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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q38-Q43):

NEW QUESTION # 38
Which is NOT a built-in memory type in LangChain?

A. Conversation ImgeMemory
B. Conversation Buffer Memory
C. Conversation Summary Memory
D. Conversation Token Buffer Memory

Answer: A

NEW QUESTION # 39
Which is NOT a category of pertained foundational models available in the OCI Generative AI service?

A. Translation models
B. Summarization models
C. Embedding models
D. Generation models

Answer: A

Explanation:
In the OCI Generative AI service, the categories of pre-trained foundational models available include Summarization models, Generation models, and Embedding models. However, Translation models are not listed as a category of pre-trained foundational models available in OCI Generative AI service. The service focuses on providing models that support text generation, summarization, and embedding tasks.
Reference
OCI Generative AI service documentation
Listings and descriptions of pre-trained foundational models in OCI

NEW QUESTION # 40
ow do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language?

A. Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.
B. Dot Product measures the magnitude and direction vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude.
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: B

NEW QUESTION # 41
Which is a distinguishing feature of "Parameter-Efficient Fine-tuning (PEFT)" as opposed to classic Tine- tuning" in Large Language Model training?

A. PEFT involves only a few or new parameters and uses labeled, task-specific data.
B. PEFT parameters and b typically used when no training data exists.
C. PEFT does not modify any parameters but uses soft prompting with unlabeled data. PEFT modifies
D. PEFT modifies all parameters and uses unlabeled, task-agnostic data.

Answer: A

Explanation:
Parameter-Efficient Fine-Tuning (PEFT) is a technique used in large language model training that focuses on adjusting only a subset of the model's parameters rather than all of them. This approach involves using labeled, task-specific data to fine-tune new or a limited number of parameters. PEFT is designed to be more efficient than classic fine-tuning, which typically adjusts all the parameters of the model. By only updating a small fraction of the model's parameters, PEFT reduces the computational resources and time required for fine-tuning while still achieving significant performance improvements on specific tasks.
Reference
Research papers on Parameter-Efficient Fine-Tuning (PEFT)
Technical documentation on fine-tuning techniques for large language models

NEW QUESTION # 42
How does the integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models(LLMS) fundamentally alter their responses?

A. It enables them to bypass the need for pretraining on large text corpora.
B. It limits their ability to understand and generate natural language.
C. It transforms their architecture from a neural network to a traditional database system.
D. It shifts the basis of their responses from pretrained internal knowledge to real-time data retrieval.

Answer: D

NEW QUESTION # 43
......

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q38-Q43):

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