1Z0-1127-25 LATEST DEMO, 1Z0-1127-25 VALID EXAM FEE

1Z0-1127-25 Latest Demo, 1Z0-1127-25 Valid Exam Fee

1Z0-1127-25 Latest Demo, 1Z0-1127-25 Valid Exam Fee

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Oracle 1Z0-1127-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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 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 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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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q74-Q79):

NEW QUESTION # 74
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

  • A. Translation models
  • B. Generation models
  • C. Summarization models
  • D. Embedding models

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
OCI Generative AI typically offers pretrained models for summarization (A), generation (B), and embeddings (D), aligning with common generative tasks. Translation models (C) are less emphasized in generative AI services, often handled by specialized NLP platforms, making C the NOT category. While possible, translation isn't a core OCI generative focus based on standard offerings.
OCI 2025 Generative AI documentation likely lists model categories under pretrained options.


NEW QUESTION # 75
When does a chain typically interact with memory in a run within the LangChain framework?

  • A. Only after the output has been generated.
  • 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. Continuously throughout the entire chain execution process.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, a chain interacts with memory after receiving user input (to load prior context) but before execution (to inform the process), and again after the core logic (to update memory with new context) but before the final output. This ensures context continuity, making Option C correct. Option A is too late, missing pre-execution context. Option B is misordered. Option D overstates interaction, as it's not continuous but at specific points. Memory integration is key for stateful chains.
OCI 2025 Generative AI documentation likely details memory interaction under LangChain workflows.


NEW QUESTION # 76
Which is a distinguishing feature of "Parameter-Efficient Fine-Tuning (PEFT)" as opposed to classic "Fine-tuning" in Large Language Model training?

  • A. PEFT modifies all parameters and uses unlabeled, task-agnostic data.
  • B. PEFT modifies all parameters and is typically used when no training data exists.
  • C. PEFT does not modify any parameters but uses soft prompting with unlabeled data.
  • D. PEFT involves only a few or new parameters and uses labeled, task-specific data.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
PEFT (e.g., LoRA, T-Few) updates a small subset of parameters (often new ones) using labeled, task-specific data, unlike classic fine-tuning, which updates all parameters-Option A is correct. Option B reverses PEFT's efficiency. Option C (no modification) fits soft prompting, not all PEFT. Option D (all parameters) mimics classic fine-tuning. PEFT reduces resource demands.
OCI 2025 Generative AI documentation likely contrasts PEFT and fine-tuning under customization methods.


NEW QUESTION # 77
How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?

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

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Temperature controls the randomness of an LLM's output by adjusting the softmax probability distribution over the vocabulary. Increasing temperature (e.g., to 1.5) flattens the distribution, reducing the dominance of high-probability words and allowing more diverse, less predictable choices, making Option C correct. Option A is misleading-higher temperature doesn't remove the top word's impact entirely but reduces its relative likelihood. Option B is incorrect, as decreasing temperature sharpens the distribution, favoring likely words, not broadening it. Option D is false, as temperature directly affects the distribution, not just decoding speed. This mechanism is key for balancing creativity and coherence.
OCI 2025 Generative AI documentation likely explains temperature under decoding or output control parameters.


NEW QUESTION # 78
How are chains traditionally created in LangChain?

  • A. Using Python classes, such as LLMChain and others
  • B. Declaratively, with no coding required
  • C. By using machine learning algorithms
  • D. Exclusively through third-party software integrations

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Traditionally, LangChain chains (e.g., LLMChain) are created using Python classes that define sequences of operations, such as calling an LLM or processing data. This programmatic approach predates LCEL's declarative style, making Option C correct. Option A is vague and incorrect, as chains aren't ML algorithms themselves. Option B describes LCEL, not traditional methods. Option D is false, as third-party integrations aren't required. Python classes provide structured chain building.
OCI 2025 Generative AI documentation likely contrasts traditional chains with LCEL under LangChain sections.


NEW QUESTION # 79
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