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NEW QUESTION # 30
What differentiates Semantic search from traditional keyword search?
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation=
Semantic search uses embeddings and NLP to understand the meaning, intent, and context behind a query, rather than just matching exact keywords (as in traditional search). This enables more relevant results, even if exact terms aren't present, making Option C correct. Options A and B describe traditional keyword search mechanics. Option D is unrelated, as metadata like date or author isn't the primary focus of semantic search. Semantic search leverages vector representations for deeper understanding.
OCI 2025 Generative AI documentation likely contrasts semantic and keyword search under search or retrieval sections.
NEW QUESTION # 31
What is the purpose of memory in the LangChain framework?
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, memory stores contextual data (e.g., chat history) and provides mechanisms to summarize or recall past interactions, enabling coherent, context-aware conversations. This makes Option B correct. Option A is too limited, as memory does more than just input/output handling. Option C is unrelated, as memory focuses on interaction context, not abstract calculations. Option D is inaccurate, as memory is dynamic, not a static database. Memory is crucial for stateful applications.
OCI 2025 Generative AI documentation likely discusses memory under LangChain's context management features.
NEW QUESTION # 32
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In RAG, the Ranker evaluates and prioritizes retrieved information (e.g., documents) based on relevance to the query, refining what the Retriever fetches-Option D is correct. The Retriever (A) fetches data, not ranks it. Encoder-Decoder (B) isn't a distinct RAG component-it's part of the LLM. The Generator (C) produces text, not prioritizes. Ranking ensures high-quality inputs for generation.
OCI 2025 Generative AI documentation likely details the Ranker under RAG pipeline components.
NEW QUESTION # 33
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation=
The "temperature" parameter adjusts the randomness of an LLM's output by scaling the softmax distribution-low values (e.g., 0.7) make it more deterministic, high values (e.g., 1.5) increase creativity-Option A is correct. Option B (stop string) is the stop sequence. Option C (penalty) relates to presence/frequency penalties. Option D (max tokens) is a separate parameter. Temperature shapes output style.
OCI 2025 Generative AI documentation likely defines temperature under generation parameters.
NEW QUESTION # 34
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours are required for fine-tuning if the cluster is active for 10 hours?
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation=
In OCI, unit hours typically equal actual hours of cluster activity unless specified otherwise (e.g., per GPU scaling). For 10 hours of activity, it's 10 hours × 1 unit/hour = 10 unit hours, but options suggest a multiplier (common in cloud pricing). Assuming a standard 2-unit/hour rate (e.g., for GPU clusters), it's 10 × 2 = 20 unit hours-Option C fits best. Options A, B, and D imply inconsistent rates (2.5, 4, 3).
OCI 2025 Generative AI documentation likely specifies unit hour rates under DedicatedAI Cluster pricing.
NEW QUESTION # 35
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