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Oracle 1Z0-1127-25 Exam Syllabus Topics:

Topic
Details

Topic 1

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 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 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.

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The Oracle 1Z0-1127-25 certification is one of the top-rated career advancement certifications in the market. This Oracle Cloud Infrastructure 2025 Generative AI Professional (1Z0-1127-25) certification exam has been inspiring candidates since its beginning. Over this long time period, thousands of 1Z0-1127-25 Exam candidates have passed their Oracle Cloud Infrastructure 2025 Generative AI Professional (1Z0-1127-25) certification exam and now they are doing jobs in the world's top brands. You can also be a part of this wonderful community.

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q36-Q41):

NEW QUESTION # 36
What differentiates Semantic search from traditional keyword search?

A. It depends on the number of times keywords appear in the content.
B. It involves understanding the intent and context of the search.
C. It is based on the date and author of the content.
D. It relies solely on matching exact keywords in the content.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Semantic search uses embeddings and NLP to understand the meaning, intent, and context behind a query, rather than just matching exact keywords (as in traditional search). This enables more relevant results, even if exact terms aren't present, making Option C correct. Options A and B describe traditional keyword search mechanics. Option D is unrelated, as metadata like date or author isn't the primary focus of semantic search. Semantic search leverages vector representations for deeper understanding.
OCI 2025 Generative AI documentation likely contrasts semantic and keyword search under search or retrieval sections.

NEW QUESTION # 37
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" are identical in their approach to token selection but differ in their application of penalties to tokens.
B. "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.
C. "Top k" and "Top p" both select from the same set of tokens but use different methods to prioritize them based on frequency.
D. "Top k" considers the sum of probabilities of the top tokens, whereas "Top p" selects from the "Top k" tokens sorted by probability.

Answer: B

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 # 38
When should you use the T-Few fine-tuning method for training a model?

A. For complicated semantic understanding improvement
B. For datasets with hundreds of thousands to millions of samples
C. For models that require their own hosting dedicated AI cluster
D. For datasets with a few thousand samples or less

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few is ideal for smaller datasets (e.g., a few thousand samples) where full fine-tuning risks overfitting and is computationally wasteful-Option C is correct. Option A (semantic understanding) is too vague-dataset size matters more. Option B (dedicated cluster) isn't a condition for T-Few. Option D (large datasets) favors Vanilla fine-tuning. T-Few excels in low-data scenarios.
OCI 2025 Generative AI documentation likely specifies T-Few use cases under fine-tuning guidelines.

NEW QUESTION # 39
Given the following prompts used with a Large Language Model, classify each as employing the Chain-of-Thought, Least-to-Most, or Step-Back prompting technique:

A. "To understand the impact of greenhouse gases on climate change, let's start by defining what greenhouse gases are. Next, we'll explore how they trap heat in the Earth's atmosphere."A. 1: Step-Back, 2: Chain-of-Thought, 3: Least-to-MostB. 1: Least-to-Most, 2: Chain-of-Thought, 3: Step-BackC. 1: Chain-of-Thought, 2: Step-Back, 3: Least-to-MostD. 1: Chain-of-Thought, 2: Least-to-Most, 3: Step-Back
B. "Solve a complex math problem by first identifying the formula needed, and then solve a simpler version of the problem before tackling the full question."
C. "Calculate the total number of wheels needed for 3 cars. Cars have 4 wheels each. Then, use the total number of wheels to determine how many sets of wheels we can buy with $200 if one set (4 wheels) costs $50."

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompt 1: Shows intermediate steps (3 × 4 = 12, then 12 ÷ 4 = 3 sets, $200 ÷ $50 = 4)-Chain-of-Thought.
Prompt 2: Steps back to a simpler problem before the full one-Step-Back.
Prompt 3: OCI 2025 Generative AI documentation likely defines these under prompting strategies.

NEW QUESTION # 40
Which is NOT a typical use case for LangSmith Evaluators?

A. Detecting bias or toxicity
B. Measuring coherence of generated text
C. Evaluating factual accuracy of outputs
D. Aligning code readability

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LangSmith Evaluators assess LLM outputs for qualities like coherence (A), factual accuracy (C), and bias/toxicity (D), aiding development and debugging. Aligning code readability (B) pertains to software engineering, not LLM evaluation, making it the odd one out-Option B is correct as NOT a use case. Options A, C, and D align with LangSmith's focus on text quality and ethics.
OCI 2025 Generative AI documentation likely lists LangSmith Evaluator use cases under evaluation tools.

NEW QUESTION # 41
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Sophia Davis Sophia Davis

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