1Z0-1127-25 RELIABLE EXAM SYLLABUS, PDF 1Z0-1127-25 FORMAT

1Z0-1127-25 Reliable Exam Syllabus, Pdf 1Z0-1127-25 Format

1Z0-1127-25 Reliable Exam Syllabus, Pdf 1Z0-1127-25 Format

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q84-Q89):

NEW QUESTION # 84
What is the purpose of embeddings in natural language processing?

  • A. To translate text into a different language
  • B. To create numerical representations of text that capture the meaning and relationships between words or phrases
  • C. To increase the complexity and size of text data
  • D. To compress text data into smaller files for storage

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Embeddings in NLP are dense, numerical vectors that represent words, phrases, or sentences in a way that captures their semantic meaning and relationships (e.g., "king" and "queen" being close in vector space). This enables models to process text mathematically, making Option C correct. Option A is false, as embeddings simplify processing, not increase complexity. Option B relates to translation, not embeddings' primary purpose. Option D is incorrect, as embeddings aren't primarily for compression but for representation.
OCI 2025 Generative AI documentation likely covers embeddings under data preprocessing or vector databases.


NEW QUESTION # 85
Which role does a "model endpoint" serve in the inference workflow of the OCI Generative AI service?

  • A. Serves as a designated point for user requests and model responses
  • B. Evaluates the performance metrics of the custom models
  • C. Updates the weights of the base model during the fine-tuning process
  • D. Hosts the training data for fine-tuning custom models

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
A "model endpoint" in OCI's inference workflow is an API or interface where users send requests and receive responses from a deployed model-Option B is correct. Option A (weight updates) occurs during fine-tuning, not inference. Option C (metrics) is for evaluation, not endpoints. Option D (training data) relates to storage, not inference. Endpoints enable real-time interaction.
OCI 2025 Generative AI documentation likely describes endpoints under inference deployment.


NEW QUESTION # 86
What issue might arise from using small datasets with the Vanilla fine-tuning method in the OCI Generative AI service?

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

Answer: B

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 # 87
When does a chain typically interact with memory in a run within the LangChain framework?

  • A. Continuously throughout the entire chain execution process
  • B. After user input but before chain execution, and again after core logic but before output
  • C. Only after the output has been generated
  • D. Before user input and after chain execution

Answer: B

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 # 88
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

  • A. Ranker
  • B. Generator
  • C. Retriever
  • D. Encoder-Decoder

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In RAG, the Ranker evaluates and prioritizes retrieved information (e.g., documents) based on relevance to the query, refining what the Retriever fetches-Option D is correct. The Retriever (A) fetches data, not ranks it. Encoder-Decoder (B) isn't a distinct RAG component-it's part of the LLM. The Generator (C) produces text, not prioritizes. Ranking ensures high-quality inputs for generation.
OCI 2025 Generative AI documentation likely details the Ranker under RAG pipeline components.


NEW QUESTION # 89
......

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