AI Knowledge Information 2 — Questions and Answers
Question 1: Which knowledge representation technique uses probabilistic relationships between variables to model uncertainty?
- Decision tree
- Bayesian network (Correct answer)
- Rule-based system
- Semantic network
Correct answer: Bayesian network
Bayesian networks use directed acyclic graphs to represent probabilistic dependencies between variables, enabling inference under uncertainty.
Question 2: In a knowledge graph, what is the primary purpose of an 'ontology'?
- Store raw data efficiently
- Define concepts, relationships, and rules within a domain (Correct answer)
- Optimize query execution speed
- Encrypt sensitive knowledge entries
Correct answer: Define concepts, relationships, and rules within a domain
An ontology formally defines the concepts, their properties, and relationships within a domain, providing a shared vocabulary and structure for the knowledge graph.
Question 3: What is 'knowledge distillation' in the context of AI model compression?
- Removing duplicate facts from a knowledge base
- Training a smaller student model to mimic a larger teacher model (Correct answer)
- Extracting rules from a neural network's weights
- Converting symbolic knowledge to vector embeddings
Correct answer: Training a smaller student model to mimic a larger teacher model
Knowledge distillation transfers knowledge from a large, complex teacher model to a smaller student model by training the student on soft probability outputs from the teacher.
Question 4: Which information retrieval metric measures the fraction of relevant documents returned out of all documents returned?
- Recall
- F1 score
- Precision (Correct answer)
- Mean reciprocal rank
Correct answer: Precision
Precision measures how many of the retrieved documents are actually relevant, calculated as true positives divided by all retrieved documents.
Question 5: In retrieval-augmented generation (RAG), what role does the 'retriever' component play?
- Fine-tunes the language model on domain data
- Fetches relevant context documents to ground the generator's response (Correct answer)
- Evaluates the factual accuracy of generated text
- Compresses the knowledge base into dense embeddings only
Correct answer: Fetches relevant context documents to ground the generator's response
The retriever searches an external knowledge store for documents relevant to the query and passes them as context to the generative model.
Question 6: What does 'knowledge grounding' refer to in natural language processing?
- Tokenizing text into subword units
- Linking language model outputs to verified external facts or real-world entities (Correct answer)
- Converting knowledge graphs to relational databases
- Initializing model weights with domain-specific values
Correct answer: Linking language model outputs to verified external facts or real-world entities
Knowledge grounding connects model-generated statements to external, verifiable sources to reduce hallucinations and improve factual reliability.
Question 7: Which of the following best describes 'closed-world assumption' in knowledge systems?
- Any fact not known to be true is assumed false (Correct answer)
- The knowledge base grows continuously from new data
- Only expert-verified facts are stored in the system
- All possible facts must be enumerated at system startup
Correct answer: Any fact not known to be true is assumed false
Under the closed-world assumption, anything not explicitly present in the knowledge base is treated as false, unlike the open-world assumption used in many semantic web systems.
AI Knowledge Information 2
Which knowledge representation technique uses probabilistic relationships between variables to model uncertainty?