100% PASS 1Z0-1127-25 - ORACLE CLOUD INFRASTRUCTURE 2025 GENERATIVE AI PROFESSIONAL–VALID DOWNLOAD

100% Pass 1Z0-1127-25 - Oracle Cloud Infrastructure 2025 Generative AI Professional–Valid Download

100% Pass 1Z0-1127-25 - Oracle Cloud Infrastructure 2025 Generative AI Professional–Valid Download

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q22-Q27):

NEW QUESTION # 22
What do prompt templates use for templating in language model applications?

  • A. Python's str.format syntax
  • B. Python's class and object structures
  • C. Python's list comprehension syntax
  • D. Python's lambda functions

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompt templates in LLM applications (e.g., LangChain) typically use Python's str.format() syntax to insert variables into predefined string patterns (e.g., "Hello, {name}!"). This makes Option B correct. Option A (list comprehension) is for list operations, not templating. Option C (lambda functions) defines functions, not templates. Option D (classes/objects) is overkill-templates are simpler constructs. str.format() ensures flexibility and readability.
OCI 2025 Generative AI documentation likely mentions str.format() under prompt template design.


NEW QUESTION # 23
What does the Loss metric indicate about a model's predictions?

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

Answer: B

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 # 24
Which is NOT a typical use case for LangSmith Evaluators?

  • A. Evaluating factual accuracy of outputs
  • B. Measuring coherence of generated text
  • C. Detecting bias or toxicity
  • D. Aligning code readability

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LangSmith Evaluators assess LLM outputs for qualities like coherence (A), factual accuracy (C), and bias/toxicity (D), aiding development and debugging. Aligning code readability (B) pertains to software engineering, not LLM evaluation, making it the odd one out-Option B is correct as NOT a use case. Options A, C, and D align with LangSmith's focus on text quality and ethics.
OCI 2025 Generative AI documentation likely lists LangSmith Evaluator use cases under evaluation tools.


NEW QUESTION # 25
An LLM emits intermediate reasoning steps as part of its responses. Which of the following techniques is being utilized?

  • A. Chain-of-Thought
  • B. Least-to-Most Prompting
  • C. In-context Learning
  • D. Step-Back Prompting

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Chain-of-Thought (CoT) prompting encourages an LLM to emit intermediate reasoning steps before providing a final answer, improving performance on complex tasks by mimicking human reasoning. This matches the scenario, making Option D correct. Option A (In-context Learning) involves learning from examples in the prompt, not necessarily reasoning steps. Option B (Step-Back Prompting) involves reframing the problem, not emitting steps. Option C (Least-to-Most Prompting) breaks tasks into subtasks but doesn't focus on intermediate reasoning explicitly. CoT is widely recognized for reasoning tasks.
OCI 2025 Generative AI documentation likely covers Chain-of-Thought under advanced prompting techniques.


NEW QUESTION # 26
What is the purpose of Retrieval Augmented Generation (RAG) in text generation?

  • A. To generate text using extra information obtained from an external data source
  • B. To store text in an external database without using it for generation
  • C. To retrieve text from an external source and present it without any modifications
  • D. To generate text based only on the model's internal knowledge without external data

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
RAG enhances text generation by combining an LLM's internal knowledge with external data retrieved from sources (e.g., vector databases), improving accuracy and relevance. This makes Option B correct. Option A describes standalone LLMs, not RAG. Option C misrepresents RAG's purpose-data is used, not just stored. Option D is incorrect-RAG generates new text, not just retrieves. RAG is ideal for dynamic, informed responses.
OCI 2025 Generative AI documentation likely explains RAG under advanced generation techniques.


NEW QUESTION # 27
......

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