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AI-PO Assessment

AI-PO Assessment

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AI Essentials for Product Owners Assessment

An assessment to evaluate your AI knowledge required for product ownership.

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The minimum passing score is 12 out of 20.

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Question 1 of 20

1. Question
What does LLM stand for?
- Low Language Model
- Large Leverage Model
- Language Large Model
- Large Language Model
Question 2 of 20

2. Question
Which AI applications are relevant for enhancing customer understanding? (choose the best three answers)
- Hyper-segmentation of users
- Predictive analytics for user behavior
- Automated sprint planning
- Generating personas based on data
Question 3 of 20

3. Question
Which one is correct?
- AI, Deep Learning, and Machine Learning are the same
- Machine Learning and AI are sub-disciplines of Deep Learning, and Machine Learning is a sub-discipline of AI
- Machine Learning and Deep Learning are sub-disciplines of AI, and Deep Learning is a sub-discipline of Machine Learning
- Deep Learning and AI are sub-disciplines of Machine Learning, and Deep Learning is a sub-discipline of AI
Question 4 of 20

4. Question
What is the difference between AI and Generative AI? (choose the best two answers)
- Both AI and Generative AI models generate entirely new content based on the data they have been trained on. The only difference is that AI is more advanced
- AI exhibits human-like intelligence, such as learning, problem-solving, and decision-making while Generative AI focuses on creating new content
- Generative AI is like a music store that can suggest various existing music based on your preferences while AI is like a musician that can compose a completely new song
- AI is like a chef who can cook many different meals from an existing list, while Generative AI is like a chef who can invent new dishes
Question 5 of 20

5. Question
What can LLMs do? (choose multiple answers)
- Brainstorming
- Translation
- Proofreading
- Feeling
- Summarizing text
- Categorization
- Reading text
- Chatting
Question 6 of 20

6. Question
What are the potential security risks when using AI-powered voice assistants? (choose the best three answers)
- Data breaches involving sensitive customer information
- Biases in voice recognition algorithms excluding certain accents/dialects
- Lack of transparency in decision-making logic
- Increased battery consumption during operation
Question 7 of 20

7. Question
Which one is correct? (choose the best three answers)
- LLMs work based on Natural Language Processing (NLP)
- Natural Language Processing (NLP) focuses on enabling computers to interpret and generate human language
- Generative AI models understand their answers like humans
- Generative AI models generate outputs based on patterns in the data they have been trained on and operate without genuine comprehension, intention, or awareness
Question 8 of 20

8. Question
Which one is NOT the LLM limit?
- Hallucination
- Knowledge cutoff
- Chatting tone
- Bias
- Input & Output length
Question 9 of 20

9. Question
Which method enhances LLMs’ capabilities to generate more accurate answers? (choose the best four answers)
- Fine-tuning
- Ignore-go tuning
- RAG (Retrieval Augmented Generation)
- Instruction tuning
- RLHF (Reinforcement Learning from Human Feedback)
Question 10 of 20

10. Question
You are using AI to generate user personas for the Volkswagen ID.x in-car experience. After reviewing the AI-generated personas, you notice that they heavily favor one demographic (e.g., urban professionals) while excluding others (e.g., rural families). What should you do next?
- Accept the personas as they are because they are data-driven.
- Use AI to refine the personas by providing additional data inputs to address the imbalance.
- Discard the AI-generated personas and create them manually.
- Prioritize features based on the existing personas since urban professionals are likely a key target demographic.
Question 11 of 20

11. Question
Which one is correct about Hallucination? (choose the best two answers)
- Generating correct information based on the data the model has been trained on
- Generating incorrect or misleading information that is not supported by real-world facts
- This is the imagination power of LLMs helping people start creating something new
- An example of hallucination is generating a fictional story of the future with a real character from the past
Question 12 of 20

12. Question
Imagine you have been hired as an employee in a company. They have an AI-based application to onboard new employees. You ask the application how to have a company mobile phone. How did they enhance their application to give such accurate context-based answers?
- The LLM model doesn’t need enhancement, the original training is enough to do such a thing
- They have given a few samples of similar questions to the model behind the application
- It is not possible to have such an application
- They have given the related HR policy document to the application, which is a type of RAG
Question 13 of 20

13. Question
Which one is correct about a Prompt? (choose the best four answers)
- Prompts optimize the response of Generative AI models
- A Prompt is any input or series of instructions used to produce a desired output
- There isn’t any limit to the length of a Prompt
- Naive Prompts result in generic output
- Creating quality output requires an iterative approach to Prompting
Question 14 of 20

14. Question
You are tasked with prioritizing features for the next sprint using AI tools that analyze business value, user impact, and technical feasibility. Which actions should you take to ensure the ethical and effective use of AI in this process? (choose the best three answers)
- Ensure transparency by documenting how AI arrived at its prioritization recommendations.
- Rely solely on AI recommendations without consulting stakeholders to avoid bias.
- Regularly review and validate the data used by AI to ensure it is up-to-date and unbiased.
- Use AI recommendations as one input among others, such as stakeholder feedback and market trends.
Question 15 of 20

15. Question
What is Prompt Engineering? (choose the best three answers)
- Prompt Engineering is the process of designing effective Prompts to generate better and desired responses
- Prompt Engineering is an iterative experimentation process to achieve desired answers
- Prompt Engineering requires a lot of syntaxes like a programming language that people have to learn
- Prompt Engineering is the way of writing Prompts based on human natural language making it easy for everyone to communicate with AI models
Question 16 of 20

16. Question
Which one is a Prompt Engineering approach? (choose the best three answers)
- Interview Pattern Prompting
- Chain-of-Thought Prompting
- Tree-of-Thought Prompting
- Ecocycle Pattern Prompting
Question 17 of 20

17. Question
Which one is correct about AI models? (choose the best three answers)
- Diffusion Models are used to generate new images
- Large Language Models (LLMs) are used to generate new text
- Diffusion Models are used to generate new audio
- Large Language Models (LLMs) are used for software programming
Question 18 of 20

18. Question
Which statement is correct about Prompting? (choose applicable answers)
- Few-Shot Learning Prompts: give a few examples of the desired input-output format to guide the LLM toward a new task
- Zero-Shot Prompts: directly instruct the model to perform a task without any additional examples to steer it
- Story Continuation Prompts: set the stage for a narrative and let the LLM continue the story
- Task Completion Prompts: provide a scenario or problem and ask the LLM to complete a task related to it
Question 19 of 20

19. Question
What are some well-known Large Language Models (LLMs)? (choose the best three answers)
- GPT (by OpenAI)
- DALL·E (by OpenAI)
- Gemini (by Google DeepMind)
- Llama (by Meta (Facebook))
- Stable Diffusion XL (by Stability AI)
Question 20 of 20

20. Question
You’re using an AI tool to help generate user stories for your product backlog. The AI produces a large number of stories, some of which seem innovative but others that appear irrelevant. What would you do?
- Include all AI-generated stories in the backlog for the team to review.
- Manually filter the stories, only adding those you deem relevant to the backlog.

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