Organizer: Microsoft and Linkedin
About the Course
- Microsoft and LinkedIn launched a free generative Artificial Intelligence learning path through which we can learn the core concepts of artificial intelligence 6 generative AI functionality and get a professional verified Microsoft and LinkedIn Certification.
- You can Apply who anyone interested in learning about how Career Essentials in Generative AI works in professional life can Apply in this course and you will be eligible for a professional verified certification by Microsoft and LinkedIn and if you can complete the program successfully.
- Certification: This learning path is eligible for a Professional Certificate from Microsoft and LinkedIn
Course Items:
- 1 Course: What Is Generative AI ?
- 2 Course: Generative AI: The Evolution of Thoughtful Online Search
- 3 Course: Streamlining Your Work with Microsoft Copilot
- 4 Course: Learning Microsoft 365 Copilot And Business Chat
- 5 Course: Ethics in the Age of Generative AI
- 6 Course: Introduction to Artificial Intelligence (2023)
Discover the skills needed to apply Generative AI in your career. Learn the core concepts of artificial intelligence and generative AI functionality.
- Develop an understanding of generative AI models.
- Learn the ethical considerations of using generative AI.
- Explore the impact of generative AI tools.
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Online Career Essentials in Generative AI by Microsoft and LinkedIn Quiz Exam Answers Available:
What is Generative AI?
Question 1 of 4
What is a key benefit of using generative AI for repetitive or computational tasks?
It reduces the risk of errors and biases.
It increases the flexibility and adaptability of the inputs.
It improves the quality and efficiency of the outputs.
It allows humans to focus on more creative and strategic activities.
Ans. It allows humans to focus on more creative and strategic activities.
Question 2 of 4
What is the difference between generative AI and other types of AI that generate content?
Generative AI’s primary function is to create content.
Generative AI uses unsupervised learning to generate content without preexisting data.
Generative AI is a subset of reactive machines that generate content in response to stimuli.
Generative AI can generate content in any domain or format.
Ans. Generative AI’s primary function is to create content.
Question 3 of 4
What does the term open source mean in the context of generative AI models?
It means that the models are transparent and explainable to human users.
It means that the models are publicly available for anyone to use and modify.
It means that the models are generated by crowdsourcing data from online users.
It means that the models are funded by the public sector and non-profit organizations.
Ans.It means that the models are publicly available for anyone to use and modify.
Question 4 of
What is an AI model?
A model is a set of algorithms that have been trained on a specific dataset.
A model is a program that detects certain patterns using a collection of datasets.
A model is a tool for writing and running code.
A model is an example of how a generative AI application can be used.
Ans. A model is a set of algorithms that have been trained on a specific dataset.
Main Model-2
Question 1 of 5
What is one real-world application of Variational Autoencoders (VAE) in anomaly detection?
Generating synthetic fraudulent transactions to train financial fraud detection models.
VAE does not generate fraudulent transactions, but it does detect fraud so financial risks are minimized.
Detecting defects in industrial quality control by identifying images of products that deviate from a dataset of normal products.
Identifying patterns in social media activity to predict consumer behavior.
Creating realistic audio samples for use in music production.
Ans. Detecting defects in industrial quality control by identifying images of products that deviate from a dataset of normal products.
Question 2 of 5
What is GPT and why has it become notable in the field of natural language processing?
GPT is a language model developed by Microsoft that suggests code and entire functions in real-time to its users.
GPT is a language model developed by OpenAI that can take in a prompt and generate text based on it, making it useful for a multitude of tasks.
GPT is a language model developed by OpenAI that uses a transformer architecture to generate human-like texts.
GPT is a language model developed by Google that uses a pre-training technique to improve its performance on task-specific datasets.
Ans. GPT is a language model developed by OpenAI that can take in a prompt and generate text based on it, making it useful for a multitude of tasks.
Question 3 of 5
What are Midjourney, DALL-E, and Stable Diffusion, and which industries are their early adopters?
They are primary text-to-image generation services and models. Art, filmmaking, fashion, and marketing are the first industries to widely adopt their use.
They are large language models models. Chatbots, search engines, and customer service are the primary industry adopters.
They are 3D asset generation companies. Their generated outcomes are used to design clothes, objects, CGI VFX, and are helping filmmakers quickly generate 3D environments.
Ans. They are primary text-to-image generation services and models. Art, filmmaking, fashion, and marketing are the first industries to widely adopt their use
Question 4 of 5
How does a GAN network improve its ability to generate better content?
The user writes text to generate content and the network learns to improve itself each time it is being used.
The generator and discriminator parts of the network work together in a competition to improve the generator’s ability to create realistic data.
The generator and discriminator parts of the network work together in harmony to challenge and trick the user in identifying which outcomes are “real” and which are “synthetic”.
Ans. The generator and discriminator parts of the network work together in a competition to improve the generator’s ability to create realistic data.
Question 5 of 5
What is the purpose of the discriminator in a GAN model?
to create realistic data for training models
to evaluate the data created by the generator and give feedback on how to improve the next iteration.
to input one type of data and output the same type of data
to generate synthetic versions of fraudulent transactions for financial fraud detection.
Ans. to evaluate the data created by the generator and give feedback on how to improve the next iteration
The Future of AI-3
Question 1 of 1
What will be the main benefit of generative AI in the next years?
automate repetitive tasks and liberate humanity from dull, dirty, difficult, or dangerous jobs
optimize supply chains for corporations to save excess spending
increase security through Blockchain and distributed ledger technology
Ans. automate repetitive tasks and liberate humanity from dull, dirty, difficult, or dangerous jobs
Ethics and Responsibility-4
Question 1 of 2
What are the top moral and executive skill sets required when working with generative AI?
Transparency, fairness, empathy and responsibility. Approach production and operations with caution, always asking, “Who is benefiting?” from our generative AI solution.
A rich technical background, especially in machine learning and natural language processing, coupled with excellent leadership skills.
Lobbying and diplomacy
Ans. Transparency, fairness, empathy and responsibility. Approach production and operations with caution, always asking, “Who is benefiting?” from our generative AI solution.
Question 2 of 2
When considering the integration of generative AI tools in business operations, what is the primary emphasis regarding the role of executive leadership and organizational strategy?
advocating for unrestricted reliance on generative AI outcomes to streamline decision-making processes
prioritizing human-centered approaches, ethical considerations, and maintaining human control over AI-generated content
establishing rigid guidelines that prioritize algorithm-generated content over human decision-making
encouraging blind reliance on AI-generated content without considering ethical implications or human oversight
Ans. prioritizing human-centered approaches, ethical considerations, and maintaining human control over AI-generated content
Course- Generative AI: The Evolution of Thoughtful Online Search
Search Engines vs. Reasoning Engines-1
Question 1 of 10
What is the goal of the continuous crawling process of a search engine?
to discover new or updated webpages
to keep the search engine’s index up-to-date
to store and organize webpage information
Ans. to keep the search engine’s index up-to-date
Question 2 of 10
When a user enters a query, what does the reasoning engine strive to provide?
a relevant, informative text response using human-like speech
a brief bulleted list summarizing the main points of the query
a ranked list of webpage results
Ans.a relevant, informative text response using human-like speech
Question 3 of 10
When might a search engine be a superior tool to a reasoning engine?
when you’d like to read further about a subject across a collection of different sources—but not necessarily when you want to ask deeper questions
when you want to have an intelligent conversation with an AI chatbot
when you want a customized, well-reasoned answer generated with human-like speech
Ans. when you’d like to read further about a subject across a collection of different sources—but not necessarily when you want to ask deeper questions
Question 4 of 10
Which is not a main function of a search engine?
transforming
crawling
indexing
ranking
Ans. transforming
Question 5 of 10
What is the most important benefit that the synergy between modern search engines and reasoning engines provides, as far as confidence in the results?
avoiding time wasted on unrelated results
being able to use both engines simultaneously on a single platform
verifying and validating search results
Ans. verifying and validating search results
Question 6 of 10
How can a user best combine a search engine and a reasoning engine to find information about an unknown topic?
Use the reasoning engine to get a summary of the topic, and then use the search engine for more detailed information.
Use one engine to find general information, and then use the other engine to find disputing information on that topic.
Use the search engine to find basic information, and then use the reasoning engine for a deeper dive.
Ans. Use the search engine to find basic information, and then use the reasoning engine for a deeper dive.
Question 7 of 10
How does a reasoning engine’s ability to understand and interpret language provide the greatest advantage over a search engine?
It can provide a collection of additional ranked results on the topic requested.
It can have an actual conversation with the user.
It can mediate conflicting information between different machine learning models.
Ans. It can have an actual conversation with the user.
Question 8 of 10
How are reasoning engines an improvement over search engines when it comes to entering what you are looking for?
They can understand your intent and not just the words you used.
They can provide a direct, ranked answer to your request.
They can suggest alternative search queries to use if they sense yours needs improvement
Ans. They can understand your intent and not just the words you used.
Question 9 of 10
How do human supervisors assist in training a reasoning engine?
Human supervisors perform monthly audits on reasoning engine responses to ensure the AI is doing a suitable job.
In early training phases, human supervisors oversee the process, guiding the model towards accurate responses and contributing to its knowledge development.
Human supervisors monitor every user query to make sure the reasoning engine creates accurate responses.
Ans. In early training phases, human supervisors oversee the process, guiding the model towards accurate responses and contributing to its knowledge development
Question 10 of 10
True or False: Reasoning engines are an all-knowing source of truth and should be trusted implicitly.
FALSE
TRUE
Ans. FALSE
Thoughtful Search Strategies and Considerations in Reasoning Engines-2
Question 1 of 9
When a user enters a query, what does the reasoning engine strive to provide?
a relevant, informative text response using human-like speech
a brief bulleted list summarizing the main points of the query
a ranked list of webpage results
Ans. a relevant, informative text response using human-like speech
Question 2 of 9
When might a search engine be a superior tool to a reasoning engine?
when you’d like to read further about a subject across a collection of different sources—but not necessarily when you want to ask deeper questions
when you want to have an intelligent conversation with an AI chatbot
when you want a customized, well-reasoned answer generated with human-like speech
Ans. when you’d like to read further about a subject across a collection of different sources—but not necessarily when you want to ask deeper questions
Question 3 of 9
If your reasoning engine response is problematic (i.e., inaccurate, discriminatory, limited in view, etc.) what should you do?
Report the problematic response to the FC
Give up and seek other research methods; the reasoning engine is unlikely to produce a valid result.
Continue iterating. Keep regenerating and refining the prompt to get a more accurate, better result.
Ans.Continue iterating. Keep regenerating and refining the prompt to get a more accurate, better result.
Question 4 of 9
In prompt engineering, what is one-shot or few-shot learning?
It refers to the style of answer you want to target in your response.
It refers to how many chances you give the reasoning engine to get the right answer.
It refers to how much instruction you provide in order to guide the answer. This may involve including examples of what a “correct” answer may look like.
Ans. It refers to how much instruction you provide in order to guide the answer. This may involve including examples of what a “correct” answer may look like.
Question 5 of 9
In most instances, how should you craft your prompts?
Use clear language with proper grammar.
Use conversational and informal language.
Use jargon common to the industry.
Ans. Use clear language with proper grammar.
Question 6 of 9
Why is the iteration process necessary when you use a reasoning engine?
You want to use a combination of both keywords and regular language prompts.
You want to keep honing your prompt to get more and better results.
You want to avoid creating multiple prompts when one good prompt will suffice.
Ans. You want to keep honing your prompt to get more and better results.
Question 7 of 9
Riva considers herself a prompt engineer. What does this mean?
She can use coding to create better prompts than the prompts she creates with regular language.
She can create a prompt with samples of her question and the answers that she would like to see.
She has created a number of prompts to the point where she is an expert on crafting prompts.
Ans. She can create a prompt with samples of her question and the answers that she would like to see.
Question 8 of 9
What is the following creative type of prompt known as: “Imagine you’re the manager of a small botique video editing company. What are 10 innovative marketing ideas that could attract new business?”
Debate-style
Role play
Analogy
Ans. Role play
Question 9 of 9
True or False: Reasoning engines are an all-knowing source of truth and should be trusted implicitly.
FALSE
TRUE
Ans. Flase
Learning Microsoft 365 Copilot and Business Chat Course
Explore Microsoft 365’s AI assistant-1
Question 1 of 1
Who is able to purchase a Microsoft 365 Copilot license?
people who use Microsoft Teams
any user with an existing Microsoft 365 subscription
US residents only
the Microsoft 365 Administrator at a company, school, government agency, or similar organization
Ans. the Microsoft 365 Administrator at a company, school, government agency, or similar organization
Using Copilot in Office Applications-2
Question 1 of 4
Copilot is only available in Excel if _.
you are working with Excel on Window
you are working with Excel on the web
the file you are working on is stored on OneDrive, SharePoint, or Teams
Ans. the file you are working on is stored on OneDrive, SharePoint, or Teams
Question 2 of 4
Which statement about Copilot in PowerPoint is true?
It can draft new presentations or add content to existing presentations.
It can only modify existing presentation. It cannot be used to create new presentations.
It can only draft new presentations. It cannot be used to modify existing presentations.
Ans. It can draft new presentations or add content to existing presentations.
Question 3 of 4
What is a common reason the summary option may not be available in Outlook?
You are using Outlook on a Mac.
You are using Outlook on the web.
The message you have selected is very short and does not have enough information to summarize
Ans. The message you have selected is very short and does not have enough information to summarize.
Question 4 of 4
If you join a Teams meeting that’s already in progress, you can ask Copilot to summarize the topics that were discussed that you may have missed. This will only work if _.
each person in the meeting gives their permission
there are five or more people in the meeting
somebody in the meeting starts the transcript feature
Ans. somebody in the meeting starts the transcript feature
Ethics in the Age of Generative AI Course
Distinguishing responsible tech from human behavior-1
Question 1 of 4
Damien wants to know who the target population is for the AI tool and what their main goals are. Which pillar in the AI framework is he addressing?
boundaries on safe and appropriate us
robust transparency
responsible data practice
ans. boundaries on safe and appropriate use
Ans. boundaries on safe and appropriate use
Question 2 of 4
Cuong is auditing the company’s new AI tool. He determines that customers are using it beyond what it was intended for. What should be his first step when handling this issue?
Set boundaries of use.
Take it offline.
Change the data set.
ans.Take it offline.
Question 3 of 4
What tools can be used to create a persona to deliver fraudulent information?
copyrights
chatbots
deep fakes
ans. deep fakes
Question 4 of 4
Which pillar of the ethical AI framework is the starting point for all ethical AI tools?
robust transparency
responsible data practice
boundaries on safe and appropriate use
ans. responsible data practice
Preparing technology teams to make ethical decisions-2
Question 1 of 6
Which responsibility falls into the realm of the board?
Identify specific metrics to manage the company’s AI.
Ensure the company has resources to manage AI ethical risks.
Create an AI policy and governance framework.
ans. Ensure the company has resources to manage AI ethical risks.
Question 2 of 6
In which of the three goals for an effective and ethical data organization would a training curriculum on data responsibility be promoted?
promoting transparency
prioritizing privacy
reducing bias
ans.prioritizing privacy
Question 3 of 6
As a chief AI officer, Natasha is reviewing with one of the teams the purpose of the product and possible risks to users. Where does this task fit into the LISA framework for listening to customers?
Sharing
Auditing
Listening
Involving
ans. Auditing
Question 4 of 6
Lately, a technical team has been facing several challenges in the use of technology at the company. Which action should the company and the team take first?
Encourage everyone to ask questions and raise concerns.
Obtain training on ethical use challenges with emerging technology.
Discuss ethical dilemmas and potential remediations with new initiatives.
ans. Encourage everyone to ask questions and raise concerns.
Question 5 of 6
In using the acronym ETHICS to adhere to responsibilities for yourself and those around you, which letter represents the group responsible for feedback and insights?
I
T
H
C
ans. C
Question 6 of 6
A large company has been trying to implement AI. The C-suite established an AI policy and governance framework. What should the company do to sustain its AI efforts?
Set up a training program for technical teams, including bringing a new CTO into the C-suite.
Establish accountability for AI ethics implementation in a C-Suite role, which might be a chief AI ethics officer.
Conduct a bias audit and hire a new HR manager.
ans. Establish accountability for AI ethics implementation in a C-Suite role, which might be a chief AI ethics officer.
Introduction to Artificial Intelligence (2023) Course
What is Artificial Intelligence ?
Question 1 of 3
Why did some of the earliest artificial intelligence systems focus on board games such as checkers and chess?
It’s easiest to make a computer system seem intelligent when it’s working with set rules and patterns.
Because early computer scientists didn’t want the system to seem to sound too intelligent.
Board games gave computer systems access to huge amounts of data which allowed the machine to learn new things.
Board games were an easy way to have computers create neural pathways.
ans. It’s easiest to make a computer system seem intelligent when it’s working with set rules and patterns.
Question 2 of 3
You’re a product manager who’s in charge of building a weak AI expert system that will give tax advice. You’re working with dozens of accountants who go through thousands of different taxpayer scenarios. When a customer asks a question, then the expert system will ask a follow-up question. It will do this until it makes a recommendation. What’s one of the biggest challenges with this system?
The system could evolve into strong AI and develop a personality.
Many people will be uncomfortable trusting a computer system with their taxes.
There will be too many tax combinations for the experts to cover with one system.
There aren’t enough tax experts to help develop scenarios for the system.
ans. There will be too many tax combinations for the experts to cover with one system.
Question 3 of 3
Luella seeks medical attention for chest pains. A nurse uses an artificial intelligence program to diagnose the cause. Why is this system likely not really intelligent?
The program can only be intelligent if the patient provides a complete medical history.
The program only matches her symptoms to steps in a system an expert created.
The program is only intelligent if a patient has been there before.
ans. The program only matches her symptoms to steps in a system an expert created.
The Rise of Machine Learning-2
Question 1 of 1
How does an artificial neural network learn?
A computer scientist programs each neuron to have the correct answer to any question.
Only correct answers go into the input layer, so it learns what’s correct from the output layer.
The hidden layers hide the incorrect answers from the rest of the network.
It looks at the data and makes guesses, then it compares those guesses to the correct answer.
ans. It looks at the data and makes guesses, then it compares those guesses to the correct answer.
Common AI System-3
Question 1 of 3
The healthcare and medical insurance industries caution against using machine learning to search for patterns in data, and they do not want machines making decisions about a person’s health. Why?
They may be decisions that humans cannot understand.
They may be decisions that humans are unable to make.
They may be decisions that will supplant office visits
ans. They may be decisions that humans cannot understand.
Question 2 of 3
What type of impact does artificial intelligence have on robotics?
AI systems can create robots that can more easily learn new tasks.
AI systems will help robots do very precise work.
Newer robots won’t be able to do anything without artificial intelligence.
AI systems can help robots do simple repetitive work
ans. AI systems can create robots that can more easily learn new tasks.
Question 3 of 3
What impact will the Internet of Things (IoT) have on artificial intelligence?
IoT devices will keep artificial intelligence agents from becoming strong AI.
These devices can act as experts to help program expert systems.
These devices will be a great new source of “real world” data.
IoT devices can form neurons to help create artificial neural networks.
ans. These devices will be a great new source of “real world” data.
Learn from Data-4
Question 1 of 2
A new online camping goods store wants to find connections between products customers buy and other products they might buy. Why would the company use unsupervised learning?
Supervised learning is unable to identify connections between unrelated products.
Connections can be found with any input required by the user.
It does not yet have enough customers to make supervised learning meaningful.
ans. It does not yet have enough customers to make supervised learning meaningful.
Question 2 of 2
You’re a preschool worker and you want to teach your class the letters in the alphabet. So you draw the letter “B” on the board. Then you ask the two-year-old students to find a block with that same letter. Some of the students correctly find the blocks with the letter “B”, but some of the students confuse the letter “B” with the letter “D.” So the incorrect students compare their block to the letter “B” on the board, recognize the error and then decide to get another block. What type of learning is this?
machine learning
reinforcement learning
supervised learning
unsupervised learning
ans. supervised learning
Identify Patterns-5
Question 1 of 2
Why might you want to use reinforcement learning instead of unsupervised learning?
Reinforcement learning doesn’t require training and test data in the same way as unsupervised learning.
Reinforcement learning allows the machine to make predictions and create strategies instead of just clustering the data.
Reinforcement learning is a great way to cluster data based on items that are frequently bought together.
Reinforcement learning allows the machine to create binary classifications based on labeled data.
ans. Reinforcement learning allows the machine to make predictions and create strategies instead of just clustering the data.
Question 2 of 2
What is one of the greatest challenges with supervised learning binary classification?
You need a lot of pre-classified or labeled data for the training set.
These systems are complex and inherently unreliable.
You have to come up with multiple classifications.
You have to let the machine come up with its own classification labels.
ans. You need a lot of pre-classified or labeled data for the training set.
Machine Learning Algorithms-6
Question 1 of 2
You work for a company that’s selling electric cars to consumers. The company wants to get the maximum amount of value from its advertising dollars. So it wants to ramp up advertising when it thinks that customers would be most interested in purchasing an electric car. Your data science team wants to create a regression analysis based on fuel prices. How might this look on an XY diagram?
Put electric car sales on both the X and Y axis.
You don’t need to put anything on the Y axis; just put fuel prices on the X axis and the trendline.
Create a trendline with fuel prices along the X axis and electric car sales on the Y axis.
Put fuel prices in dollars on the X axis and fuel prices in rupees on the Y axis.
ans. Create a trendline with fuel prices along the X axis and electric car sales on the Y axis.
Question 2 of 2
How is K Nearest Neighbor like the old saying, “birds of a feather flock together?”
Multiclass classification is like a flock of birds that needs to be classified.
Classify unknown data against the closest data that you do know.
You want to fly through the data as quickly as possible.
Make sure you know everything about the data before you try to classify.
ans. Classify unknown data against the closest data that you do know.
Fit the Algorithms
Question 1 of 3
What is ensemble modeling?
This is when you use a mix of different machine learning algorithms or data to improve the outcome.
This is when you use machine learning to perform music composition.
This is when you mix the training data with the test data to improve the machine learning algorithm.
This is when you use supervised and unsupervised machine learning together to make better predictions.
ans. This is when you use supervised and unsupervised machine learning together to make better predictions.
Question 2 of 3
You work for a credit card company that’s trying to do a better job identifying fraudulent transactions. So your team uses unsupervised learning to create clusters of transactions that are likely to be fraudulent. The machine identified that when customers are buying electronics it’s much more likely to be a fraudulent transaction. So you use this model for your new fraud detection system. Then customers started to complain that they couldn’t use their credit cards to purchase any electronics. What is the challenge with your model?
You used too much data to train the algorithm how to make predictions.
You used unsupervised learning when you should have used supervised learning.
You overfit the model to the data, the added complexity made it difficult to manage the system.
You underfit the model to the data, the simple rule made too many inaccurate predictions.
ans. You underfit the model to the data, the simple rule made too many inaccurate predictions.
Question 3 of 3
How does the bias-variance trade-off affect machine learning?
If the machine makes a change to one, it must consider how the other is affected.
The machine will adjust both until there is low bias and low variance.
The machine will get either bias or variance low, which will then bring the other to low.
ans. If the machine makes a change to one, it must consider how the other is affected.
Artificial Neural Networks-8
Question 1 of 2
Kira is building a neural network to identify customer returns using binary classifications of defective or unsatisfied. In which layer of this neural network will Kira have a probability score?
the hidden layers
the input layer
the output laye
ans. the output laye
Question 2 of 2
You work for a security firm that wants to use an artificial neural network to create a video facial recognition system. So you create a training set with hundreds of images of people that are found in your video footage. You initialize the artificial neural network with random weights assigned to all its connections. When you feed through the first few images the system does a terrible job identifying whether those people are included in the video. What would the artificial neural network now do to try and improve?
It will reinitialize and add random weights to all the connections.
It will adjust the weights of the connections to see if it does a better job making a prediction.
It will add weight to the data to do a better job identifying the image in the network.
It will add more layers to the output layer to see if it does a better job making a prediction.
ans. It will adjust the weights of the connections to see if it does a better job making a prediction.
Improve Accuracy-9
Question 1 of 2
With an artificial neural network what is the point of having a cost function?
It helps the network determine the cost of the error so they can make larger or smaller adjustments to its guesses.
It helps the network determine whether there should be many more hidden layers in the network.
It shows that the network should make the same level of adjustment whether it’s 67% right or 99% right.
It shows that at some point the processing power cost will be too great for the neural network to make accurate predictions.
ans. It helps the network determine the cost of the error so they can make larger or smaller adjustments to its guesses.
Question 2 of 2
How can you best describe the cost function as it applies to neural networks?
a measure of how accurate a machine learning estimate is
the amount of money spent to develop a neural network
a number the system uses to measure its answer against the correct answer
ans. a number the system uses to measure its answer against the correct answer