Module 3: Final project and course wrap up

Course quiz: Generative AI in software development

Graded Assignment

1. Which of the following outcomes aligns with the goals of this course? Select all that apply.

  • Exploring ethical implications of AI tools. ✅
  • Learning the theoretical structure of generative AI.
  • Building expertise in coding decision trees.
  • Understanding how generative AI helps to create new content in software development. ✅

Explanation:
 These align with the goals of the course because it covers ethical concerns and how generative AI helps in software development by creating new content.

2. Which of the following examples demonstrates the capabilities of Generative AI?

Select all that apply.

  • Completing a program in Java with suggested enhancements and explanations. ✅
  • Producing a unique painting from a text prompt. ✅
  • Writing a new story based on a theme. ✅
  • Writing a new story without any human prompt.

Explanation:
Generative AI is capable of enhancing code, creating art from prompts, and generating new stories based on themes. However, generating a story without any prompt is not typically possible for generative AI at this stage.

3. A Generative AI model creates a recipe by learning from thousands of cookbooks and generates a song by analyzing popular music trends. Which concepts describe how AI works to create content like recipes and songs?

Select all that apply.

  • Pattern recognition from large datasets. ✅ 
  • Identifying patterns in large datasets (e.g., cookbooks or music) and creating new outputs based on those learned patterns. ✅
  • Generating outputs based on learned patterns without relying on extensive datasets.
  • Use of explicit, hard-coded instructions like if, if-else, for, switch.

Explanation:
AI relies on large datasets to learn patterns and generate content. It doesn’t generate outputs without data or based on explicit rules.

4. A developer uses a generative AI tool to build a program. Which of the following tasks can the tool assist with? Select all that apply.

  • Deploying the program to production automatically.
  • Providing suggestions based on comments in the code. ✅
  • Completing a partially written function. ✅
  • Debugging and improving an existing code snippet. ✅

Explanation:
Generative AI tools assist with providing suggestions, completing functions, and debugging code, but they cannot automatically deploy the code to production.

5. Which of the following is a key ethical concern associated with generative AI?

  • The risk of diminishing human creativity, such as singing, due to AI’s ability to compose songs.
  • The concern about developers profiting significantly from users of GenAI.
  • Its inability to automate tasks without human input.
  • The potential for bias in AI-generated content. ✅

Explanation:
One of the major ethical concerns in generative AI is the risk of bias in the content it generates, which can reflect biases present in the training data.

6. What is one key benefit of using generative AI tools in Java development?

  • Eliminating the need for developers to learn Java.
  • Completely replacing debugging and testing processes.
  • Enhancing coding efficiency by generating boilerplate code. ✅
  • Automatically deploying Java applications to production.

Explanation:
Generative AI helps developers by automating repetitive tasks like generating boilerplate code, but it doesn’t completely replace debugging or testing.

7. A user wants to generate a detailed essay using a GenAI tool. Which prompt engineering techniques could help? Select all that apply.

  • Provide clear instructions, such as, “Write a 500-word essay about climate change in the USA during the last 20 years starting from the current year backward.” ✅
  • Specifying role play and the target audience. ✅
  • Offering examples of the desired output to provide the context. ✅
  • Using vague terms like, “Give me something interesting”.

Explanation:
Clear instructions and examples help AI understand the task and produce accurate, relevant outputs, while vague terms like “Give me something interesting” are not specific enough.

8. Amazon Q Developer enhances productivity in software development by _ _ _ _ _ _.

  • Providing code suggestions, explanations, reviews, fixes, optimizations and debugging capabilities ✅
  • Replacing the need for software testing.
  • Automating the deployment of applications.
  • Generating new programming languages.

Explanation:
Amazon Q Developer enhances developer productivity by suggesting code improvements, explanations, and optimizations but does not handle application deployment or create new programming languages.

9. What is the main feature of GitHub Copilot in software development?

  • Generating code suggestions and completing partially written code. ✅
  • Automating the deployment of applications to production.
  • Designing the architecture of software applications.
  • Providing an option for employers to replace software developers.

Explanation:
 GitHub Copilot is designed to assist developers by suggesting code and completing incomplete code, not by automating deployment or software architecture design.

10. Machine Learning (ML) is a subset of Artificial Intelligence (AI) that _ _ _ _ _ _.

  • Learns from data and improves without explicit programming. ✅
  • Makes decisions based solely on predefined rules.
  • Learns from the prompt given by the user.
  • Makes decisions based solely on the programming logic input in the code.

Explanation:
Machine learning models improve over time by learning from data, unlike traditional programming methods where rules are explicitly defined.

11. What is the primary function of a decision tree in Machine Learning?

  • To store data for future analysis.
  • To predict random outcomes without patterns.
  • To replicate the neural structure of a human brain.
  • To split data into branches based on feature values to make decisions. ✅

Explanation:
A decision tree organizes data based on feature values to make predictions or classifications.

12. Random forests improve prediction accuracy by combining _ _ _ _ _ _.

  • Multiple decision trees using techniques like bagging. ✅
  • The outputs of unrelated algorithms.
  • Predefined answers to every scenario possible.
  • The results of exactly two decision trees.

Explanation:
Random forests improve prediction accuracy by combining multiple decision trees using bagging, which helps reduce overfitting.

13. What is the primary purpose of a neural network in machine learning?

  • To process inputs and learn patterns for making predictions or decisions. ✅
  • To teach new information to humans using videos and the internet.
  • To make decisions based on rules put in the programming, resulting in a leaf node.
  • To combine the results of multiple trees into an averaged result.

Explanation:
 Neural networks are designed to learn from inputs, recognize patterns, and make predictions or decisions.

14. In a neural network, what happens when the input data is processed through the layers?

  • The data is stored in memory for future use.
  • It immediately results in a final decision.
  • The network generates random outputs unrelated to the input.
  • Patterns and features are extracted to make predictions. ✅

Explanation:
As input data is processed through layers in a neural network, it extracts relevant features and patterns that allow it to make predictions.

15. Digital transformation enables businesses to enhance efficiency and deliver value by _ _ _ _ _ .

  • Ignoring data and customer feedback
  • To use GenAI tools in all business processes.
  • Using digital technologies across various processes. ✅
  • Focusing only on software development.

Explanation:
Digital transformation leverages digital tools across processes to improve efficiency and create value, not just focusing on software development.

16. A business integrates AI tools, uses data to analyze trends, improves workflows, and trains employees to adapt to new tools. Which domains of digital transformation does this example represent? Select all that apply.

  • Cybersecurity
  • Change management ✅
  • Technology ✅
  • Data ✅

Explanation:
 The integration of AI, data, and changes in technology is central to digital transformation. Cybersecurity may also be relevant but wasn’t directly addressed here.

17. Which of the following technologies is commonly associated with driving digital transformation?

  • Manual filing systems
  • Internet of Things (IoT) ✅
  • Blockchain
  • Virtual reality (VR)

Explanation:
 Technologies like IoT, Blockchain, and VR are essential to driving digital transformation, unlike manual filing systems.

18. In digital transformation, data is primarily used to _ _ _ _ _ _.

  • Provide insights that guide decision-making and improve efficiency. ✅
  • Store historical records without analysis.
  • Automate every business function.
  • Predict the future with a guaranteed accuracy of 100%.

Explanation:
In digital transformation, data is analyzed to guide decisions, improve processes, and increase efficiency.

19. An AI-powered CRM system provides chatbots for customer support, recommends products based on purchase history, and predicts customer churn. Which benefits of AI in CRM are demonstrated? Select all that apply.

  • Providing 24/7 customer support. ✅
  • Predicting customer needs and improving satisfaction. ✅
  • Enhancing customer engagement through personalized recommendations. ✅
  • Replacing all human involvement in customer service.

Explanation:
AI-powered CRM systems improve customer engagement, predict needs, and offer support but don’t completely replace human customer service.

20. What is a common challenge when integrating AI solutions with existing business systems?

  • Ensuring compatibility with legacy systems and workflows. ✅
  • Not quite. Please review the video Integration with existing systems .
  • AI completely replacing all existing systems.
  • The inability of AI to manage the costs of AI implementation.

Explanation:
A common challenge is ensuring that AI solutions are compatible with existing systems, as opposed to completely replacing them.

21. A company plans to deploy an AI tool that processes customer data. Which ethical and regulatory challenges should it consider? Select all that apply.

  • Mitigating biases in AI-generated decisions. ✅
  • Protecting data privacy and preventing misuse. ✅
  • Relying solely on internal policies to speed up deployment.
  • Ensuring customer consent for data usage. ✅

Explanation:
Ethical and regulatory concerns around AI include bias mitigation, protecting privacy, and ensuring consent for data usage.

22. A company plans to scale its AI system to handle millions of users. What could be the possible solutions for this process? Select all that apply.

  • Elasticity. ✅
  • Optimizing the AI model and the neural network.
  • Completely eliminating manual oversight of the system.
  • Load balancing. ✅

Explanation:
Scaling AI involves adjusting resources and load balancing to handle higher user volumes, not eliminating manual oversight.

23. Interpretability in AI is essential because it allows users to _ _ _ _ _ _.

  • *A: Understand how decisions are made. ✅
  • Guarantee that the AI is 100% accurate.
  • Remove all biases from the data.
  • Use the AI system without training.

Explanation:
Interpretability in AI is important because it allows users to understand the reasoning behind decisions made by the AI model.

24. Techniques like LIME and SHAP are used in AI to _ _ _ _ _ _.

  • Explain predictions and enhance model transparency. ✅
  • Increase the randomness of model outputs.
  • Remove all biases in AI systems.
  • Eliminate the need for feature selection.

Explanation:
LIME and SHAP are used to explain how AI models make predictions and to improve transparency.

25. A company deploys an AI system to handle sensitive customer data. What compliance steps should they take? Select all that apply.

  • Ensure the system meets data protection laws like GDPR. ✅
  • Avoid creating documentation for the system to protect its design.
  • Regularly audit the AI system for biases and fairness. ✅
  • Obtain customer consent for data usage. ✅

Explanation:
Compliance involves ensuring AI systems meet legal requirements, addressing biases, and obtaining consent for data usage.

26. A developer uses Generative AI tools like GitHub Copilot and Amazon Q Developer. Which of the following benefits are typical of these tools? Select all that apply.

  • Replacing the need for software testing.
  • Generating repetitive boilerplate code. ✅
  • Providing debugging suggestions for common errors. ✅
  • Offering intelligent code completion based on the context. ✅

Explanation:
Generative AI tools assist with common coding tasks such as generating boilerplate code and providing suggestions, but they don’t replace testing.

27. A team uses Amazon Q Developer to complete boilerplate code for a web application. Which tasks might the tool assist with? Select all that apply.

  • Designing the entire software architecture autonomously.
  • Suggesting fixes for incomplete code snippets. ✅
  • Identifying and resolving syntax errors. ✅
  • Generating code templates for common tasks like authentication. ✅

Explanation:
Amazon Q Developer helps with generating templates, fixing errors, and providing suggestions, but it does not autonomously design entire applications.

28. A neural network is trained to classify types of flowers based on features like petal length and width. The network misclassifies one type of flower. What could be the reason for this error? Select all that apply.

  • The network does not have enough hidden layers.
  • The training data contained too few examples of the misclassified flower. ✅
  • The input features were irrelevant or noisy. ✅
  • The neural network has memorized the training data instead of generalizing. ✅

Explanation:
Errors in classification can arise due to insufficient training data, irrelevant features, or overfitting (memorizing training data).

29. A company uses a decision tree to predict employee retention based on hours worked, job satisfaction, and age. How can they improve its accuracy? Select all that apply.

  • Add more diverse training data. ✅
  • Use random forests. ✅
  • Increase the number of leaf nodes indefinitely.
  • Use random feature selection for splits.

Explanation:
Improving accuracy involves using diverse data and random forests which help in creating better decision boundaries.

30. True or False: One of the major ethical concerns when using AI in business is ensuring fairness and avoiding bias in the decision-making process.

  • True ✅
  • False

Explanation:
Fairness and avoiding bias in AI decision-making are significant ethical concerns, as AI systems can unintentionally perpetuate biases.

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