Q 21 to 30 : IZ0-1127-24 Generative AI certification | oracle certification | oracle generative ai

Опубликовано: 30 Март 2025
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Hi All,
Here we are sharing some Oracle Cloud Infrastructure 2024
Generative AI Professional certification (1Z0-1127-24) dump, it will help you to prepare for generative ai certification.

Generative AI Professional certification Questions (1Z0-1127-24):
21. "You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training data.
How many unit hours are required for fine-tuning if the cluster is active for 10 hours?"
22. Which Oracle Accelerated Data Science (ADS) class can be used to deploy a Large Language Model (LLM) application to OCI Data Science model deployment?
23. "Given the following prompts used with a Large Language Model, classify each as employing the Chain-of- Thought, Least-to-most, or Step-Back prompting technique.
1. Calculate the total number of wheels needed for 3 cars. Cars have 4 wheels each. Then, use the total number of wheels to determine how many sets of wheels we can buy with $200 if one set (4 wheels) costs $50.
2. Solve a complex math problem by first identifying the formula needed, and then solve a simpler version of the problem before tackling the full question.
3. To understand the impact of greenhouse gases on climate change, let's start by defining what greenhouse gases are. Next, we'll explore how they trap heat in the Earth's atmosphere."
24. Analyze the user prompts provided to a language model. Which scenario exemplifies prompt injection (jailbreaking)?
25. What does "k-shot prompting" refer to when using Large Language Models for task-specific applications?
26. Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?
27. Which is the main characteristic of greedy decoding in the context of language model word prediction?
28. What is the primary purpose of LangSmith Tracing?
29. Which is NOT a typical use case for LangSmith Evaluators?
30. How does the integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models (LLMs) fundamentally alter their responses?




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