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Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?
Comprehensive and Detailed In-Depth Explanation=
OCI Generative AI typically offers pretrained models for summarization (A), generation (B), and embeddings (D), aligning with common generative tasks. Translation models (C) are less emphasized in generative AI services, often handled by specialized NLP platforms, making C the NOT category. While possible, translation isn't a core OCI generative focus based on standard offerings.
: OCI 2025 Generative AI documentation likely lists model categories under pretrained options.
An LLM emits intermediate reasoning steps as part of its responses. Which of the following techniques is being utilized?
Comprehensive and Detailed In-Depth Explanation=
Chain-of-Thought (CoT) prompting encourages an LLM to emit intermediate reasoning steps before providing a final answer, improving performance on complex tasks by mimicking human reasoning. This matches the scenario, making Option D correct. Option A (In-context Learning) involves learning from examples in the prompt, not necessarily reasoning steps. Option B (Step-Back Prompting) involves reframing the problem, not emitting steps. Option C (Least-to-Most Prompting) breaks tasks into subtasks but doesn't focus on intermediate reasoning explicitly. CoT is widely recognized for reasoning tasks.
: OCI 2025 Generative AI documentation likely covers Chain-of-Thought under advanced prompting techniques.
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.
Which role does a "model endpoint" serve in the inference workflow of the OCI Generative AI service?
Comprehensive and Detailed In-Depth Explanation=
A ''model endpoint'' in OCI's inference workflow is an API or interface where users send requests and receive responses from a deployed model---Option B is correct. Option A (weight updates) occurs during fine-tuning, not inference. Option C (metrics) is for evaluation, not endpoints. Option D (training data) relates to storage, not inference. Endpoints enable real-time interaction.
: OCI 2025 Generative AI documentation likely describes endpoints under inference deployment.
Which is NOT a built-in memory type in LangChain?
Comprehensive and Detailed In-Depth Explanation=
LangChain includes built-in memory types like ConversationBufferMemory (stores full history), ConversationSummaryMemory (summarizes history), and ConversationTokenBufferMemory (limits by token count)---Options B, C, and D are valid. ConversationImageMemory (A) isn't a standard type---image handling typically requires custom or multimodal extensions, not a built-in memory class---making A correct as NOT included.
: OCI 2025 Generative AI documentation likely lists memory types under LangChain memory management.
