2025 1Z0-1127-25 RELIABLE EXAM BOOK | EXCELLENT ORACLE CLOUD INFRASTRUCTURE 2025 GENERATIVE AI PROFESSIONAL 100% FREE LATEST PRACTICE MATERIALS

2025 1Z0-1127-25 Reliable Exam Book | Excellent Oracle Cloud Infrastructure 2025 Generative AI Professional 100% Free Latest Practice Materials

2025 1Z0-1127-25 Reliable Exam Book | Excellent Oracle Cloud Infrastructure 2025 Generative AI Professional 100% Free Latest Practice Materials

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q52-Q57):

NEW QUESTION # 52
What is the primary purpose of LangSmith Tracing?

  • A. To generate test cases for language models
  • B. To debug issues in language model outputs
  • C. To monitor the performance of language models
  • D. To analyze the reasoning process of language models

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LangSmith Tracing is a tool for debugging and understanding LLM applications by tracking inputs, outputs, and intermediate steps, helping identify issues in complex chains. This makes Option C correct. Option A (test cases) is a secondary use, not primary. Option B (reasoning) overlaps but isn't the core focus-debugging is. Option D (performance) is broader-tracing targets specific issues. It's essential for development transparency.OCI 2025 Generative AI documentation likely covers LangSmith under debugging or monitoring tools.


NEW QUESTION # 53
Why is normalization of vectors important before indexing in a hybrid search system?

  • A. It ensures that all vectors represent keywords only.
  • B. It standardizes vector lengths for meaningful comparison using metrics such as Cosine Similarity.
  • C. It significantly reduces the size of the database.
  • D. It converts all sparse vectors to dense vectors.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Normalization scales vectors to unit length, ensuring comparisons (e.g., cosine similarity) reflect directional similarity, not magnitude differences, critical for hybrid search accuracy. This makes Option C correct. Option A is false-vectors represent semantics, not just keywords. Option B (size reduction) isn't the goal. Option D (sparse to dense) is unrelated-normalization adjusts length. Normalized vectors ensure fair similarity metrics.
OCI 2025 Generative AI documentation likely explains normalization under vector preprocessing.


NEW QUESTION # 54
What is the purpose of Retrieval Augmented Generation (RAG) in text generation?

  • A. To generate text using extra information obtained from an external data source
  • B. To generate text based only on the model's internal knowledge without external data
  • C. To retrieve text from an external source and present it without any modifications
  • D. To store text in an external database without using it for generation

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
RAG enhances text generation by combining an LLM's internal knowledge with external data retrieved from sources (e.g., vector databases), improving accuracy and relevance. This makes Option B correct. Option A describes standalone LLMs, not RAG. Option C misrepresents RAG's purpose-data is used, not just stored. Option D is incorrect-RAG generates new text, not just retrieves. RAG is ideal for dynamic, informed responses.
OCI 2025 Generative AI documentation likely explains RAG under advanced generation techniques.


NEW QUESTION # 55
Which LangChain component is responsible for generating the linguistic output in a chatbot system?

  • A. Document Loaders
  • B. LangChain Application
  • C. Vector Stores
  • D. LLMs

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, LLMs (Large Language Models) generate the linguistic output (text responses) in a chatbot system, leveraging their pre-trained capabilities. This makes Option D correct. Option A (Document Loaders) ingests data, not generates text. Option B (Vector Stores) manages embeddings for retrieval, not generation. Option C (LangChain Application) is too vague-it's the system, not a specific component. LLMs are the core text-producing engine.
OCI 2025 Generative AI documentation likely identifies LLMs as the generation component in LangChain.


NEW QUESTION # 56
Which statement accurately reflects the differences between these approaches in terms of the number of parameters modified and the type of data used?

  • A. Fine-tuning modifies all parameters using labeled, task-specific data, whereas Parameter Efficient Fine-Tuning updates a few, new parameters also with labeled, task-specific data.
  • B. Soft Prompting and continuous pretraining are both methods that require no modification to the original parameters of the model.
  • C. Fine-tuning and continuous pretraining both modify all parameters and use labeled, task-specific data.
  • D. Parameter Efficient Fine-Tuning and Soft Prompting modify all parameters of the model using unlabeled data.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Fine-tuning typically involves updating all parameters of an LLM using labeled, task-specific data to adapt it to a specific task, which is computationally expensive. Parameter Efficient Fine-Tuning (PEFT), such as methods like LoRA (Low-Rank Adaptation), updates only a small subset of parameters (often newly added ones) while still using labeled, task-specific data, making it more efficient. Option C correctly captures this distinction. Option A is wrong because continuous pretraining uses unlabeled data and isn't task-specific. Option B is incorrect as PEFT and Soft Prompting don't modify all parameters, and Soft Prompting typically uses labeled examples indirectly. Option D is inaccurate because continuous pretraining modifies parameters, while SoftPrompting doesn't.
OCI 2025 Generative AI documentation likely discusses Fine-tuning and PEFT under model customization techniques.


NEW QUESTION # 57
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