Q88. 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.✔️
Q89. 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
the multiclass training set challenge
Q90. 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
Q91. K-means clustering is what type of machine learning algorithm?
Q92. 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✔️
Q93. 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.
Q94. Which project might be best suited for supervised machine learning?
predicting a risk score✔️
tax filing software
Q95. 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 “Q” in Q-learning reinforcement learning
Q96. An organisation that owns dozens of shopping malls wants to create a machine learning product that will use facial recognition to identify customers. What is the main challenge of developing such a model?
most machine learning models are not designed for video
it might be unethical for the business to identify people without their consent✔️
it will be difficult to decide between supervised and unsupervised learning
the image in the video would not be high quality enough to identify individuals
Q97. Which of the following machine learning algorithms is unsupervised?
Q98. Averaging the output of multiple decision trees helps to::
Q99. To optimize your objective function, you are performing full batch gradient descent using the entire training set (not stochastic gradient descent). Is it required to shuffle your training set?
Yes. If you don’t, the optimization will oscillate around the minimum at the end of training.
Yes, in order to help the model generalize to the test dataset.
No, it is not necessary because the dataset can already be considered shuffled from the data collection process.
No, because each update passes through the entire dataset anyway and the order doesn’t matter.✔️
Q100. You’ve received 1,000,000 images and have split it in 96%/2%/2% between train, dev and test sets. You’ve trained your model, and analyzed the results. After working further on the problem, you’ve decided to correct the incorrectly labeled data on the dev set.
Which of these statements do you agree with?
You should also correct the incorrectly labeled data in the test set, so that the dev and test sets still come from the same distribution.✔️
You should correct incorrectly labeled data in the training set as well so as to avoid your training set now being even more different from your dev set.
You should not correct the incorrectly labeled data in the test set, because the test set should reflect the data distribution of the real world.
If you want to correct incorrectly labeled data, you should do it on all three sets (train/dev/test) in order to maintain similar distributions.
Q101. You’re working on a binary classification task, to classify if an image contains a cat (“1”) or doesn’t contain a cat (“0”). What loss would you choose to minimize in order to train a model?
L = y log y^ + (1−y) log (1− y^)
L = – y log y^ – (1−y) log (1− y^)✔️
L = || y – y^ ||22
L = || y – y^ ||22 + constant
Q102. You want to create a machine learning algorithm that finds the top 100 people who have shared photographs of themselves on social media. What is the best machine learning method to use?
Q103. The famous data scientist Andrew Ng has been quoted as saying, “Applied machine learning is basically feature engineering.” What is feature engineering?
scraping new features from web data
creating new variables by combining and modifying the original variables✔️
designing innovative new user features to add to software
using deep learning to find features in the data
Q104. In the context of calculus, what is df/dx?
the prediction function
the derivative of f of x✔️
the derivative of x
equivalent to f divided by x
Q105. What is a well-designed/well-fitted model?
one that has been trained and tested with the same data
one that has a high degree of accuracy and is able to accurately predict results✔️
one that has been trained with labeled training data
one that has been trained with an exhaustive set of all conditions and permutations in the training data
Q106. __ -based collaborative filtering occurs when a person is recommended an item similar to an item they have purchased.
Q107. Fill in the blanks: Two multivariate imputer techniques are the __ imputer and the __ imputer.
Q108. You are working on a regression model using the Keras library. What method on the Model class do you use to train the model?
Q109. What is the goal of regularization in the K nearest neighbors algorithm?
normalizing the data points so they can be compared with each other
using a straight line model to make predictions based on training data
finding the slope of the line that represents the model
making the decision boundaries more regular✔️
Q110. If there is no trend between two variables x and y, we say that there is a __ connection between x and y.
Q111. Naive Bayes looks at each __ predictor and creates a probability that belongs in each class.
Q112. 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.
Q113. Your machine learning system is using labeled examples to try to predict future data, compare that data to the predicted result, and then the model. What is the best description of this machine learning method?
Q114. 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?
Q115. You are working with your machine learning algorithm on something called class predictor probability. What algorithm are you most likely using?
multiclass binary classification
decision tree analysis
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