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Question 20
A product team wants to organize thousands of untagged customer comments into themes before analysts name them. What learning task is suitable?
- Unsupervised clustering
- Information retrieval
- Anomaly detection
- Regression forecasting
Explanation
Correct Answer
A:The team has no gold category labels, so structure must be inferred. Clustering groups similar observations without preassigned outcome labels.
Incorrect Answers
B:Retrieval finds existing relevant records or passages rather than creating a new source document. This does not directly meet the case requirement to group text by latent similarity before labeling.
C:Anomaly detection flags unusual patterns or outliers compared with expected behavior. This does not directly meet the case requirement to group text by latent similarity before labeling.
D:Regression estimates a continuous numerical quantity such as demand or spending. This does not directly meet the case requirement to group text by latent similarity before labeling.
Question 21
A company wants one broad pretrained starting point that can be adapted to multiple downstream applications. What kind of model is described?
- Embedding model
- Rule-based keyword matcher
- Large language model (LLM)
- Foundation model
Explanation
Correct Answer
D:Broad pretraining creates transferable capabilities across tasks. A foundation model is broadly pretrained and adapted to numerous downstream tasks.
Incorrect Answers
A:An embedding model converts inputs into vectors suited to semantic similarity and retrieval. This does not directly meet the case requirement to reuse a general pretrained model for several downstream tasks.
B:Keyword matching finds specified words without a learned semantic representation or text generation. This does not directly meet the case requirement to reuse a general pretrained model for several downstream tasks.
C:An LLM specializes in interpreting and generating language and other tokenized sequences. This does not directly meet the case requirement to reuse a general pretrained model for several downstream tasks.
Question 22
A chatbot needs to draft and edit policy language in fluent English. Which model family directly addresses the language task?
- Diffusion image model
- Large language model (LLM)
- Rule-based keyword matcher
- Embedding model
Explanation
Correct Answer
B:Language generation and comprehension are core LLM capabilities. An LLM specializes in interpreting and generating language and other tokenized sequences.
Incorrect Answers
A:Diffusion models are widely used to synthesize visual content through iterative denoising. This does not directly meet the case requirement to produce fluent language responses to instructions.
C:Keyword matching finds specified words without a learned semantic representation or text generation. This does not directly meet the case requirement to produce fluent language responses to instructions.
D:An embedding model converts inputs into vectors suited to semantic similarity and retrieval. This does not directly meet the case requirement to produce fluent language responses to instructions.
Question 23
An accessibility app must interpret a diagram and a user's spoken question together. Which model capability matters?
- Rule-based keyword matcher
- Multimodal model
- Large language model (LLM)
- Diffusion image model
Explanation
Correct Answer
B:Joint understanding requires processing more than text alone. A multimodal model handles more than one modality, such as text combined with images or audio.
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 reason jointly over visual and spoken inputs.
C:An LLM specializes in interpreting and generating language and other tokenized sequences. This does not directly meet the case requirement to reason jointly over visual and spoken inputs.
D:Diffusion models are widely used to synthesize visual content through iterative denoising. This does not directly meet the case requirement to reason jointly over visual and spoken inputs.
Question 24
An image team describes gradually refining noise into a coherent picture. Which common generative modeling family follows that pattern?
- Multimodal model
- Foundation model
- Rule-based keyword matcher
- Diffusion image model
Explanation
Correct Answer
D:Iterative denoising is characteristic of diffusion-based generation. Diffusion models are widely used to synthesize visual content through iterative denoising.
Incorrect Answers
A: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 synthesize an image through denoising steps.
B:A foundation model is broadly pretrained and adapted to numerous downstream tasks. This does not directly meet the case requirement to synthesize an image through denoising steps.
C:Keyword matching finds specified words without a learned semantic representation or text generation. This does not directly meet the case requirement to synthesize an image through denoising steps.
Question 25
An enterprise search application stores vector representations of document passages for semantic matching. Which type of model provides the vectors?
- Multimodal model
- Embedding model
- Large language model (LLM)
- Foundation model
Explanation
Correct Answer
B:Embeddings represent meaning numerically for nearest-neighbor retrieval. An embedding model converts inputs into vectors suited to semantic similarity and retrieval.
Incorrect Answers
A: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 encode passages as comparable semantic vectors.
C:An LLM specializes in interpreting and generating language and other tokenized sequences. This does not directly meet the case requirement to encode passages as comparable semantic vectors.
D:A foundation model is broadly pretrained and adapted to numerous downstream tasks. This does not directly meet the case requirement to encode passages as comparable semantic vectors.
Question 26
A simple audit search flags files containing a literal prohibited term, without semantic interpretation. What technique is sufficient?
- Diffusion image model
- Embedding model
- Rule-based keyword matcher
- Foundation model
Explanation
Correct Answer
C:Exact keyword matching can detect literal strings without a foundation model. Keyword matching finds specified words without a learned semantic representation or text generation.
Incorrect Answers
A:Diffusion models are widely used to synthesize visual content through iterative denoising. This does not directly meet the case requirement to detect exact words rather than infer intent.
B:An embedding model converts inputs into vectors suited to semantic similarity and retrieval. This does not directly meet the case requirement to detect exact words rather than infer intent.
D:A foundation model is broadly pretrained and adapted to numerous downstream tasks. This does not directly meet the case requirement to detect exact words rather than infer intent.
Question 27
A team fine-tunes a general pretrained base to support customer support and document categorization. What is the underlying base called?
- Foundation model
- Rule-based keyword matcher
- Diffusion image model
- Embedding model
Explanation
Correct Answer
A:A widely adaptable pretrained model supports task-specific customization. A foundation model is broadly pretrained and adapted to numerous downstream tasks.
Incorrect Answers
B:Keyword matching finds specified words without a learned semantic representation or text generation. This does not directly meet the case requirement to start with a versatile pretrained model.
C:Diffusion models are widely used to synthesize visual content through iterative denoising. This does not directly meet the case requirement to start with a versatile pretrained model.
D:An embedding model converts inputs into vectors suited to semantic similarity and retrieval. This does not directly meet the case requirement to start with a versatile pretrained model.
Question 28
A customer sends both a photo of a damaged item and a text explanation, and expects a combined response. Which capability is essential?
- Large language model (LLM)
- Diffusion image model
- Multimodal model
- Foundation model
Explanation
Correct Answer
C:Combining image and language signals requires multimodal processing. A multimodal model handles more than one modality, such as text combined with images or audio.
Incorrect Answers
A:An LLM specializes in interpreting and generating language and other tokenized sequences. This does not directly meet the case requirement to interpret text and image together.
B:Diffusion models are widely used to synthesize visual content through iterative denoising. This does not directly meet the case requirement to interpret text and image together.
D:A foundation model is broadly pretrained and adapted to numerous downstream tasks. This does not directly meet the case requirement to interpret text and image together.
Question 29
A search engineer observes semantically related sentences close together in vector space. Which learned representation is being used?
- Foundation model
- Rule-based keyword matcher
- Embedding model
- Diffusion image model
Explanation
Correct Answer
C:Vector embeddings capture semantic similarity rather than exact wording. An embedding model converts inputs into vectors suited to semantic similarity and retrieval.
Incorrect Answers
A:A foundation model is broadly pretrained and adapted to numerous downstream tasks. This does not directly meet the case requirement to compare meaning using vector distance.
B:Keyword matching finds specified words without a learned semantic representation or text generation. This does not directly meet the case requirement to compare meaning using vector distance.
D:Diffusion models are widely used to synthesize visual content through iterative denoising. This does not directly meet the case requirement to compare meaning using vector distance.
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.
Generative AI Leader Practice Tests
