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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q34-Q39):

NEW QUESTION # 34
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

  • A. "Top p" selects tokens from the "Top k" tokens sorted by probability.
  • B. "Top p" determines the maximum number of tokens per response.
  • C. "Top p" limits token selection based on the sum of their probabilities.
  • D. "Top p" assigns penalties to frequently occurring tokens.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
"Top p" (nucleus sampling) selects tokens whose cumulative probability exceeds a threshold (p), limiting the pool to the smallest set meeting this sum, enhancing diversity-Option C is correct. Option A confuses it with "Top k." Option B (penalties) is unrelated. Option D (max tokens) is a different parameter. Top p balances randomness and coherence.
OCI 2025 Generative AI documentation likely explains "Top p" under sampling methods.
Here is the next batch of 10 questions (81-90) 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 # 35
What is the purpose of the "stop sequence" parameter in the OCI Generative AI Generation models?

  • A. It specifies a string that tells the model to stop generating more content.
  • B. It controls the randomness of the model's output, affecting its creativity.
  • 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: A

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 # 36
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?

  • A. By incorporating additional layers to the base model
  • B. By restricting updates to only a specific group of transformer layers
  • C. By allowing updates across all layers of the model
  • D. By excluding transformer layers from the fine-tuning process entirely

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few fine-tuning enhances efficiency by updating only a small subset of transformer layers or parameters (e.g., via adapters), reducing computational load-Option D is correct. Option A (adding layers) increases complexity, not efficiency. Option B (all layers) describes Vanilla fine-tuning. Option C (excluding layers) is false-T-Few updates, not excludes. This selective approach optimizes resource use.
OCI 2025 Generative AI documentation likely details T-Few under PEFT methods.


NEW QUESTION # 37
Why is it challenging to apply diffusion models to text generation?

  • A. Because text generation does not require complex models
  • B. Because text representation is categorical unlike images
  • C. Because diffusion models can only produce images
  • D. Because text is not categorical

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Diffusion models, widely used for image generation, iteratively denoise data from noise to a structured output. Images are continuous (pixel values), while text is categorical (discrete tokens), making it challenging to apply diffusion directly to text, as the denoising process struggles with discrete spaces. This makes Option C correct. Option A is false-text generation can benefit from complex models. Option B is incorrect-text is categorical. Option D is wrong, as diffusion models aren't inherently image-only but are better suited to continuous data. Research adapts diffusion for text, but it's less straightforward.
OCI 2025 Generative AI documentation likely discusses diffusion models under generative techniques, noting their image focus.


NEW QUESTION # 38
What is LangChain?

  • A. A Java library for text summarization
  • B. A JavaScript library for natural language processing
  • C. A Python library for building applications with Large Language Models
  • D. A Ruby library for text generation

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LangChain is a Python library designed to simplify building applications with LLMs by providing tools for chaining operations, managing memory, and integrating external data (e.g., via RAG). This makes Option B correct. Options A, C, and D are incorrect, as LangChain is neither JavaScript, Java, nor Ruby-based, nor limited to summarization or generation alone-it's broader in scope. It's widely used for LLM-powered apps.
OCI 2025 Generative AI documentation likely introduces LangChain under supported frameworks.


NEW QUESTION # 39
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