Quiz 2025 Professional 1Z0-1127-25: Detailed Oracle Cloud Infrastructure 2025 Generative AI Professional Study Dumps

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q56-Q61):

NEW QUESTION # 56
Which LangChain component is responsible for generating the linguistic output in a chatbot system?

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

Answer: A

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 # 57
When should you use the T-Few fine-tuning method for training a model?

A. For complicated semantic understanding improvement
B. For datasets with hundreds of thousands to millions of samples
C. For models that require their own hosting dedicated AI cluster
D. For datasets with a few thousand samples or less

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few is ideal for smaller datasets (e.g., a few thousand samples) where full fine-tuning risks overfitting and is computationally wasteful-Option C is correct. Option A (semantic understanding) is too vague-dataset size matters more. Option B (dedicated cluster) isn't a condition for T-Few. Option D (large datasets) favors Vanilla fine-tuning. T-Few excels in low-data scenarios.
OCI 2025 Generative AI documentation likely specifies T-Few use cases under fine-tuning guidelines.

NEW QUESTION # 58
What does accuracy measure in the context of fine-tuning results for a generative model?

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

Answer: B

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 # 59
What do prompt templates use for templating in language model applications?

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

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 # 60
Which is a cost-related benefit of using vector databases with Large Language Models (LLMs)?

A. They offer real-time updated knowledge bases and are cheaper than fine-tuned LLMs.
B. They increase the cost due to the need for real-time updates.
C. They require frequent manual updates, which increase operational costs.
D. They are more expensive but provide higher quality data.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Vector databases enable real-time knowledge retrieval for LLMs (e.g., in RAG), avoiding the high computational and data costs of fine-tuning an LLM for every update. They store embeddings efficiently, making them a cost-effective alternative to retraining, thus Option B is correct. Option A is false-updates are automated, not manual. Option C misrepresents-real-time capability reduces, not increases, costs compared to fine-tuning. Option D is incorrect-vector databases aren't inherently more expensive; they optimize cost and performance. This makes them economical for dynamic applications.
OCI 2025 Generative AI documentation likely highlights vector database cost benefits under RAG or data management sections.

NEW QUESTION # 61
......

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