Infosys Certified Generative AI Professional - Foundation
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Join Premium10 Infosys Certified Generative AI Professional - Foundation practice questions with answers
Real Lex exam-pattern multiple-choice questions for the Infosys Certified Generative AI Professional - Foundation certification. Each question includes the correct answer. The full question bank is available to Premium members.
- Question 1
Which of the following is an example of a use case for AI Canvas?
- ✓
Designing a marketing campaign
Correct - B
Building a self-driving car
- C
Creating a social media profile
- D
Planning a company picnic
ExplanationOption 2, "Building a self-driving car," is an example of a use case for AI Canvas. AI Canvas is a strategic planning tool specifically designed for artificial intelligence projects. Developing a self-driving car involves complex AI technologies and systems, making it a suitable use case for applying AI Canvas to align technical aspects with organizational goals, address stakeholders' concerns, and ensure strategic alignment throughout the project.
Options 1, 3, and 4 are not typical examples of AI projects that would directly benefit from AI Canvas. Designing a marketing campaign, creating a social media profile, and planning a company picnic involve tasks that may not heavily rely on the specialized considerations that AI Canvas addresses for AI projects.
- ✓
- Question 2
What role does transparency play in AI Canvas?
- ✓
It is not important for AI projects
Correct - B
It helps in confusing stakeholders
- C
It promotes trust, accountability, and ethical practices
- D
It slows down the project implementation
- ✓
- Question 3
A transportation company is exploring AI applications to improve route optimization and fuel efficiency. How can AI Canvas help the team in conceptualizing this use case and its potential impact?
- ✓
By focusing AI algorithm technical details alone
Correct - B
By outlining the physical infrastructure needed for AI implementation
- C
By considering the data sources, algorithms, and strategic alignment to address the company's transportation challenges
- D
By emphasizing revenue streams from fuel savings without considering data sources
- ✓
- Question 4
Which element of AI Canvas relates to the tangible and intangible benefits an AI project offers to its users or customers?
- ✓
Data
Correct - B
Integration
- C
Value Proposition
- D
Revenue
ExplanationOption 3, "Value Proposition," is the element of AI Canvas that relates to the tangible and intangible benefits an AI project offers to its users or customers. The value proposition outlines the value that the AI solution provides to its intended users, which may include aspects such as efficiency improvements, cost savings, enhanced user experience, or other specific benefits. This element helps define what makes the AI project valuable to its target audience and aligns the project with user needs and organizational goals.
- ✓
- Question 5
A social media platform is seeking to incorporate AI to recommend content to its users. Considering the evolving role of AI Canvas, how can it assist in ensuring ethical and responsible use of AI in this context?
- ✓
By actively addressing potential biases and considering the social impact of content ecommendations within the canvas framework
Correct - B
By disregarding ethical considerations and focusing solely on technical aspects
- C
By assuming that AI technologies are always ethically neutral
- D
By excluding user feedback and concerns from the canvas to streamline the process
- ✓
- Question 6
Who developed Stable Diffusion model?
- ✓
StabilityAI
Correct - B
OpenAI
- C
LAION
- D
RunwayML
- ✓
- Question 7
Which of the following statements about latent diffusion models and their advantages are true? (Select all that apply)
- ✓
Latent diffusion models work directly with pixel space and full-size images.
Correct - B
The main advantage of latent diffusion models is their ability to work sequentially on the entire image, leading to faster training and inference times.
- C
Latent diffusion models use random noise as inputs, which can be conditioned with text or images.
- D
Working in a compressed image representation allows latent diffusion models to handle different modalities like text and images.
- E
Latent diffusion models require hundreds of GPUs to train due to their high computational costs.
- ✓
- Question 8
Identify the usage of Stable Diffusion model
- ✓
Image compression
Correct - B
Image generation
- C
Image filtering
- D
Image segmentation
ExplanationOption 2, "Image generation," is a common usage of Stable Diffusion models. Stable Diffusion models are a type of generative model designed for image generation tasks. They can be used to generate realistic and diverse images by sampling from the learned distribution of the training data. These models are trained to capture complex patterns and variations in the input data, making them suitable for tasks like generating new images that resemble the ones seen during training.
- ✓
- Question 9
What is the Negative Prompt in Stable Diffusion?
- ✓
An input that generates a wide range of possible outputs
Correct - B
An input that focuses on enhancing the model's creative capabilities
- C
An input that allow the user to tell the model what not to generate.
- D
An input that accelerates the training process by providing additional context
ExplanationThe Negative Prompt in Stable Diffusion is Option 3: "An input that allows the user to tell the model what not to generate." In the context of generative models, negative prompts are inputs that guide the model away from generating certain types of content or specific characteristics. They are used to influence the model's output by providing information on what should be avoided. This can be a useful mechanism for controlling the content generated by the model.
- ✓
- Question 10
In the context of NLP, what does "perplexity" measure in language models?
- ✓
The level of confusion among the model's predictions
Correct - B
The model's ability to generate complex language constructs
- C
The computational complexity of the model's architecture
- D
The model's overall ability to predict unseen data
- ✓
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