robotics-course/exam_1

1[2    {3        "question": "Which of the following best describes a Large Language Model (LLM)?",4        "answer_a": "A model specializing in language recognition",5        "answer_b": "A massive neural network that understands and generates human language",6        "answer_c": "A model exclusively used for language data tasks like summarization or classification",7        "answer_d": "A rule-based chatbot used for conversations",8        "correct_answer": "B"9    },10    {11        "question": "LLMs are typically:",12        "answer_a": "Pre-trained on small, curated datasets",13        "answer_b": "Trained on large text corpora to capture linguistic patterns",14        "answer_c": "Trained purely on translation tasks",15        "answer_d": "Designed to function solely with GPU resources",16        "correct_answer": "B"17    },18    {19        "question": "Which of the following is a common architecture for LLMs?",20        "answer_a": "Convolutional Neural Networks (CNNs)",21        "answer_b": "Transformer",22        "answer_c": "Recurrent Neural Networks (RNNs) with LSTM",23        "answer_d": "Support Vector Machines",24        "correct_answer": "B"25    },26    {27        "question": "What does it mean when we say LLMs are \"autoregressive\"?",28        "answer_a": "They regress to the mean to reduce variance",29        "answer_b": "They generate text by predicting the next token based on previous tokens",30        "answer_c": "They can only handle labeled data",31        "answer_d": "They can output text only after the entire input is known at once",32        "correct_answer": "B"33    },34    {35        "question": "Which of these is NOT a common use of LLMs?",36        "answer_a": "Summarizing content",37        "answer_b": "Generating code",38        "answer_c": "Playing strategy games like chess or Go",39        "answer_d": "Conversational AI",40        "correct_answer": "C"41    },42    {43        "question": "Which of the following best describes a \"special token\"?",44        "answer_a": "A token that makes the model forget all context",45        "answer_b": "A model signature required for API calls",46        "answer_c": "A token that helps segment or structure the conversation in the model",47        "answer_d": "A token that always represents the end of text",48        "correct_answer": "C"49    },50    {51        "question": "What is the primary goal of a \"chat template\"?",52        "answer_a": "To force the model into a single-turn conversation",53        "answer_b": "To structure interactions and define roles in a conversation",54        "answer_c": "To replace the need for system messages",55        "answer_d": "To store prompts into the model's weights permanently",56        "correct_answer": "B"57    },58    {59        "question": "How do tokenizers handle text for modern NLP models?",60        "answer_a": "By splitting text into individual words only",61        "answer_b": "By breaking words into subword units and assigning numerical IDs",62        "answer_c": "By storing text directly without transformation",63        "answer_d": "By removing all punctuation automatically",64        "correct_answer": "B"65    },66    {67        "question": "Which role in a conversation sets the overall behavior for a model?",68        "answer_a": "user",69        "answer_b": "system",70        "answer_c": "assistant",71        "answer_d": "developer",72        "correct_answer": "B"73    },74    {75        "question": "Which statement is TRUE about tool usage in chat templates?",76        "answer_a": "Tools cannot be used within the conversation context.",77        "answer_b": "Tools are used only for logging messages.",78        "answer_c": "Tools allow the assistant to offload tasks like web search or calculations.",79        "answer_d": "Tools are unsupported in all modern LLMs.",80        "correct_answer": "C"81    }82]