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

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.

Comprehensive and Detailed In-Depth Explanation:

Retrievers in LangChain fetch relevant information (e.g., documents, embeddings) from external knowledge bases (like vector stores) to provide context for LLM responses, especially in RAG setups. This makes Option B correct. Option A (training) is unrelated—Retrievers operate at inference. Option C (task breakdown) pertains to prompting techniques, not retrieval. Option D (pipeline combination) describes chains, not Retrievers specifically. Retrievers enhance context awareness.

OCI 2025 Generative AI documentation likely defines Retrievers under LangChain components.

Comprehensive and Detailed In-Depth Explanation:

A “model endpoint” in OCI’s inference workflow is an API or interface where users send requests and receive responses from a deployed model—Option B is correct. Option A (weight updates) occurs during fine-tuning, not inference. Option C (metrics) is for evaluation, not endpoints. Option D (training data) relates to storage, not inference. Endpoints enable real-time interaction.

OCI 2025 Generative AI documentation likely describes endpoints under inference deployment.

Comprehensive and Detailed In-Depth Explanation:

Prompt engineering involves crafting and refining input prompts to guide an LLM to produce desired outputs without altering its internal structure or parameters. It’s an iterative process that leverages the model’s pre-trained knowledge, making Option A correct. Option B is unrelated, as adding layers pertains to model architecture design, not prompting. Option C refers to hyperparameter tuning (e.g., temperature), not prompt engineering. Option D describes pretraining or fine-tuning, not prompt engineering.

OCI 2025 Generative AI documentation likely covers prompt engineering in sections on model interaction or inference.

Comprehensive and Detailed In-Depth Explanation:

In OCI, fine-tuned models are stored in Object Storage, encrypted by default, ensuring privacy and security per cloud best practices—Option B is correct. Option A (shared) violates privacy. Option C (unencrypted) contradicts security standards. Option D (Key Management) stores keys, not models. Encryption protects customer data.

OCI 2025 Generative AI documentation likely details storage security under fine-tuning workflows.

Comprehensive and Detailed In-Depth Explanation:

Fine-tuning updates all model parameters on task-specific data, incurring high computational costs, while PEFT (e.g., LoRA, T-Few) updates a small subset of parameters, reducing resource demands and often requiring less data, making Option A correct. Option B is false—PEFT doesn’t replace architecture. Option C is incorrect, as PEFT isn’t trained from scratch and is less intensive. Option D is wrong, as both involve modification, but PEFT is more efficient. This distinction is critical for practical LLM customization.

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

Here is the next batch of 10 questions (31–40) from your list, formatted as requested with detailed explanations. The answers are based on widely accepted principles in generative AI and Large Language Models (LLMs), aligned with what is likely reflected in the Oracle Cloud Infrastructure (OCI) 2025 Generative AI documentation. Typographical errors have been corrected for clarity.

Comprehensive and Detailed In-Depth Explanation:

A stop sequence in text generation (e.g., a period) instructs the model to halt generation once it encounters that token, regardless of the token limit. If set to a period, the model stops after the first sentence ends, making Option D correct. Option A is false, as stop sequences are enforced. Option B contradicts the stop sequence’s purpose. Option C is incorrect, as it stops at the sentence level, not paragraph.

OCI 2025 Generative AI documentation likely explains stop sequences under text generation parameters.

Comprehensive and Detailed In-Depth Explanation:

Few-shot prompting involves providing a few examples in the prompt to guide the LLM’s behavior, leveraging its in-context learning ability without requiring retraining or additional computational resources. This makes Option C correct. Option A is false, as few-shot prompting doesn’t expand the dataset. Option B overstates the case, as inference still requires resources. Option D is incorrect, as latency isn’t significantly affected by few-shot prompting.

OCI 2025 Generative AI documentation likely highlights few-shot prompting in sections on efficient customization.