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Free AIGP Practice Questions

10 free, exam-style AI Governance Professional (AIGP) practice questions with answers and explanations. No signup required. Work through them below, then take the full free AIGP practice test to study every exam domain.

Question 1

Machine learning is best described as a type of algorithm by which:

  1. Systems can mimic human intelligence with the goal of performing routine tasks
  2. Systems can automatically improve from experience through predictive patterns
  3. Statistical inferences are drawn from a sample with the goal of predicting human intelligence
  4. Previously unknown properties are discovered in data and used to predict improvements
Show answer & explanation

Correct answer: B - Systems can automatically improve from experience through predictive patterns

Question 2

An AI system studies a large set of unlabeled data and tries to detect hidden patterns within it. What type of machine learning is being used?

  1. Supervised learning
  2. Unsupervised learning
  3. Reinforcement learning
  4. Transfer learning
Show answer & explanation

Correct answer: B - Unsupervised learning

Question 3

In supervised learning, the AI model is trained using:

  1. Unlabeled data to find hidden patterns
  2. Labeled data with known inputs and outputs
  3. A reward and punishment system
  4. Real-time feedback from users
Show answer & explanation

Correct answer: B - Labeled data with known inputs and outputs

Question 4

A company developed AI technology that can analyze text, video, images, and sound to tag content. What type of AI model is this classified as?

  1. Narrow AI
  2. Multi-modal model
  3. Expert system
  4. Symbolic AI
Show answer & explanation

Correct answer: B - Multi-modal model

Question 5

What distinguishes generative AI from discriminative AI?

  1. Generative AI only works with text data
  2. Generative AI creates new content while discriminative AI classifies existing data
  3. Discriminative AI is more accurate than generative AI
  4. Generative AI requires less training data
Show answer & explanation

Correct answer: B - Generative AI creates new content while discriminative AI classifies existing data

Question 6

A large language model (LLM) that has been trained on massive datasets and can be adapted for multiple downstream tasks is known as a:

  1. Narrow AI system
  2. Foundation model
  3. Expert system
  4. Symbolic reasoning engine
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Correct answer: B - Foundation model

Question 7

Which of the following best describes deep learning?

  1. AI systems that use simple if-then rules
  2. Machine learning using neural networks with multiple layers
  3. AI that requires human intervention for each decision
  4. Statistical analysis using linear regression
Show answer & explanation

Correct answer: B - Machine learning using neural networks with multiple layers

Question 8

Natural Language Processing (NLP) enables AI systems to:

  1. Process and understand human language
  2. Only translate between programming languages
  3. Create visual art from descriptions
  4. Perform mathematical calculations faster
Show answer & explanation

Correct answer: A - Process and understand human language

Question 9

Computer vision is a field of AI that enables machines to:

  1. Generate computer code automatically
  2. Interpret and understand visual information from the world
  3. Communicate with other computers more efficiently
  4. Predict stock market trends
Show answer & explanation

Correct answer: B - Interpret and understand visual information from the world

Question 10

In reinforcement learning, an AI agent learns by:

  1. Analyzing labeled training data
  2. Receiving rewards and penalties based on actions
  3. Copying human behavior exactly
  4. Using pre-programmed rules
Show answer & explanation

Correct answer: B - Receiving rewards and penalties based on actions

What's on the AIGP exam

The AI Governance Professional (AIGP) exam is organized into 4 knowledge domains. These free practice questions are drawn from across them so you can see where you're strong and where to study:

  1. Foundations of Artificial Intelligence and Responsible AI Principles
  2. AI Laws, Regulations, and Standards
  3. AI Development Lifecycle and Governance
  4. AI Deployment, Risk Management, and Generative AI Risks

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