1z0-1127-25 Oracle Cloud Infrastructure 2025 Generative AI Professional Free Practice Exam Questions (2025 Updated)

Comprehensive and Detailed In-Depth Explanation:

Prompt injection (jailbreaking) attempts to bypass an LLM’s restrictions by crafting prompts that trick it into revealing restricted information or behavior. Option A asks the model to creatively circumvent its protocols, a classic jailbreaking tactic—making it correct. Option B is a hypothetical persuasion task, not a bypass. Option C tests privacy handling, not injection. Option D is a creative writing prompt, not an attempt to break rules. A seeks to exploit protocol gaps.

OCI 2025 Generative AI documentation likely addresses prompt injection under security or ethics sections.

Comprehensive and Detailed In-Depth Explanation:

Frequency penalties reduce the likelihood of repeating tokens that have already appeared in the output, based on their frequency, to enhance diversity and avoid repetition. This makes Option B correct. Option A is the opposite effect. Option C describes a different mechanism (e.g., presence penalty in some contexts). Option D is inaccurate, as penalties aren’t random but frequency-based.

OCI 2025 Generative AI documentation likely covers frequency penalties under output control parameters.

Below is the next batch of 10 questions (11–20) from your list, formatted as requested with detailed explanations. These 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.

Comprehensive and Detailed In-Depth Explanation:

Normalization scales vectors to unit length, ensuring comparisons (e.g., cosine similarity) reflect directional similarity, not magnitude differences, critical for hybrid search accuracy. This makes Option C correct. Option A is false—vectors represent semantics, not just keywords. Option B (size reduction) isn’t the goal. Option D (sparse to dense) is unrelated—normalization adjusts length. Normalized vectors ensure fair similarity metrics.

OCI 2025 Generative AI documentation likely explains normalization under vector preprocessing.

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.

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.