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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q44-Q49):

NEW QUESTION # 44
How do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language?

A. Dot Product measures the magnitude and direction vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude.
B. Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.
C. Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance.
D. Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.

Answer: A

Explanation:
Dot Product and Cosine Distance are both metrics used to compare text embeddings, but they operate differently:
Dot Product: Measures the magnitude and direction of the vectors. It takes into account both the size (magnitude) and the angle (direction) between the vectors. This can result in higher similarity scores for longer vectors, even if they point in similar directions.
Cosine Distance: Focuses on the orientation of the vectors regardless of their magnitude. It measures the cosine of the angle between two vectors, which normalizes the vectors to unit length. This makes it a measure of the angle (or orientation) between the vectors, providing a similarity score that is independent of the vector lengths.
Reference
Research papers on text embedding comparison metrics
Technical documentation on vector similarity measures

NEW QUESTION # 45
In LangChain, which retriever search type is used to balance between relevancy and diversity?

A. top k
B. similarity_score_threshold
C. similarity
D. mmr

Answer: C

NEW QUESTION # 46
Which is a key advantage of usingT-Few over Vanilla fine-tuning in the OCI Generative AI service?

A. Enhanced generalization to unseen data
B. Increased model interpretability
C. Reduced model complexity
D. Foster training time and lower cost

Answer: D

NEW QUESTION # 47
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

A. Ranker
B. Encoder-decoder
C. Retriever
D. Generator

Answer: A

NEW QUESTION # 48
How does the Retrieval-Augmented Generation (RAG) Token technique differ from RAG Sequence when generating a model's response?

A. RAG Token does not use document retrieval but generates responses based on pre-existing knowledge only.
B. Unlike RAG Sequence, RAG Token generates the entire response at once without considering individual parts.
C. RAG Token retrieves relevant documents for each part of the response and constructs the answer incrementally.
D. RAG Token retrieves documents oar/at the beginning of the response generation and uses those for the entire content

Answer: D

NEW QUESTION # 49
......

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