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[General] 100% Pass Quiz Useful Oracle - 1Z0-1127-25 - Oracle Cloud Infrastructure 2025 Ge

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【General】 100% Pass Quiz Useful Oracle - 1Z0-1127-25 - Oracle Cloud Infrastructure 2025 Ge

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Oracle 1Z0-1127-25 Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 2
  • 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 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.

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q16-Q21):NEW QUESTION # 16
Which statement is true about string prompt templates and their capability regarding variables?
  • A. They are unable to use any variables.
  • B. They can only support a single variable at a time.
  • C. They support any number of variables, including the possibility of having none.
  • D. They require a minimum of two variables to function properly.
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
String prompt templates (e.g., in LangChain) are flexible frameworks that can include zero, one, or multiple variables (placeholders) to customize prompts dynamically. They can be static (no variables) or complex (many variables), making Option C correct. Option A is too restrictive. Option B is false-variables are a core feature. Option D is incorrect, as no minimum is required. This flexibility aids prompt engineering.
OCI 2025 Generative AI documentation likely covers prompt templates under LangChain or prompt design.

NEW QUESTION # 17
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. 744 unit hours
  • C. 480 unit hours
  • D. 20 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 # 18
In which scenario is soft prompting appropriate compared to other training styles?
  • A. When the model requires continued pretraining on unlabeled data
  • B. When the model needs to be adapted to perform well in a domain on which it was not originally trained
  • C. When there is a significant amount of labeled, task-specific data available
  • D. When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training
Answer: D
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Soft prompting adds trainable parameters (soft prompts) to adapt an LLM without retraining its core weights, ideal for low-resource customization without task-specific data. This makes Option C correct. Option A suits fine-tuning. Option B may require more than soft prompting (e.g., domain fine-tuning). Option D describes pretraining, not soft prompting. Soft prompting is efficient for specific adaptations.
OCI 2025 Generative AI documentation likely discusses soft prompting under PEFT methods.

NEW QUESTION # 19
How does the temperature setting in a decoding algorithm influence the probability distribution over the vocabulary?
  • A. Decreasing the temperature broadens the distribution, making less likely words more probable.
  • B. Temperature has no effect on probability distribution; it only changes the speed of decoding.
  • C. Increasing the temperature flattens the distribution, allowing for more varied word choices.
  • D. Increasing the temperature removes the impact of the most likely word.
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Temperature adjusts the softmax distribution in decoding. Increasing it (e.g., to 2.0) flattens the curve, giving lower-probability words a better chance, thus increasing diversity-Option C is correct. Option A exaggerates-top words still have impact, just less dominance. Option B is backwards-decreasing temperature sharpens, not broadens. Option D is false-temperature directly alters distribution, not speed. This controls output creativity.
OCI 2025 Generative AI documentation likely reiterates temperature effects under decoding parameters.

NEW QUESTION # 20
How does a presence penalty function in language model generation?
  • A. It penalizes a token each time it appears after the first occurrence.
  • B. It penalizes all tokens equally, regardless of how often they have appeared.
  • C. It applies a penalty only if the token has appeared more than twice.
  • D. It penalizes only tokens that have never appeared in the text before.
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
A presence penalty reduces the probability of tokens that have already appeared in the output, applying the penalty each time they reoccur after their first use, to discourage repetition. This makes Option D correct. Option A (equal penalties) ignores prior appearance. Option B is the opposite-penalizing unused tokens isn't the intent. Option C (more than twice) adds an arbitrary threshold not typically used. Presence penalty enhances output variety.OCI 2025 Generative AI documentation likely details presence penalty under generation control parameters.

NEW QUESTION # 21
......
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