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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q12-Q17):

NEW QUESTION # 12
How does the Retrieval-Augmented Generation (RAG) Token technique differ from RAG Sequence when generating a model's response?

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

Answer: D

NEW QUESTION # 13
Which is NOT a category of pertained foundational models available in the OCI Generative AI service?

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

Answer: D

NEW QUESTION # 14
Given the following prompts used with a Large Language Model, classify each as employing the Chain-of- Thought, Least-to-most, or Step-Back prompting technique.
L Calculate the total number of wheels needed for 3 cars. Cars have 4 wheels each. Then, use the total number of wheels to determine how many sets of wheels we can buy with $200 if one set (4 wheels) costs $50.
2. Solve a complex math problem by first identifying the formula needed, and then solve a simpler version of the problem before tackling the full question.
3. To understand the impact of greenhouse gases on climate change, let's start by defining what greenhouse gases are. Next, well explore how they trap heat in the Earths atmosphere.

A. 1:Step-Back, 2:Chain-of-Thought, 3:Least-to-most
B. 1:Least-to-most, 2 Chain-of-Thought, 3:Step-Back
C. 1:Chain-of-Thought ,2:Step-Back, 3:Least-to most
D. 1:Chain-of-throught, 2: Least-to-most, 3:Step-Back

Answer: D

Explanation:
Chain-of-Thought: The first prompt calculates the total number of wheels and then uses that information to determine how many sets of wheels can be bought. This sequential reasoning process aligns with the Chain-of-Thought technique.
Least-to-most: The second prompt solves a complex problem by first identifying the needed formula and then solving a simpler version before tackling the full question. This incremental approach matches the Least-to-most technique.
Step-Back: The third prompt starts by defining greenhouse gases and then explores their impact on climate change, taking a step back to establish foundational knowledge before addressing the main question.
Reference
Research articles on prompting techniques for language models
Documentation on effective use of prompting strategies

NEW QUESTION # 15
How are documents usually evaluated in the simplest form of keyword-based search?

A. Based on the number of images and videos contained in the documents
B. By the complexity of language used in the documents
C. Based on the presence and frequency of the user-provided keywords
D. According to the length of the documents

Answer: C

Explanation:
In the simplest form of keyword-based search, documents are evaluated based on keyword matching and term frequency. This approach does not account for context, semantics, or the meaning behind the words, but rather focuses on:
Presence of Keywords - If a document contains the search term, it is considered relevant.
Term Frequency (TF) - The more a keyword appears in a document, the higher the ranking in basic search algorithms.
Inverse Document Frequency (IDF) - Words that are common across many documents (e.g., "the," "is") are given less weight, while rare words are prioritized.
Boolean Matching - Some basic search engines support logical operators like AND, OR, and NOT to refine keyword searches.
Exact Match vs. Partial Match - Some systems prioritize exact keyword matches, while others allow partial or fuzzy matches.
🔹 Oracle Generative AI Reference:
Oracle has implemented semantic search and advanced AI-driven document search techniques in its cloud solutions, but traditional keyword-based search still forms the foundation of many enterprise search mechanisms.

NEW QUESTION # 16
Which is a distinguishing feature of "Parameter-Efficient Fine-tuning (PEFT)" as opposed to classic Tine- tuning" in Large Language Model training?

A. PEFT parameters and b typically used when no training data exists.
B. PEFT involves only a few or new parameters and uses labeled, task-specific data.
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: B

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 # 17
......

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Sam Shaw Sam Shaw

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