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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 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	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 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.

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q88-Q93):

NEW QUESTION # 88
Which is a key characteristic of Large Language Models (LLMs) without Retrieval Augmented Generation (RAG)?

A. They use vector databases exclusively to produce answers.
B. They rely on internal knowledge learned during pretraining on a large text corpus.
C. They cannot generate responses without fine-tuning.
D. They always use an external database for generating responses.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LLMs without Retrieval Augmented Generation (RAG) depend solely on the knowledge encoded in their parameters during pretraining on a large, general text corpus. They generate responses basedon this internal knowledge without accessing external data at inference time, making Option B correct. Option A is false, as external databases are a feature of RAG, not standalone LLMs. Option C is incorrect, as LLMs can generate responses without fine-tuning via prompting or in-context learning. Option D is wrong, as vector databases are used in RAG or similar systems, not in basic LLMs. This reliance on pretraining distinguishes non-RAG LLMs from those augmented with real-time retrieval.
OCI 2025 Generative AI documentation likely contrasts RAG and non-RAG LLMs under model architecture or response generation sections.

NEW QUESTION # 89
What is prompt engineering in the context of Large Language Models (LLMs)?

A. Adjusting the hyperparameters of the model
B. Adding more layers to the neural network
C. Training the model on a large dataset
D. Iteratively refining the ask to elicit a desired response

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompt engineering involves crafting and refining input prompts to guide an LLM to produce desired outputs without altering its internal structure or parameters. It's an iterative process that leverages the model's pre-trained knowledge, making Option A correct. Option B is unrelated, as adding layers pertains to model architecture design, not prompting. Option C refers to hyperparameter tuning (e.g., temperature), not prompt engineering. Option D describes pretraining or fine-tuning, not prompt engineering.
OCI 2025 Generative AI documentation likely covers prompt engineering in sections on model interaction or inference.

NEW QUESTION # 90
Which is a distinctive feature of GPUs in Dedicated AI Clusters used for generative AI tasks?

A. GPUs are shared with other customers to maximize resource utilization.
B. Each customer's GPUs are connected via a public Internet network for ease of access.
C. The GPUs allocated for a customer's generative AI tasks are isolated from other GPUs.
D. GPUs are used exclusively for storing large datasets, not for computation.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In Dedicated AI Clusters (e.g., in OCI), GPUs are allocated exclusively to a customer for their generative AI tasks, ensuring isolation for security, performance, and privacy. This makes Option B correct. Option A describes shared resources, not dedicated clusters. Option C is false, as GPUs are for computation, not storage. Option D is incorrect, as public Internet connections would compromise security and efficiency.
OCI 2025 Generative AI documentation likely details GPU isolation under DedicatedAI Clusters.

NEW QUESTION # 91
How does a presence penalty function in language model generation?

A. It applies a penalty only if the token has appeared more than twice.
B. It penalizes a token each time it appears after the first occurrence.
C. It penalizes all tokens equally, regardless of how often they have appeared.
D. It penalizes only tokens that have never appeared in the text before.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
A presence penalty reduces the probability of tokens that have already appeared in the output, applying the penalty each time they reoccur after their first use, to discourage repetition. This makes Option D correct. Option A (equal penalties) ignores prior appearance. Option B is the opposite-penalizing unused tokens isn't the intent. Option C (more than twice) adds an arbitrary threshold not typically used. Presence penalty enhances output variety.OCI 2025 Generative AI documentation likely details presence penalty under generation control parameters.

NEW QUESTION # 92
What issue might arise from using small datasets with the Vanilla fine-tuning method in the OCI Generative AI service?

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

Answer: A

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 # 93
......

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Oracle 1Z0-1127-25 Reliable Braindumps Questions & 1Z0-1127-25 Test Sample Questions

1Z0-1127-25 Test Sample Questions & Reliable 1Z0-1127-25 Exam Sims

Oracle 1Z0-1127-25 Exam Syllabus Topics:

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q88-Q93):

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