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Oracle 1Z0-1127-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Fundamentals of Large Language Models (LLMs): This section of the exam measures the skills of AI Engineers and Data Scientists in understanding the core principles of large language models. It covers LLM architectures, including transformer-based models, and explains how to design and use prompts effectively. The section also focuses on fine-tuning LLMs for specific tasks and introduces concepts related to code models, multi-modal capabilities, and language agents.
Topic 2
  • Implement RAG Using OCI Generative AI Service: This section tests the knowledge of Knowledge Engineers and Database Specialists in implementing Retrieval-Augmented Generation (RAG) workflows using OCI Generative AI services. It covers integrating LangChain with Oracle Database 23ai, document processing techniques like chunking and embedding, storing indexed chunks in Oracle Database 23ai, performing similarity searches, and generating responses using OCI Generative AI.
Topic 3
  • Using OCI Generative AI Service: This section evaluates the expertise of Cloud AI Specialists and Solution Architects in utilizing Oracle Cloud Infrastructure (OCI) Generative AI services. It includes understanding pre-trained foundational models for chat and embedding, creating dedicated AI clusters for fine-tuning and inference, and deploying model endpoints for real-time inference. The section also explores OCI's security architecture for generative AI and emphasizes responsible AI practices.
Topic 4
  • Using OCI Generative AI RAG Agents Service: This domain measures the skills of Conversational AI Developers and AI Application Architects in creating and managing RAG agents using OCI Generative AI services. It includes building knowledge bases, deploying agents as chatbots, and invoking deployed RAG agents for interactive use cases. The focus is on leveraging generative AI to create intelligent conversational systems.

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q84-Q89):

NEW QUESTION # 84
Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?

  • A. Least-to-Most Prompting
  • B. In-Context Learning
  • C. Step-Back Prompting
  • D. Chain-of-Thought

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Chain-of-Thought (CoT) prompting explicitly instructs an LLM to provide intermediate reasoning steps, enhancing complex task performance-Option B is correct. Option A (Step-Back) reframes problems, not emits steps. Option C (Least-to-Most) breaks tasks into subtasks, not necessarily showing reasoning. Option D (In-Context Learning) uses examples, not reasoning steps. CoT improves transparency and accuracy.
OCI 2025 Generative AI documentation likely covers CoT under advanced prompting techniques.


NEW QUESTION # 85
Which statement accurately reflects the differences between these approaches in terms of the number of parameters modified and the type of data used?

  • A. Fine-tuning modifies all parameters using labeled, task-specific data, whereas Parameter Efficient Fine-Tuning updates a few, new parameters also with labeled, task-specific data.
  • B. Soft Prompting and continuous pretraining are both methods that require no modification to the original parameters of the model.
  • C. Fine-tuning and continuous pretraining both modify all parameters and use labeled, task-specific data.
  • D. Parameter Efficient Fine-Tuning and Soft Prompting modify all parameters of the model using unlabeled data.

Answer: A

Explanation:
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.


NEW QUESTION # 86
How does a presence penalty function in language model generation when using OCI Generative AI service?

  • A. It penalizes all tokens equally, regardless of how often they have appeared.
  • B. It only penalizes tokens that have never appeared in the text before.
  • C. It penalizes a token each time it appears after the first occurrence.
  • D. It applies a penalty only if the token has appeared more than twice.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
A presence penalty in LLMs (including OCI's service) reduces the probability of tokens that have already appeared in the output, applying the penalty each time they reoccur after their first use. This discourages repetition, making Option D correct. Option A is false, as penalties depend on prior appearance, not uniform application. Option B is the opposite-penalizing unused tokens isn't the goal. Option C is incorrect, as the penalty isn't threshold-based (e.g., more than twice) but applied per reoccurrence. This enhances output diversity.
OCI 2025 Generative AI documentation likely details presence penalty under generation parameters.


NEW QUESTION # 87
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. According to the length of the documents
  • C. Based on the presence and frequency of the user-provided keywords
  • D. By the complexity of language used in the documents

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In basic keyword-based search, documents are evaluated by matching user-provided keywords, with relevance often determined by their presence and frequency (e.g., term frequency in TF-IDF). This makes Option C correct. Option A (language complexity) is unrelated to simple keyword search. Option B (multimedia) isn't considered in text-based keyword methods. Option D (length) may influence scoring indirectly but isn't the primary metric. Keyword search prioritizes exact matches.
OCI 2025 Generative AI documentation likely contrasts keyword search with semantic search under retrieval methods.


NEW QUESTION # 88
What is prompt engineering in the context of Large Language Models (LLMs)?

  • A. Iteratively refining the ask to elicit a desired response
  • B. Adding more layers to the neural network
  • C. Adjusting the hyperparameters of the model
  • D. Training the model on a large dataset

Answer: A

Explanation:
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.


NEW QUESTION # 89
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