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[General] Latest Oracle 1Z0-1127-25 Mock Test & 1Z0-1127-25 Pdf Braindumps

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【General】 Latest Oracle 1Z0-1127-25 Mock Test & 1Z0-1127-25 Pdf Braindumps

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Oracle 1Z0-1127-25 Exam Syllabus Topics:
TopicDetails
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
  • 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 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.

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q48-Q53):NEW QUESTION # 48
Which is NOT a typical use case for LangSmith Evaluators?
  • A. Aligning code readability
  • B. Detecting bias or toxicity
  • C. Measuring coherence of generated text
  • D. Evaluating factual accuracy of outputs
Answer: A
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 # 49
What happens if a period (.) is used as a stop sequence in text generation?
  • A. The model ignores periods and continues generating text until it reaches the token limit.
  • B. The model stops generating text after it reaches the end of the first sentence, even if the token limit is much higher.
  • C. The model stops generating text after it reaches the end of the current paragraph.
  • D. The model generates additional sentences to complete the paragraph.
Answer: B
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 # 50
What does "k-shot prompting" refer to when using Large Language Models for task-specific applications?
  • A. Limiting the model to only k possible outcomes or answers for a given task
  • B. The process of training the model on k different tasks simultaneously to improve its versatility
  • C. Providing the exact k words in the prompt to guide the model's response
  • D. Explicitly providing k examples of the intended task in the prompt to guide the model's output
Answer: D
Explanation:
Comprehensive and Detailed In-Depth Explanation=
"k-shot prompting" (e.g., few-shot) involves providing k examples of a task in the prompt to guide the LLM's output via in-context learning, without additional training. This makes Option B correct. Option A (k words) misinterprets-examples, not word count, matter. Option C (training) confuses prompting with fine-tuning. Option D (k outcomes) is unrelated-k refers to examples, not limits. k-shot leverages pre-trained knowledge efficiently.
OCI 2025 Generative AI documentation likely covers k-shot prompting under prompt engineering techniques.

NEW QUESTION # 51
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?
  • A. By restricting updates to only a specific group of transformer layers
  • B. By incorporating additional layers to the base model
  • C. By excluding transformer layers from the fine-tuning process entirely
  • D. By allowing updates across all layers of the model
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few fine-tuning enhances efficiency by updating only a small subset of transformer layers or parameters (e.g., via adapters), reducing computational load-Option D is correct. Option A (adding layers) increases complexity, not efficiency. Option B (all layers) describes Vanilla fine-tuning. Option C (excluding layers) is false-T-Few updates, not excludes. This selective approach optimizes resource use.
OCI 2025 Generative AI documentation likely details T-Few under PEFT methods.

NEW QUESTION # 52
When does a chain typically interact with memory in a run within the LangChain framework?
  • A. Continuously throughout the entire chain execution process
  • B. Before user input and after chain execution
  • C. After user input but before chain execution, and again after core logic but before output
  • D. Only after the output has been generated
Answer: C
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 # 53
......
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