Basic AI Fundamentals Quiz

No.	Training Unit	Lecture	Training content	Question	Level	Mark	Answer	Answer Option A	Answer Option B	Answer Option C	Answer Option D	Explanation
1	Unit 1: Basic AI Fundamentals	Lec1	RAG Architecture	What is RAG (Retrieval-Augmented Generation), a hybrid AI architecture, designed to do?	Medium	1	C	Increase the speed of natural language processing	Reduce the cost of training language models	Enhance the quality and reliability of Large Language Models	Increase the creativity of language models	RAG is designed to enhance the quality and reliability of Large Language Models (LLMs) by integrating an information retrieval step from an external knowledge base before the LLM generates text.
2	Unit 1: Basic AI Fundamentals	Lec1	RAG Core Problems	What is one of the core technical problems that RAG solves?	Easy	1	A	Reduce hallucination (making up information)	Improve data retrieval speed	Increase data storage capacity	Enhance information security	RAG addresses limitations of traditional LLMs such as hallucination, outdated knowledge, lack of transparency, and difficulty accessing specialized knowledge.
3	Unit 1: Basic AI Fundamentals	Lec1	RAG vs Fine-tuning	What is the advantage of RAG over fine-tuning when updating knowledge for LLMs?	Medium	1	D	RAG is only suitable for unstructured data	RAG requires greater computing resources	RAG has lower transparency	RAG allows faster knowledge updates	RAG allows quick and nearly instant knowledge updates by updating the vector database, while fine-tuning requires retraining the model, which is expensive and slower.
4	Unit 1: Basic AI Fundamentals	Lec1	RAG Use Cases	When should you choose RAG instead of fine-tuning an LLM?	Medium	1	A	When you need to add factual knowledge and answer questions based on new data	When you need to reduce model operating costs	When you need to enhance the model’s reasoning ability	When you need to adjust the model’s behavior and style	RAG is suitable when you need to add factual knowledge and answer questions based on new data, while fine-tuning is appropriate when you need to adjust behavior, style, or learn a new skill.
5	Unit 1: Basic AI Fundamentals	Lec1	RAG Pipeline	In the RAG architecture, which phase occurs once or periodically to prepare data?	Easy	1	D	Query vectorization phase	Similarity search phase	Retrieval and answer generation phase (Retrieval Generation Online)	Data indexing phase (Indexing Offline)	The Data Indexing phase (Indexing Offline) occurs once or periodically to prepare data for RAG.
6	Unit 1: Basic AI Fundamentals	Lec1	Chunking	What is the purpose of dividing data into smaller text chunks in the ‘Load and Chunk’ step?	Easy	1	A	To ensure semantics are not lost and optimize for searching	To simplify the vectorization process	To reduce the storage capacity of data	To speed up data loading into the system	Chunking ensures that semantics are not lost and optimizes for searching.
7	Unit 1: Basic AI Fundamentals	Lec1	Vector Similarity	What is the most common method for measuring similarity between query vectors and document vectors in a Vector Database?	Medium	1	C	Manhattan distance	Jaccard similarity	Cosine Similarity	Euclidean distance	Cosine Similarity is the most common method for measuring the cosine angle between two vectors.
8	Unit 1: Basic AI Fundamentals	Lec1	RAG Online Phase	What happens to the user’s question in the first step of the ‘Retrieval and Answer Generation’ phase?	Easy	1	D	The question is stored in the database	The question is divided into smaller chunks	The question is translated to another language	The question is vectorized using an Embedding model	The user’s question is vectorized using an Embedding model.
9	Unit 1: Basic AI Fundamentals	Lec1	Embedding Quality	The quality of which component directly affects the effectiveness of the entire RAG system?	Medium	1	A	Embedding model	Similarity search method	Vector database	Prompting technique	The quality of the Embedding model directly affects the effectiveness of the entire system.
10	Unit 1: Basic AI Fundamentals	Lec1	Softmax Function	In the LLM model, what is the role of the Softmax function?	Hard	1	A	Convert scores (logits) into a probability distribution to select the most likely word	Filter out irrelevant sentences or information in text chunks	Calculate scores (logits) for all words in the vocabulary	Search for suitable text chunks	The Softmax function converts scores (logits) into a probability distribution, helping the model select the most likely word to appear.
11	Unit 1: Basic AI Fundamentals	Lec1	HyDE Technique	What is the HyDE (Hypothetical Document Embeddings) technique used for?	Hard	1	A	Expand the input query to improve retrieval results	Re-evaluate the relevance of each (question, chunk) pair	Filter out irrelevant information in text chunks	Combine the power of keyword search and vector search	HyDE uses a small LLM to generate a hypothetical document containing the answer, then uses this document’s vector for searching, improving retrieval results.
12	Unit 1: Basic AI Fundamentals	Lec1	Hybrid Search	What is Hybrid Search?	Medium	1	A	A method that combines the power of keyword search and vector search	A method that re-evaluates the relevance of each (question, chunk) pair	A method that transforms questions to improve retrieval results	A method that compresses context before putting it into the prompt	Hybrid Search combines keyword search (e.g., BM25) and vector search to achieve more comprehensive results.
13	Unit 1: Basic AI Fundamentals	Lec1	Context Compression	What is the purpose of Context Compression?	Medium	1	D	Rearrange potential candidates to select the top quality chunks	Transform input questions to improve retrieval results	Improve the accuracy of information retrieval	Reduce prompt length and help LLM focus on core information	Context Compression helps reduce prompt length and helps the LLM focus on core information by filtering out irrelevant information.
14	Unit 1: Basic AI Fundamentals	Lec1	Re-ranker	What is the role of a Re-ranker in the RAG process?	Medium	1	C	Compress text chunks to reduce prompt length	Transform the original question to improve retrieval results	Re-evaluate the relevance of each (question, chunk) pair and reorder them	Search for text chunks based on keywords	Re-ranker re-evaluates the relevance of each (question, chunk) pair and reorders them to select the top quality chunks.
15	Unit 1: Basic AI Fundamentals	Lec1	Retriever Failure	What happens if the retrieval system (retriever) does not find accurate documents in the RAG system?	Medium	1	B	The system will automatically adjust retrieval parameters to find more suitable documents	The Large Language Model (LLM) cannot answer correctly	The Large Language Model (LLM) will search for information from external sources to compensate for missing data	The Large Language Model (LLM) can still generate accurate answers based on prior knowledge	If the retriever does not find the correct documents, no matter how smart the LLM is, it cannot answer correctly.
16	Unit 1: Basic AI Fundamentals	Lec1	Lost in the Middle	What does the ‘Lost in the Middle’ syndrome in RAG systems refer to?	Hard	1	A	The tendency of LLMs to focus on information at the beginning and end of long contexts, ignoring information in the middle	Text chunks having duplicate information in the middle, causing noise in processing	Difficulty integrating LLMs in the middle of the retrieval and generation process	Delays in information retrieval when relevant documents are in the middle position in the database	When prompts contain long contexts, LLMs tend to focus only on information at the beginning and end, easily ignoring important details in the middle.
17	Unit 1: Basic AI Fundamentals	Lec1	Faithfulness Evaluation	What does ‘Faithfulness’ evaluation in RAG systems measure?	Medium	1	A	The degree to which the generated answer adheres to the provided context	The speed of processing and generating answers by the system	The relevance of the answer to the user’s question	The system’s ability to retrieve information from different sources	Faithfulness measures the degree to which the generated answer adheres to the provided context. Does the system add information on its own?
18	Unit 1: Basic AI Fundamentals	Lec1	Attention Mechanism	What role does the Attention Mechanism play in the Transformer architecture of RAG systems?	Hard	1	C	Improve the model’s parallel processing capability, helping to speed up computation	Reduce dependence on fully connected layers in the model	Allow the model to weigh the importance of different words in the input sequence for deep context understanding	Enhance the ability to encode input information into semantic vectors	The Attention Mechanism allows the model to weigh the importance of different words in the input sequence for deep context understanding.
19	Unit 1: Basic AI Fundamentals	Lec1	MRR Metric	What does the Mean Reciprocal Rank (MRR) metric measure in Retrieval Evaluation?	Hard	1	C	Measure the system’s ability to synthesize information from different sources	Measure the relevance between the question and the generated answer	Measure the position of the first correct chunk in the returned result list	Measure the percentage of questions for which the system retrieves at least one chunk containing correct answer information	Mean Reciprocal Rank (MRR) measures the position of the first correct chunk in the returned result list. The higher the position, the higher the MRR score.
20	Unit 1: Basic AI Fundamentals	Lec1	Value in RAG	In the RAG model, which element represents the actual extracted information?	Medium	1	D	Key	Query	Key vector dimension (d_k)	Value	Value represents the actual extracted information in the RAG model.
21	Unit 1: Basic AI Fundamentals	Lec1	Multimodal RAG	Which RAG development direction allows retrieving information from different types of data such as images, audio, and text?	Easy	1	A	Multimodal RAG	Internal RAG system	Agentic RAG	RAG Chatbot	Multimodal RAG allows retrieving information from different data sources, not just text.
22	Unit 1: Basic AI Fundamentals	Lec1	Agentic RAG	Which type of RAG application has the ability to ask sub-questions and interact with external tools to gather information?	Medium	1	B	Internal document RAG system	Agentic RAG	Multimodal RAG	RAG Chatbot	Agentic RAG is more proactive in gathering information by asking sub-questions and interacting with external tools.
23	Unit 1: Basic AI Fundamentals	Lec1	Enterprise RAG	Which RAG application helps employees search for information in the company’s internal documents quickly and accurately?	Easy	1	D	Multimodal RAG	Research and specialized analysis assistant	Smart customer support chatbots	Enterprise internal document RAG system	Enterprise internal document RAG systems help employees search for information quickly and accurately.
24	Unit 1: Basic AI Fundamentals	Lec1	Interactive Learning	What problem does RAG (Retrieval-Augmented Generation) application solve in interactive learning?	Medium	1	C	Limited access to learning materials	Inaccurate assessment of learning outcomes	Boredom and passivity when learning through textbooks	Lack of updated information in textbooks	RAG creates interactive tools that allow students to interact with learning materials more actively compared to reading traditional textbooks.
25	Unit 1: Basic AI Fundamentals	Lec1	Financial RAG	In the financial field, how can RAG support analysts?	Medium	1	A	Summarize and analyze risks from long financial reports	Manage personal investment portfolios	Predict stock market fluctuations	Automatically create financial reports	RAG can summarize and analyze risks from long financial reports, helping analysts save time and make decisions faster.
26	Unit 1: Basic AI Fundamentals	Lec1	E-commerce RAG	How does RAG improve product recommendation systems on e-commerce sites?	Medium	1	A	Retrieve information from detailed descriptions, product reviews, and technical specifications	Optimize product prices based on competitors	Provide 24/7 online customer support services	Enhance the ability to predict customer needs	RAG retrieves information from detailed descriptions, product reviews, and technical specifications to provide personalized recommendations, rather than relying solely on click history.
27	Unit 1: Basic AI Fundamentals	Lec1	RAG Distinctive Feature	What is the distinctive feature of RAG compared to traditional generative AI systems?	Medium	1	D	Integration with cloud platforms to increase scalability	Using the most advanced deep learning algorithms	Ability to automatically adjust parameters to optimize performance	Combining the deep language capabilities of LLMs with the accuracy of external knowledge bases	RAG combines the language capabilities of LLMs with the accuracy and up-to-date nature of external knowledge bases, creating more reliable and transparent AI applications.
28	Unit 1: Basic AI Fundamentals	Lec1	Vector Database	What is the primary purpose of a Vector Database in a RAG system?	Easy	1	B	Store raw text documents for quick retrieval	Store and efficiently search through vector embeddings	Manage user authentication and access control	Cache frequently asked questions and answers	A Vector Database is specifically designed to store and efficiently search through vector embeddings, enabling fast similarity searches in the RAG pipeline.
29	Unit 1: Basic AI Fundamentals	Lec1	Chunking Strategies	Which chunking strategy maintains the logical structure of a document by splitting at natural boundaries?	Medium	1	C	Fixed-size chunking	Random chunking	Semantic chunking	Overlapping chunking	Semantic chunking splits documents at natural boundaries (paragraphs, sentences, sections) to maintain logical structure and preserve meaning within each chunk.
30	Unit 1: Basic AI Fundamentals	Lec1	Top-K Retrieval	What does the ‘Top-K’ parameter control in RAG retrieval?	Easy	1	A	The number of most similar documents to retrieve	The maximum length of each chunk	The threshold for similarity scores	The number of re-ranking iterations	Top-K parameter controls how many of the most similar documents are retrieved from the vector database to provide context for the LLM.
31	Unit 1: Basic AI Fundamentals	Lec1	Prompt Engineering	In RAG systems, what is the role of the system prompt when generating answers?	Medium	1	B	To store retrieved documents permanently	To instruct the LLM on how to use the retrieved context to generate answers	To perform the similarity search in the vector database	To convert user queries into embeddings	The system prompt instructs the LLM on how to use the retrieved context to generate accurate, grounded answers and may include formatting guidelines and constraints.
32	Unit 1: Basic AI Fundamentals	Lec1	Answer Relevance	What does ‘Answer Relevance’ measure in RAG evaluation?	Medium	1	C	How fast the system generates responses	The accuracy of the embedding model	How well the generated answer addresses the user’s original question	The number of retrieved documents used	Answer Relevance measures how well the generated answer addresses the user’s original question, ensuring the response is pertinent and useful.
33	Unit 1: Basic AI Fundamentals	Lec1	Context Window	What limitation does the ‘context window’ impose on RAG systems?	Hard	1	D	The maximum number of documents that can be stored	The time limit for generating responses	The minimum similarity score for retrieval	The maximum amount of text that can be processed by the LLM at once	The context window limits the maximum amount of text (retrieved chunks + query + system prompt) that can be processed by the LLM at once, requiring careful management of chunk sizes.
34	Unit 1: Basic AI Fundamentals	Lec1	Metadata Filtering	What is the benefit of using metadata filtering in RAG retrieval?	Medium	1	A	Narrow down search results based on document attributes before semantic search	Increase the size of the vector database	Speed up the embedding generation process	Reduce the cost of LLM API calls	Metadata filtering allows narrowing down search results based on document attributes (date, source, category) before or during semantic search, improving retrieval precision.
35	Unit 1: Basic AI Fundamentals	Lec1	Hallucination Prevention	Which technique helps prevent hallucination in RAG systems by ensuring answers are grounded in retrieved content?	Hard	1	B	Increasing the temperature parameter	Instructing the LLM to only use information from the provided context	Using larger embedding dimensions	Reducing the Top-K value to 1	Instructing the LLM through the system prompt to only use information from the provided context and to say “I don’t know” when information is not available helps prevent hallucination and ensures answers are grounded in retrieved content.