Machine Learning Test

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

PIVOT TABLE

LINEAR REGRESSION AND SCATTER PLOTS PIVOT TABLE

K-MEANS CLUSTER DIAGRAM

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.

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?

DATA CLUSTER

SUPERVISED SET

BIG DATA

TEST DATA

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

PATTERNS

PROGRAMS

RULES

DATA

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.

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.

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

____looks at the relationship between predictors and your outcome.

REGRESSION ANALYSIS

K-MEANS CLUSTERING

BIG DATA

UNSUPERVISED LEARNING

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

You for 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.

To predict a quantity value. use ____.

REGRESSION

CLUSTERING

CLASSIFICATION

DIMENSIONALITY REDUCTION

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.

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.

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.

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.

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.

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.

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.

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?

TRAINING DATA

LINEAR REGRESSION

BIG DATA

TEST DATA

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

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.

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

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

AGGREGATED TREES

BOOSTED TREES

BAGGED TREES

STACKED TREES

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

SEMI-SUPERVISED LEARNING

SUPERVISED LEARNING

REINFORCEMENT LEARNING

UNSUPERVISED LEARNING

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.

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

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

Your data science team wants to use the K-nearest neighbor classification algorithm. Someone on your team wants w 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.

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

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.

. 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

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.

Which choice is best for binary classification?

K-MEANS

LOGISTIC REGRESSION

LINEAR REGRESSION

PRINCIPAL COMPONENT ANALYSIS (PCA)

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

SUPERVISED LEARNING

DATA

UNSUPERVISED LEARNING

ALGORITHMS

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.

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

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

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.

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Exam Type:	Machine Learning MCQ Skill Test
Questions Type:	Multiple Choice Questions
Total Questions:	38
Time Limit:	30 Minutes
Last Update	June, 2025

Pass Machine Learning Exam

Machine Learning Quiz

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

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?

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?

____looks at the relationship between predictors and your outcome.

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

To predict a quantity value. use ____.

Why is naive Bayes called naive?

How is machine learning related to artificial intelligence?

How do machine learning algorithms make more precise predictions?

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

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

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?

What is stacking?

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?

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?

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?

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?

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

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

Which statement about K-means clustering is true?

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

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?

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

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

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

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

Which choice is best for binary classification?

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

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

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

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?

Machine Learning Test Explained