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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

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 3

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 4

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.

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q72-Q77):

NEW QUESTION # 72
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. "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."
B. "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."
C. "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

Answer: C

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 # 73
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours are required for fine-tuning if the cluster is active for 10 days?

A. 744 unit hours
B. 20 unit hours
C. 240 unit hours
D. 480 unit hours

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In OCI, a dedicated AI cluster's usage is typically measured in unit hours, where 1 unit hour = 1 hour of cluster activity. For 10 days, assuming 24 hours per day, the calculation is: 10 days × 24 hours/day = 240 hours. Thus, Option B (240 unit hours) is correct. Option A (480) might assume multiple clusters or higher rates, but the question specifies one cluster. Option C (744) approximates a month (31 days), not 10 days. Option D (20) is arbitrarily low.
OCI 2025 Generative AI documentation likely specifies unit hour calculations under Dedicated AI Cluster pricing.

NEW QUESTION # 74
Given the following code:
chain = prompt | llm
Which statement is true about LangChain Expression Language (LCEL)?

A. LCEL is a legacy method for creating chains in LangChain.
B. LCEL is a declarative and preferred way to compose chains together.
C. LCEL is a programming language used to write documentation for LangChain.
D. LCEL is an older Python library for building Large Language Models.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LangChain Expression Language (LCEL) is a declarative syntax (e.g., using | to pipe components) for composing chains in LangChain, combining prompts, LLMs, and other elements efficiently-Option C is correct. Option A is false-LCEL isn't for documentation. Option B is incorrect-it's current, not legacy; traditional Python classes are older. Option D is wrong-LCEL is part of LangChain, not a standalone LLM library. LCEL simplifies chain design.
OCI 2025 Generative AI documentation likely highlights LCEL under LangChain chaincomposition.

NEW QUESTION # 75
How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?

A. Increasing the temperature flattens the distribution, allowing for more varied word choices.
B. Decreasing the temperature broadens the distribution, making less likely words more probable.
C. Increasing the temperature removes the impact of the most likely word.
D. Temperature has no effect on probability distribution; it only changes the speed of decoding.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Temperature adjusts the softmax distribution in decoding. Increasing it (e.g., to 2.0) flattens the curve, giving lower-probability words a better chance, thus increasing diversity-Option C is correct. Option A exaggerates-top words still have impact, just less dominance. Option B is backwards-decreasing temperature sharpens, not broadens. Option D is false-temperature directly alters distribution, not speed. This controls output creativity.
OCI 2025 Generative AI documentation likely reiterates temperature effects under decoding parameters.

NEW QUESTION # 76
What does a higher number assigned to a token signify in the "Show Likelihoods" feature of the language model token generation?

A. The token will be the only one considered in the next generation step.
B. The token is more likely to follow the current token.
C. The token is unrelated to the current token and will not be used.
D. The token is less likely to follow the current token.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In "Show Likelihoods," a higher number (probability score) indicates a token's greater likelihood of following the current token, reflecting the model's prediction confidence-Option B is correct. Option A (less likely) is the opposite. Option C (unrelated) misinterprets-likelihood ties tokens contextually. Option D (only one) assumes greedy decoding, not the feature's purpose. This helps users understand model preferences.
OCI 2025 Generative AI documentation likely explains "Show Likelihoods" under token generation insights.

NEW QUESTION # 77
......

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