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Question 30
A legal analyst wants the assistant to paraphrase long text accurately; image synthesis is not in scope. What model category primarily handles this?
- Rule-based keyword matcher
- Embedding model
- Large language model (LLM)
- Multimodal model
Explanation
Correct Answer
C:LLMs operate over written language for summarization and rewriting. An LLM specializes in interpreting and generating language and other tokenized sequences.
Incorrect Answers
A:Keyword matching finds specified words without a learned semantic representation or text generation. This does not directly meet the case requirement to summarize and rewrite natural-language documents.
B:An embedding model converts inputs into vectors suited to semantic similarity and retrieval. This does not directly meet the case requirement to summarize and rewrite natural-language documents.
D:A multimodal model handles more than one modality, such as text combined with images or audio. This does not directly meet the case requirement to summarize and rewrite natural-language documents.
Question 31
A lender has past applications marked Approved or Declined and trains a classifier. Which learning regime fits the labeled outcomes?
- Reinforcement learning
- Supervised learning
- Human evaluation
- Inference
Explanation
Correct Answer
B:The known decision labels act as supervised targets. Supervised learning fits labeled input-output examples to predict known targets.
Incorrect Answers
A:Reinforcement learning adapts a policy through actions and reward signals over time. This does not directly meet the case requirement to train a predictor using labeled outcomes.
C:Human review judges or scores outputs but is not by itself a model-training regime. This does not directly meet the case requirement to train a predictor using labeled outcomes.
D:Inference runs an already trained model on new input without updating its training weights. This does not directly meet the case requirement to train a predictor using labeled outcomes.
Question 32
A retailer has unlabeled baskets and wants clusters of shopping patterns. Which regime is suitable?
- Reinforcement learning
- Unsupervised learning
- Self-supervised pretraining
- Supervised learning
Explanation
Correct Answer
B:The task discovers structure without predefined class answers. Unsupervised methods discover patterns or structure in data without known target labels.
Incorrect Answers
A:Reinforcement learning adapts a policy through actions and reward signals over time. This does not directly meet the case requirement to find latent patterns in data with no labels.
C:Self-supervised learning derives training targets from the input data itself without manually annotated labels. This does not directly meet the case requirement to find latent patterns in data with no labels.
D:Supervised learning fits labeled input-output examples to predict known targets. This does not directly meet the case requirement to find latent patterns in data with no labels.
Question 33
A robot gets positive reward for successful navigation and penalties for collisions while choosing actions. What training paradigm is being used?
- Inference
- Reinforcement learning
- Human evaluation
- Unsupervised learning
Explanation
Correct Answer
B:Learning is driven by environmental feedback on actions. Reinforcement learning adapts a policy through actions and reward signals over time.
Incorrect Answers
A:Inference runs an already trained model on new input without updating its training weights. This does not directly meet the case requirement to learn a decision policy from action rewards.
C:Human review judges or scores outputs but is not by itself a model-training regime. This does not directly meet the case requirement to learn a decision policy from action rewards.
D:Unsupervised methods discover patterns or structure in data without known target labels. This does not directly meet the case requirement to learn a decision policy from action rewards.
Question 34
A language model predicts masked or next tokens from raw documents during large-scale pretraining. Which approach describes its generated training signal?
- Reinforcement learning
- Human evaluation
- Inference
- Self-supervised pretraining
Explanation
Correct Answer
D:The source text provides the model's own prediction targets. Self-supervised learning derives training targets from the input data itself without manually annotated labels.
Incorrect Answers
A:Reinforcement learning adapts a policy through actions and reward signals over time. This does not directly meet the case requirement to derive targets automatically from unlabeled text.
B:Human review judges or scores outputs but is not by itself a model-training regime. This does not directly meet the case requirement to derive targets automatically from unlabeled text.
C:Inference runs an already trained model on new input without updating its training weights. This does not directly meet the case requirement to derive targets automatically from unlabeled text.
Question 35
A compliance committee rates sample chatbot responses for accuracy but never updates the model. What activity is occurring?
- Self-supervised pretraining
- Human evaluation
- Reinforcement learning
- Unsupervised learning
Explanation
Correct Answer
B:The reviewers are evaluating outputs, not conducting training. Human review judges or scores outputs but is not by itself a model-training regime.
Incorrect Answers
A:Self-supervised learning derives training targets from the input data itself without manually annotated labels. This does not directly meet the case requirement to assess model behavior through human judgment.
C:Reinforcement learning adapts a policy through actions and reward signals over time. This does not directly meet the case requirement to assess model behavior through human judgment.
D:Unsupervised methods discover patterns or structure in data without known target labels. This does not directly meet the case requirement to assess model behavior through human judgment.
Question 36
An already trained classifier processes a new invoice and returns its category. What stage is occurring?
- Supervised learning
- Inference
- Human evaluation
- Reinforcement learning
Explanation
Correct Answer
B:A stored model is being applied rather than fitted on more examples. Inference runs an already trained model on new input without updating its training weights.
Incorrect Answers
A:Supervised learning fits labeled input-output examples to predict known targets. This does not directly meet the case requirement to run a trained model on new records.
C:Human review judges or scores outputs but is not by itself a model-training regime. This does not directly meet the case requirement to run a trained model on new records.
D:Reinforcement learning adapts a policy through actions and reward signals over time. This does not directly meet the case requirement to run a trained model on new records.
Question 37
A hospital uses physician-labeled chest images to train a detection model. Which learning type fits?
- Supervised learning
- Inference
- Unsupervised learning
- Reinforcement learning
Explanation
Correct Answer
A:The labels provide desired outputs during training. Supervised learning fits labeled input-output examples to predict known targets.
Incorrect Answers
B:Inference runs an already trained model on new input without updating its training weights. This does not directly meet the case requirement to learn from medically labeled image examples.
C:Unsupervised methods discover patterns or structure in data without known target labels. This does not directly meet the case requirement to learn from medically labeled image examples.
D:Reinforcement learning adapts a policy through actions and reward signals over time. This does not directly meet the case requirement to learn from medically labeled image examples.
Question 38
A telecom provider seeks unusual customer segments in a database with no segment label. Which regime applies?
- Unsupervised learning
- Supervised learning
- Human evaluation
- Inference
Explanation
Correct Answer
A:The model must discover groupings rather than copy assigned categories. Unsupervised methods discover patterns or structure in data without known target labels.
Incorrect Answers
B:Supervised learning fits labeled input-output examples to predict known targets. This does not directly meet the case requirement to discover unlabeled customer segments.
C:Human review judges or scores outputs but is not by itself a model-training regime. This does not directly meet the case requirement to discover unlabeled customer segments.
D:Inference runs an already trained model on new input without updating its training weights. This does not directly meet the case requirement to discover unlabeled customer segments.
Question 39
A simulated game agent improves decisions using a score after each episode. Which paradigm explains improvement?
- Unsupervised learning
- Supervised learning
- Human evaluation
- Reinforcement learning
Explanation
Correct Answer
D:Rewards signal how well decisions advance the objective. Reinforcement learning adapts a policy through actions and reward signals over time.
Incorrect Answers
A:Unsupervised methods discover patterns or structure in data without known target labels. This does not directly meet the case requirement to optimize behavior through repeated reward feedback.
B:Supervised learning fits labeled input-output examples to predict known targets. This does not directly meet the case requirement to optimize behavior through repeated reward feedback.
C:Human review judges or scores outputs but is not by itself a model-training regime. This does not directly meet the case requirement to optimize behavior through repeated reward feedback.
Question 40
An AI team uses millions of unannotated sentences to learn language representations by reconstructing missing words. Which training signal is used?
- Inference
- Self-supervised pretraining
- Reinforcement learning
- Human evaluation
Explanation
Correct Answer
B:Automatically masked text supplies self-generated objectives. Self-supervised learning derives training targets from the input data itself without manually annotated labels.
Incorrect Answers
A:Inference runs an already trained model on new input without updating its training weights. This does not directly meet the case requirement to learn representations without manual annotation.
C:Reinforcement learning adapts a policy through actions and reward signals over time. This does not directly meet the case requirement to learn representations without manual annotation.
D:Human review judges or scores outputs but is not by itself a model-training regime. This does not directly meet the case requirement to learn representations without manual annotation.
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