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Get All Oracle Cloud Infrastructure 2025 Generative AI Professional Exam Questions with Validated Answers
Vendor: | Oracle |
---|---|
Exam Code: | 1Z0-1127-25 |
Exam Name: | Oracle Cloud Infrastructure 2025 Generative AI Professional |
Exam Questions: | 88 |
Last Updated: | October 6, 2025 |
Related Certifications: | Oracle Cloud , Oracle Cloud Infrastructure |
Exam Tags: | Professional Level Oracle Machine Learning/AI EngineersGen AI Professionals |
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In which scenario is soft prompting appropriate compared to other training styles?
Comprehensive and Detailed In-Depth Explanation=
Soft prompting adds trainable parameters (soft prompts) to adapt an LLM without retraining its core weights, ideal for low-resource customization without task-specific data. This makes Option C correct. Option A suits fine-tuning. Option B may require more than soft prompting (e.g., domain fine-tuning). Option D describes pretraining, not soft prompting. Soft prompting is efficient for specific adaptations.
: OCI 2025 Generative AI documentation likely discusses soft prompting under PEFT methods.
How does a presence penalty function in language model generation when using OCI Generative AI service?
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.
What is the primary purpose of LangSmith Tracing?
Comprehensive and Detailed In-Depth Explanation=
LangSmith Tracing is a tool for debugging and understanding LLM applications by tracking inputs, outputs, and intermediate steps, helping identify issues in complex chains. This makes Option C correct. Option A (test cases) is a secondary use, not primary. Option B (reasoning) overlaps but isn't the core focus---debugging is. Option D (performance) is broader---tracing targets specific issues. It's essential for development transparency.
: OCI 2025 Generative AI documentation likely covers LangSmith under debugging or monitoring tools.
What happens if a period (.) is used as a stop sequence in text generation?
Comprehensive and Detailed In-Depth Explanation=
A stop sequence in text generation (e.g., a period) instructs the model to halt generation once it encounters that token, regardless of the token limit. If set to a period, the model stops after the first sentence ends, making Option D correct. Option A is false, as stop sequences are enforced. Option B contradicts the stop sequence's purpose. Option C is incorrect, as it stops at the sentence level, not paragraph.
: OCI 2025 Generative AI documentation likely explains stop sequences under text generation parameters.
Which statement describes the difference between "Top k" and "Top p" in selecting the next token in the OCI Generative AI Generation models?
Comprehensive and Detailed In-Depth Explanation=
''Top k'' sampling selects from the k most probable tokens, based on their ranked position, while ''Top p'' (nucleus sampling) selects from tokens whose cumulative probability exceeds p, focusing on a dynamic probability mass---Option B is correct. Option A is false---they differ in selection, not penalties. Option C reverses definitions. Option D (frequency) is incorrect---both use probability, not frequency. This distinction affects diversity.
: OCI 2025 Generative AI documentation likely contrasts Top k and Top p under sampling methods.
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