1Z0-1127-25 Simulation Questions, 1Z0-1127-25 Reliable Test Sample

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Oracle 1Z0-1127-25 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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	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 Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q73-Q78):

NEW QUESTION # 73
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

A. Determines the maximum number of tokens the model can generate per response
B. Controls the randomness of the model's output, affecting its creativity
C. Assigns a penalty to tokens that have already appeared in the preceding text
D. Specifies a string that tells the model to stop generating more content

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
The "temperature" parameter adjusts the randomness of an LLM's output by scaling the softmax distribution-low values (e.g., 0.7) make it more deterministic, high values (e.g., 1.5) increase creativity-Option A is correct. Option B (stop string) is the stop sequence. Option C (penalty) relates to presence/frequency penalties. Option D (max tokens) is a separate parameter. Temperature shapes output style.
OCI 2025 Generative AI documentation likely defines temperature under generation parameters.

NEW QUESTION # 74
What is the purpose of the "stop sequence" parameter in the OCI Generative AI Generation models?

A. It controls the randomness of the model's output, affecting its creativity.
B. It specifies a string that tells the model to stop generating more content.
C. It determines the maximum number of tokens the model can generate per response.
D. It assigns a penalty to frequently occurring tokens to reduce repetitive text.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
The "stop sequence" parameter defines a string (e.g., "." or "n") that, when generated, halts text generation, allowing control over output length or structure-Option A is correct. Option B (penalty) describes frequency/presence penalties. Option C (max tokens) is a separate parameter. Option D (randomness) relates to temperature. Stop sequences ensure precise termination.
OCI 2025 Generative AI documentation likely details stop sequences under generation parameters.

NEW QUESTION # 75
What does accuracy measure in the context of fine-tuning results for a generative model?

A. The proportion of incorrect predictions made by the model during an evaluation
B. The number of predictions a model makes, regardless of whether they are correct or incorrect
C. The depth of the neural network layers used in the model
D. How many predictions the model made correctly out of all the predictions in an evaluation

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Accuracy in fine-tuning measures the proportion of correct predictions (e.g., matching expected outputs) out of all predictions made during evaluation, reflecting model performance-Option C is correct. Option A (total predictions) ignores correctness. Option B (incorrect proportion) is the inverse-error rate. Option D (layer depth) is unrelated to accuracy. Accuracy is a standard metric for generative tasks.OCI 2025 Generative AI documentation likely defines accuracy under fine-tuning evaluation metrics.

NEW QUESTION # 76
Which is the main characteristic of greedy decoding in the context of language model word prediction?

A. It selects words based on a flattened distribution over the vocabulary.
B. It requires a large temperature setting to ensure diverse word selection.
C. It chooses words randomly from the set of less probable candidates.
D. It picks the most likely word at each step of decoding.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Greedy decoding selects the word with the highest probability at each step, optimizing locally without lookahead, making Option D correct. Option A (random low-probability) contradicts greedy's deterministic nature. Option B (high temperature) flattens distributions for diversity, not greediness. Option C (flattened distribution) aligns with sampling, not greedy decoding. Greedy is simple but can lack global coherence.
OCI 2025 Generative AI documentation likely describes greedy decoding under decoding strategies.

NEW QUESTION # 77
Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)?

A. 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.
B. Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive.
C. 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.
D. 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.

Answer: A

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 # 78
......

The Oracle 1Z0-1127-25 certification exam is one of the valuable credentials designed to demonstrate a candidate's technical expertise in information technology. They can remain current and competitive in the highly competitive market with the 1Z0-1127-25 certificate. For novices as well as seasoned professionals, the Oracle Cloud Infrastructure 2025 Generative AI Professional Questions provide an excellent opportunity to not only validate their skills but also advance their careers.

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1Z0-1127-25 Simulation Questions, 1Z0-1127-25 Reliable Test Sample

Oracle 1Z0-1127-25 Exam Syllabus Topics:

1Z0-1127-25 Reliable Test Sample | Official 1Z0-1127-25 Practice Test

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q73-Q78):

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