ORACLE 1Z0-1127-25 LATEST TEST FORMAT & RELIABLE 1Z0-1127-25 EXAM QUESTIONS

Oracle 1Z0-1127-25 Latest Test Format & Reliable 1Z0-1127-25 Exam Questions

Oracle 1Z0-1127-25 Latest Test Format & Reliable 1Z0-1127-25 Exam Questions

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Oracle 1Z0-1127-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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.
Topic 2
  • 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 3
  • 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 4
  • 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.

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q49-Q54):

NEW QUESTION # 49
In which scenario is soft prompting appropriate compared to other training styles?

  • A. When the model requires continued pretraining on unlabeled data
  • B. When there is a significant amount of labeled, task-specific data available
  • C. When the model needs to be adapted to perform well in a domain on which it was not originally trained
  • 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 # 50
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

  • A. "Top p" determines the maximum number of tokens per response.
  • B. "Top p" assigns penalties to frequently occurring tokens.
  • C. "Top p" selects tokens from the "Top k" tokens sorted by probability.
  • D. "Top p" limits token selection based on the sum of their probabilities.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
"Top p" (nucleus sampling) selects tokens whose cumulative probability exceeds a threshold (p), limiting the pool to the smallest set meeting this sum, enhancing diversity-Option C is correct. Option A confuses it with "Top k." Option B (penalties) is unrelated. Option D (max tokens) is a different parameter. Top p balances randomness and coherence.
OCI 2025 Generative AI documentation likely explains "Top p" under sampling methods.
Here is the next batch of 10 questions (81-90) 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.


NEW QUESTION # 51
Which statement describes the difference between "Top k" and "Top p" in selecting the next token in the OCI Generative AI Generation models?

  • A. "Top k" and "Top p" both select from the same set of tokens but use different methods to prioritize them based on frequency.
  • B. "Top k" and "Top p" are identical in their approach to token selection but differ in their application of penalties to tokens.
  • C. "Top k" considers the sum of probabilities of the top tokens, whereas "Top p" selects from the "Top k" tokens sorted by probability.
  • D. "Top k" selects the next token based on its position in the list of probable tokens, whereas "Top p" selects based on the cumulative probability of the top tokens.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
"Top k" sampling selects from the k most probable tokens, based on their ranked position, while "Top p" (nucleus sampling) selects from tokens whose cumulative probability exceeds p, focusing on a dynamic probability mass-Option B is correct. Option A is false-they differ in selection, not penalties. Option C reverses definitions. Option D (frequency) is incorrect-both use probability, not frequency. This distinction affects diversity.
OCI 2025 Generative AI documentation likely contrasts Top k and Top p under sampling methods.


NEW QUESTION # 52
What issue might arise from using small datasets with the Vanilla fine-tuning method in the OCI Generative AI service?

  • A. Model Drift
  • B. Data Leakage
  • C. Underfitting
  • D. Overfitting

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Vanilla fine-tuning updates all model parameters, and with small datasets, it can overfit-memorizing the data rather than generalizing-leading to poor performance on unseen data. Option A is correct. Option B (underfitting) is unlikely with full updates-overfitting is the risk. Option C (data leakage) depends on data handling, not size. Option D (model drift) relates to deployment shifts, not training. Small datasets exacerbate overfitting in Vanilla fine-tuning.
OCI 2025 Generative AI documentation likely warns of overfitting under Vanilla fine-tuning limitations.


NEW QUESTION # 53
An LLM emits intermediate reasoning steps as part of its responses. Which of the following techniques is being utilized?

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

Answer: A

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 # 54
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