USEFUL ORACLE 1Z0-1127-25 DUMPS - 1Z0-1127-25 LATEST EXAM COST

Useful Oracle 1Z0-1127-25 Dumps - 1Z0-1127-25 Latest Exam Cost

Useful Oracle 1Z0-1127-25 Dumps - 1Z0-1127-25 Latest Exam Cost

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Oracle 1Z0-1127-25 Exam Syllabus Topics:

TopicDetails
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
  • 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
  • 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 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.

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q74-Q79):

NEW QUESTION # 74
What does "k-shot prompting" refer to when using Large Language Models for task-specific applications?

  • A. Explicitly providing k examples of the intended task in the prompt to guide the model's output
  • B. Providing the exact k words in the prompt to guide the model's response
  • C. Limiting the model to only k possible outcomes or answers for a given task
  • D. The process of training the model on k different tasks simultaneously to improve its versatility

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
"k-shot prompting" (e.g., few-shot) involves providing k examples of a task in the prompt to guide the LLM's output via in-context learning, without additional training. This makes Option B correct. Option A (k words) misinterprets-examples, not word count, matter. Option C (training) confuses prompting with fine-tuning. Option D (k outcomes) is unrelated-k refers to examples, not limits. k-shot leverages pre-trained knowledge efficiently.
OCI 2025 Generative AI documentation likely covers k-shot prompting under prompt engineering techniques.


NEW QUESTION # 75
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

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

Answer: A

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 # 76
What is the characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

  • A. It selectively updates only a fraction of weights to reduce the number of parameters.
  • B. It increases the training time as compared to Vanilla fine-tuning.
  • C. It updates all the weights of the model uniformly.
  • D. It selectively updates only a fraction of weights to reduce computational load and avoid overfitting.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few fine-tuning (a Parameter-Efficient Fine-Tuning method) updates a small subset of the model's weights, reducing computational cost and mitigating overfitting compared to Vanilla fine-tuning, which updates all weights. This makes Option C correct. Option A describes Vanilla fine-tuning, not T-Few. Option B is incomplete, as it omits the overfitting benefit. Option D is false, as T-Few typically reduces training time due to fewer updates. T-Few balances efficiency and performance.
OCI 2025 Generative AI documentation likely describes T-Few under fine-tuningoptions.


NEW QUESTION # 77
What does accuracy measure in the context of fine-tuning results for a generative model?

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

Answer: A

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 # 78
Which statement is true about string prompt templates and their capability regarding variables?

  • A. They can only support a single variable at a time.
  • B. They support any number of variables, including the possibility of having none.
  • C. They require a minimum of two variables to function properly.
  • D. They are unable to use any variables.

Answer: B

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 # 79
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