1z0-1127-25 Exam Dumps : Oracle Cloud Infrastructure 2025 Generative AI Professional

Comprehensive and Detailed In-Depth Explanation:

Fine-tuning typically involves updating all parameters of an LLM using labeled, task-specific data to adapt it to a specific task, which is computationally expensive. Parameter Efficient Fine-Tuning (PEFT), such as methods like LoRA (Low-Rank Adaptation), updates only a small subset of parameters (often newly added ones) while still using labeled, task-specific data, making it more efficient. Option C correctly captures this distinction. Option A is wrong because continuous pretraining uses unlabeled data and isn’t task-specific. Option B is incorrect as PEFT and Soft Prompting don’t modify all parameters, and Soft Prompting typically uses labeled examples indirectly. Option D is inaccurate because continuous pretraining modifies parameters, while SoftPrompting doesn’t.

OCI 2025 Generative AI documentation likely discusses Fine-tuning and PEFT under model customization techniques.

Comprehensive and Detailed In-Depth Explanation:

StreamlitChatMessageHistory integrates with Streamlit’s session state to store chat history, tied to a specific key (Option A, true). It’s not persisted beyond the session (Option B, true) and isn’t shared across users (Option C, true), as Streamlit sessions are user-specific. However, it’s designed specifically for Streamlit apps, not universally for any LLM application (e.g., non-Streamlit contexts), making Option D NOT true.

OCI 2025 Generative AI documentation likely references Streamlit integration under LangChain memory options.

Comprehensive and Detailed In-Depth Explanation:

In NLP (e.g., transformers), encoders convert input text into a vector representation (encoding meaning), while decoders generate text from such vectors (e.g., in translation or generation). This makes Option C correct. Option A is false—decoders generate text. Option B reverses roles—encoders don’t predict next words, and decoders don’t encode. Option D oversimplifies—encoders handle text, not just numbers. This is the foundation of seq2seq models.

OCI 2025 Generative AI documentation likely explains encoder-decoder roles under model architecture.