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Oracle 1Z0-1127-25 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 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 (Q78-Q83):

NEW QUESTION # 78
In which scenario is soft prompting especially appropriate compared to other training styles?

A. When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training.
B. When the model needs to be adapted to perform well in a different domain it was not originally trained on.
C. When the model requires continued pre-training on unlabeled data.
D. When there is a significant amount of labeled, task-specific data available.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Soft prompting (e.g., prompt tuning) involves adding trainable parameters (soft prompts) to an LLM's input while keeping the model's weights frozen, adapting it to tasks without task-specific retraining. This is efficient when fine-tuning or large datasets aren't feasible, making Option C correct. Option A suits full fine-tuning, not soft prompting, which avoids extensive labeled data needs. Option B could apply, but domain adaptation often requires more than soft prompting (e.g., fine-tuning). Option D describes continued pretraining, not soft prompting. Soft prompting excels in low-resource customization.
OCI 2025 Generative AI documentation likely discusses soft prompting under parameter-efficient methods.

NEW QUESTION # 79
What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?

A. The improvement in accuracy achieved by the model during training on the user-uploaded dataset
B. The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model
C. The percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation
D. The level of incorrectness in the model's predictions, with lower values indicating better performance

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Loss measures the discrepancy between a model's predictions and true values, with lower values indicating better fit-Option D is correct. Option A (accuracy difference) isn't loss-it's a derived metric. Option B (error percentage) is closer to error rate, not loss. Option C (accuracy improvement) is a training outcome, not loss's definition. Loss is a fundamental training signal.
OCI 2025 Generative AI documentation likely defines loss under fine-tuning metrics.

NEW QUESTION # 80
When does a chain typically interact with memory in a run within the LangChain framework?

A. Before user input and after chain execution
B. Only after the output has been generated
C. Continuously throughout the entire chain execution process
D. After user input but before chain execution, and again after core logic but before output

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, a chain interacts with memory after receiving user input (to retrieve context) but before execution (to inform processing), and again after core logic (to update memory) but before output (to maintain state). This makes Option C correct. Option A misses pre-execution context. Option B misplaces timing. Option D overstates-interaction is at specific stages, not continuous. Memory ensures context-aware responses.
OCI 2025 Generative AI documentation likely details memory interaction under LangChain chain execution.

NEW QUESTION # 81
Which statement is true about string prompt templates and their capability regarding variables?

A. They support any number of variables, including the possibility of having none.
B. They require a minimum of two variables to function properly.
C. They are unable to use any variables.
D. They can only support a single variable at a time.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
String prompt templates (e.g., in LangChain) are flexible frameworks that can include zero, one, or multiple variables (placeholders) to customize prompts dynamically. They can be static (no variables) or complex (many variables), making Option C correct. Option A is too restrictive. Option B is false-variables are a core feature. Option D is incorrect, as no minimum is required. This flexibility aids prompt engineering.
OCI 2025 Generative AI documentation likely covers prompt templates under LangChain or prompt design.

NEW QUESTION # 82
What happens if a period (.) is used as a stop sequence in text generation?

A. The model stops generating text after it reaches the end of the current paragraph.
B. The model generates additional sentences to complete the paragraph.
C. The model stops generating text after it reaches the end of the first sentence, even if the token limit is much higher.
D. The model ignores periods and continues generating text until it reaches the token limit.

Answer: C

Explanation:
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.

NEW QUESTION # 83
......

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