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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q15-Q20):

NEW QUESTION # 15
How does a presence penalty function in language model generation when using OCI Generative AI service?

A. It penalizes a token each time it appears after the first occurrence.
B. It penalizes all tokens equally, regardless of how often they have appeared.
C. It only penalizes tokens that have never appeared in the text before.
D. It applies a penalty only if the token has appeared more than twice.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
A presence penalty in LLMs (including OCI's service) reduces the probability of tokens that have already appeared in the output, applying the penalty each time they reoccur after their first use. This discourages repetition, making Option D correct. Option A is false, as penalties depend on prior appearance, not uniform application. Option B is the opposite-penalizing unused tokens isn't the goal. Option C is incorrect, as the penalty isn't threshold-based (e.g., more than twice) but applied per reoccurrence. This enhances output diversity.
OCI 2025 Generative AI documentation likely details presence penalty under generation parameters.

NEW QUESTION # 16
Why is it challenging to apply diffusion models to text generation?

A. Because diffusion models can only produce images
B. Because text representation is categorical unlike images
C. Because text generation does not require complex models
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 # 17
Which is a key characteristic of Large Language Models (LLMs) without Retrieval Augmented Generation (RAG)?

A. They use vector databases exclusively to produce answers.
B. They rely on internal knowledge learned during pretraining on a large text corpus.
C. They always use an external database for generating responses.
D. They cannot generate responses without fine-tuning.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LLMs without Retrieval Augmented Generation (RAG) depend solely on the knowledge encoded in their parameters during pretraining on a large, general text corpus. They generate responses basedon this internal knowledge without accessing external data at inference time, making Option B correct. Option A is false, as external databases are a feature of RAG, not standalone LLMs. Option C is incorrect, as LLMs can generate responses without fine-tuning via prompting or in-context learning. Option D is wrong, as vector databases are used in RAG or similar systems, not in basic LLMs. This reliance on pretraining distinguishes non-RAG LLMs from those augmented with real-time retrieval.
OCI 2025 Generative AI documentation likely contrasts RAG and non-RAG LLMs under model architecture or response generation sections.

NEW QUESTION # 18
How are prompt templates typically designed for language models?

A. As complex algorithms that require manual compilation
B. To work only with numerical data instead of textual content
C. To be used without any modification or customization
D. As predefined recipes that guide the generation of language model prompts

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompt templates are predefined, reusable structures (e.g., with placeholders for variables) that guide LLM prompt creation, streamlining consistent input formatting. This makes Option B correct. Option A is false, as templates aren't complex algorithms but simple frameworks. Option C is incorrect, as templates are customizable. Option D is wrong, as they handle text, not just numbers.Templates enhance efficiency in prompt engineering.
OCI 2025 Generative AI documentation likely covers prompt templates under prompt engineering or LangChain tools.
Here is the next batch of 10 questions (21-30) 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 # 19
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

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

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 # 20
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