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Oracle 1Z0-1127-25 Exam Syllabus Topics:

Topic	Details
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	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	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 4	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.

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q23-Q28):

NEW QUESTION # 23
In which scenario is soft prompting especially appropriate compared to other training styles?

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

Answer: C

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 # 24
Which is the main characteristic of greedy decoding in the context of language model word prediction?

A. It picks the most likely word at each step of decoding.
B. It selects words based on a flattened distribution over the vocabulary.
C. It requires a large temperature setting to ensure diverse word selection.
D. It chooses words randomly from the set of less probable candidates.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Greedy decoding selects the word with the highest probability at each step, optimizing locally without lookahead, making Option D correct. Option A (random low-probability) contradicts greedy's deterministic nature. Option B (high temperature) flattens distributions for diversity, not greediness. Option C (flattened distribution) aligns with sampling, not greedy decoding. Greedy is simple but can lack global coherence.
OCI 2025 Generative AI documentation likely describes greedy decoding under decoding strategies.

NEW QUESTION # 25
What is the purpose of Retrievers in LangChain?

A. To retrieve relevant information from knowledge bases
B. To train Large Language Models
C. To combine multiple components into a single pipeline
D. To break down complex tasks into smaller steps

Answer: A

Explanation:
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.

NEW QUESTION # 26
What does the term "hallucination" refer to in the context of Large Language Models (LLMs)?

A. The model's ability to generate imaginative and creative content
B. A technique used to enhance the model's performance on specific tasks
C. The phenomenon where the model generates factually incorrect information or unrelated content as if it were true
D. The process by which the model visualizes and describes images in detail

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LLMs, "hallucination" refers to the generation of plausible-sounding but factually incorrect or irrelevant content, often presented with confidence. This occurs due to the model's reliance on patterns in training data rather than factual grounding, making Option D correct. Option A describes a positive trait, not hallucination. Option B is unrelated, as hallucination isn't a performance-enhancing technique. Option C pertains to multimodal models, not the general definition of hallucination in LLMs.
OCI 2025 Generative AI documentation likely addresses hallucination under model limitations or evaluation metrics.

NEW QUESTION # 27
Given the following code:
PromptTemplate(input_variables=["human_input", "city"], template=template) Which statement is true about PromptTemplate in relation to input_variables?

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

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, PromptTemplate supports any number of input_variables (zero, one, or more), allowing flexible prompt design-Option C is correct. The example shows two, but it's not a requirement. Option A (minimum two) is false-no such limit exists. Option B (single variable) is too restrictive. Option D (no variables) contradicts its purpose-variables are optional but supported. This adaptability aids prompt engineering.
OCI 2025 Generative AI documentation likely covers PromptTemplate under LangChain prompt design.

NEW QUESTION # 28
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