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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q66-Q71):
NEW QUESTION # 66
Which is a distinguishing feature of "Parameter-Efficient Fine-Tuning (PEFT)" as opposed to classic "Fine-tuning" in Large Language Model training?
- A. PEFT involves only a few or new parameters and uses labeled, task-specific data.
- B. PEFT does not modify any parameters but uses soft prompting with unlabeled data.
- C. PEFT modifies all parameters and uses unlabeled, task-agnostic data.
- D. PEFT modifies all parameters and is typically used when no training data exists.
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
PEFT (e.g., LoRA, T-Few) updates a small subset of parameters (often new ones) using labeled, task-specific data, unlike classic fine-tuning, which updates all parameters-Option A is correct. Option B reverses PEFT's efficiency. Option C (no modification) fits soft prompting, not all PEFT. Option D (all parameters) mimics classic fine-tuning. PEFT reduces resource demands.
OCI 2025 Generative AI documentation likely contrasts PEFT and fine-tuning under customization methods.
NEW QUESTION # 67
In which scenario is soft prompting appropriate compared to other training styles?
- A. When there is a significant amount of labeled, task-specific data available
- B. When the model needs to be adapted to perform well in a domain on which it was not originally trained
- C. When the model requires continued pretraining on unlabeled data
- D. When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training
Answer: D
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Soft prompting adds trainable parameters (soft prompts) to adapt an LLM without retraining its core weights, ideal for low-resource customization without task-specific data. This makes Option C correct. Option A suits fine-tuning. Option B may require more than soft prompting (e.g., domain fine-tuning). Option D describes pretraining, not soft prompting. Soft prompting is efficient for specific adaptations.
OCI 2025 Generative AI documentation likely discusses soft prompting under PEFT methods.
NEW QUESTION # 68
An LLM emits intermediate reasoning steps as part of its responses. Which of the following techniques is being utilized?
- A. In-context Learning
- B. Step-Back Prompting
- C. Chain-of-Thought
- D. Least-to-Most Prompting
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Chain-of-Thought (CoT) prompting encourages an LLM to emit intermediate reasoning steps before providing a final answer, improving performance on complex tasks by mimicking human reasoning. This matches the scenario, making Option D correct. Option A (In-context Learning) involves learning from examples in the prompt, not necessarily reasoning steps. Option B (Step-Back Prompting) involves reframing the problem, not emitting steps. Option C (Least-to-Most Prompting) breaks tasks into subtasks but doesn't focus on intermediate reasoning explicitly. CoT is widely recognized for reasoning tasks.
OCI 2025 Generative AI documentation likely covers Chain-of-Thought under advanced prompting techniques.
NEW QUESTION # 69
What is the function of the Generator in a text generation system?
- A. To rank the information based on its relevance to the user's query
- B. To collect user queries and convert them into database search terms
- C. To generate human-like text using the information retrieved and ranked, along with the user's original query
- D. To store the generated responses for future use
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In a text generation system (e.g., with RAG), the Generator is the component (typically an LLM) that produces coherent, human-like text based on the user's query and any retrieved information (if applicable). It synthesizes the final output, making Option C correct. Option A describes a Retriever's role. Option B pertains to a Ranker. Option D is unrelated, as storage isn't the Generator's function but a separate system task. The Generator's role is critical in transforming inputs into natural language responses.
OCI 2025 Generative AI documentation likely defines the Generator under RAG or text generation workflows.
NEW QUESTION # 70
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?
- A. Support for tokenizing longer sentences
- B. Emphasis on syntactic clustering of word embeddings
- C. Improved retrievals for Retrieval Augmented Generation (RAG) systems
- D. Capacity to translate text in over 100 languages
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Cohere Embed v3, as an advanced embedding model, is designed with improved performance for retrieval tasks, enhancing RAG systems by generating more accurate, contextually rich embeddings. This makes Option B correct. Option A (tokenization) isn't a primary focus-embedding quality is. Option C (syntactic clustering) is too narrow-semantics drives improvement. Option D (translation) isn't an embedding model's role. v3 boosts RAG effectiveness.
OCI 2025 Generative AI documentation likely highlights Embed v3 under supported models or RAG enhancements.
NEW QUESTION # 71
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