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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q41-Q46):

NEW QUESTION # 41
What do prompt templates use for templating in language model applications?

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

Answer: B

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 # 42
Which is a distinguishing feature of "Parameter-Efficient Fine-Tuning (PEFT)" as opposed to classic "Fine-tuning" in Large Language Model training?

A. PEFT does not modify any parameters but uses soft prompting with unlabeled data.
B. PEFT involves only a few or new parameters and uses labeled, task-specific data.
C. PEFT modifies all parameters and uses unlabeled, task-agnostic data.
D. PEFT modifies all parameters and is typically used when no training data exists.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
PEFT (e.g., LoRA, T-Few) updates a small subset of parameters (often new ones) using labeled, task-specific data, unlike classic fine-tuning, which updates all parameters-Option A is correct. Option B reverses PEFT's efficiency. Option C (no modification) fits soft prompting, not all PEFT. Option D (all parameters) mimics classic fine-tuning. PEFT reduces resource demands.
OCI 2025 Generative AI documentation likely contrasts PEFT and fine-tuning under customization methods.

NEW QUESTION # 43
Which is NOT a typical use case for LangSmith Evaluators?

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

Answer: B

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 # 44
How can the concept of "Groundedness" differ from "Answer Relevance" in the context of Retrieval Augmented Generation (RAG)?

A. Groundedness measures relevance to the user query, whereas Answer Relevance evaluates data integrity.
B. Groundedness refers to contextual alignment, whereas Answer Relevance deals with syntactic accuracy.
C. Groundedness focuses on data integrity, whereas Answer Relevance emphasizes lexical diversity.
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 # 45
Which LangChain component is responsible for generating the linguistic output in a chatbot system?

A. LangChain Application
B. Document Loaders
C. Vector Stores
D. LLMs

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, LLMs (Large Language Models) generate the linguistic output (text responses) in a chatbot system, leveraging their pre-trained capabilities. This makes Option D correct. Option A (Document Loaders) ingests data, not generates text. Option B (Vector Stores) manages embeddings for retrieval, not generation. Option C (LangChain Application) is too vague-it's the system, not a specific component. LLMs are the core text-producing engine.
OCI 2025 Generative AI documentation likely identifies LLMs as the generation component in LangChain.

NEW QUESTION # 46
......

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Free Oracle 1Z0-1127-25 Practice Exams & Free 1Z0-1127-25 Dumps

Free 1Z0-1127-25 Dumps | New 1Z0-1127-25 Test Test

Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q41-Q46):

2025 Free 1Z0-1127-25 Practice Exams | The Best 100% Free Free Oracle Cloud Infrastructure 2025 Generative AI Professional Dumps

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