HIGHLY-DEMANDED 1Z0-1127-25 EXAM MATERIALS SUPPLY YOU UNPARALLELED PRACTICE PREP - REAL4DUMPS

Highly-demanded 1Z0-1127-25 Exam Materials Supply You Unparalleled Practice Prep - Real4dumps

Highly-demanded 1Z0-1127-25 Exam Materials Supply You Unparalleled Practice Prep - Real4dumps

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q26-Q31):

NEW QUESTION # 26
In the simplified workflow for managing and querying vector data, what is the role of indexing?

  • A. To map vectors to a data structure for faster searching, enabling efficient retrieval
  • B. To categorize vectors based on their originating data type (text, images, audio)
  • C. To convert vectors into a non-indexed format for easier retrieval
  • D. To compress vector data for minimized storage usage

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Indexing in vector databases maps high-dimensional vectors to a data structure (e.g., HNSW,Annoy) to enable fast, efficient similarity searches, critical for real-time retrieval in LLMs. This makes Option B correct. Option A is backwards-indexing organizes, not de-indexes. Option C (compression) is a side benefit, not the primary role. Option D (categorization) isn't indexing's purpose-it's about search efficiency. Indexing powers scalable vector queries.
OCI 2025 Generative AI documentation likely explains indexing under vector database operations.


NEW QUESTION # 27
How are prompt templates typically designed for language models?

  • A. As complex algorithms that require manual compilation
  • B. To be used without any modification or customization
  • C. As predefined recipes that guide the generation of language model prompts
  • D. To work only with numerical data instead of textual content

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompt templates are predefined, reusable structures (e.g., with placeholders for variables) that guide LLM prompt creation, streamlining consistent input formatting. This makes Option B correct. Option A is false, as templates aren't complex algorithms but simple frameworks. Option C is incorrect, as templates are customizable. Option D is wrong, as they handle text, not just numbers.Templates enhance efficiency in prompt engineering.
OCI 2025 Generative AI documentation likely covers prompt templates under prompt engineering or LangChain tools.
Here is the next batch of 10 questions (21-30) from your list, formatted as requested with detailed explanations. The answers are based on widely accepted principles in generative AI and Large Language Models (LLMs), aligned with what is likely reflected in the Oracle Cloud Infrastructure (OCI) 2025 Generative AI documentation. Typographical errors have been corrected for clarity.


NEW QUESTION # 28
What is LCEL in the context of LangChain Chains?

  • A. A programming language used to write documentation for LangChain
  • B. An older Python library for building Large Language Models
  • C. A legacy method for creating chains in LangChain
  • D. A declarative way to compose chains together using LangChain Expression Language

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LCEL (LangChain Expression Language) is a declarative syntax in LangChain for composing chains-sequences of operations involving LLMs, tools, and memory. It simplifies chain creation with a readable, modular approach, making Option C correct. Option A is false, as LCEL isn't fordocumentation. Option B is incorrect, as LCEL is current, not legacy. Option D is wrong, as LCEL is part of LangChain, not a standalone LLM library. LCEL enhances flexibility in application design.
OCI 2025 Generative AI documentation likely mentions LCEL under LangChain integration or chain composition.


NEW QUESTION # 29
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

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

Answer: B

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 # 30
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours are required for fine-tuning if the cluster is active for 10 days?

  • A. 240 unit hours
  • B. 20 unit hours
  • C. 480 unit hours
  • D. 744 unit hours

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In OCI, a dedicated AI cluster's usage is typically measured in unit hours, where 1 unit hour = 1 hour of cluster activity. For 10 days, assuming 24 hours per day, the calculation is: 10 days × 24 hours/day = 240 hours. Thus, Option B (240 unit hours) is correct. Option A (480) might assume multiple clusters or higher rates, but the question specifies one cluster. Option C (744) approximates a month (31 days), not 10 days. Option D (20) is arbitrarily low.
OCI 2025 Generative AI documentation likely specifies unit hour calculations under Dedicated AI Cluster pricing.


NEW QUESTION # 31
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