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1Z0-1127-25 New Real Test, Test 1Z0-1127-25 Sample Questions

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Oracle 1Z0-1127-25 Exam Syllabus Topics:

Topic
Details

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 - 1Z0-1127-25 - Oracle Cloud Infrastructure 2025 Generative AI Professional –High-quality New Real Test

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q48-Q53):

NEW QUESTION # 48
What does the Loss metric indicate about a model's predictions?

A. Loss is a measure that indicates how wrong the model's predictions are.
B. Loss describes the accuracy of the right predictions rather than the incorrect ones.
C. Loss indicates how good a prediction is, and it should increase as the model improves.
D. Loss measures the total number of predictions made by a model.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Loss is a metric that quantifies the difference between a model's predictions and the actual target values, indicating how incorrect (or "wrong") the predictions are. Lower loss means better performance, making Option B correct. Option A is false-loss isn't about prediction count. Option C is incorrect-loss decreases as the model improves, not increases. Option D is wrong-loss measures overall error, not just correct predictions. Loss guides training optimization.
OCI 2025 Generative AI documentation likely defines loss under model training and evaluation metrics.

NEW QUESTION # 49
What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?

A. The percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation
B. The level of incorrectness in the model's predictions, with lower values indicating better performance
C. The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model
D. The improvement in accuracy achieved by the model during training on the user-uploaded dataset

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Loss measures the discrepancy between a model's predictions and true values, with lower values indicating better fit-Option D is correct. Option A (accuracy difference) isn't loss-it's a derived metric. Option B (error percentage) is closer to error rate, not loss. Option C (accuracy improvement) is a training outcome, not loss's definition. Loss is a fundamental training signal.
OCI 2025 Generative AI documentation likely defines loss under fine-tuning metrics.

NEW QUESTION # 50
How are prompt templates typically designed for language models?

A. To be used without any modification or customization
B. As complex algorithms that require manual compilation
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 # 51
How can the concept of "Groundedness" differ from "Answer Relevance" in the context of Retrieval Augmented Generation (RAG)?

A. Groundedness refers to contextual alignment, whereas Answer Relevance deals with syntactic accuracy.
B. Groundedness focuses on data integrity, whereas Answer Relevance emphasizes lexical diversity.
C. Groundedness measures relevance to the user query, whereas Answer Relevance evaluates data integrity.
D. Groundedness pertains to factual correctness, whereas Answer Relevance concerns query relevance.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In RAG, "Groundedness" assesses whether the response is factually correct and supported by retrieved data, while "Answer Relevance" evaluates how well the response addresses the user's query. Option A captures this distinction accurately. Option B is off-groundedness isn't just contextual alignment, and relevance isn't about syntax. Option C swaps the definitions. Option D misaligns-groundedness isn't solely data integrity, and relevance isn't lexical diversity. This distinction ensures RAG outputs are both true and pertinent.
OCI 2025 Generative AI documentation likely defines these under RAG evaluation metrics.

NEW QUESTION # 52
Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)?

A. PEFT requires replacing the entire model architecture with a new one designed specifically for the new task, making it significantly more data-intensive than Fine-tuning.
B. Fine-tuning requires training the entire model on new data, often leading to substantial computational costs, whereas PEFT involves updating only a small subset of parameters, minimizing computational requirements and data needs.
C. Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive.
D. Fine-tuning and PEFT do not involve model modification; they differ only in the type of data used for training, with Fine-tuning requiring labeled data and PEFT using unlabeled data.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Fine-tuning updates all model parameters on task-specific data, incurring high computational costs, while PEFT (e.g., LoRA, T-Few) updates a small subset of parameters, reducing resource demands and often requiring less data, making Option A correct. Option B is false-PEFT doesn't replace architecture. Option C is incorrect, as PEFT isn't trained from scratch and is less intensive. Option D is wrong, as both involve modification, but PEFT is more efficient. This distinction is critical for practical LLM customization.
OCI 2025 Generative AI documentation likely compares Fine-tuning and PEFT under customization techniques.
Here is the next batch of 10 questions (31-40) 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 # 53
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