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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q62-Q67):

NEW QUESTION # 62
Which is NOT a typical use case for LangSmith Evaluators?

A. Detecting bias or toxicity
B. Evaluating factual accuracy of outputs
C. Aligning code readability
D. Measuring coherence of generated text

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
LangSmith Evaluators assess LLM outputs for qualities like coherence (A), factual accuracy (C), and bias/toxicity (D), aiding development and debugging. Aligning code readability (B) pertains to software engineering, not LLM evaluation, making it the odd one out-Option B is correct as NOT a use case. Options A, C, and D align with LangSmith's focus on text quality and ethics.
OCI 2025 Generative AI documentation likely lists LangSmith Evaluator use cases under evaluation tools.

NEW QUESTION # 63
Which component of Retrieval-Augmented Generation (RAG) evaluates and prioritizes the information retrieved by the retrieval system?

A. Retriever
B. Encoder-Decoder
C. Generator
D. Ranker

Answer: D

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 # 64
How do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language processing?

A. Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance.
B. Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.
C. Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.
D. Dot Product measures the magnitude and direction of vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Dot Product computes the raw similarity between two vectors, factoring in both magnitude and direction, while Cosine Distance (or similarity) normalizes for magnitude, focusing solely on directional alignment (angle), making Option C correct. Option A is vague-both measure similarity, not distinct content vs. topicality. Option B is false-both address semantics, not syntax. Option D is incorrect-neither measures word overlap or style directly; they operate on embeddings. Cosine is preferred for normalized semantic comparison.
OCI 2025 Generative AI documentation likely explains these metrics under vector similarity in embeddings.

NEW QUESTION # 65
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

A. "Top p" determines the maximum number of tokens per response.
B. "Top p" selects tokens from the "Top k" tokens sorted by probability.
C. "Top p" assigns penalties to frequently occurring tokens.
D. "Top p" limits token selection based on the sum of their probabilities.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
"Top p" (nucleus sampling) selects tokens whose cumulative probability exceeds a threshold (p), limiting the pool to the smallest set meeting this sum, enhancing diversity-Option C is correct. Option A confuses it with "Top k." Option B (penalties) is unrelated. Option D (max tokens) is a different parameter. Top p balances randomness and coherence.
OCI 2025 Generative AI documentation likely explains "Top p" under sampling methods.
Here is the next batch of 10 questions (81-90) from your list, formatted as requested with detailed explanations. The 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.

NEW QUESTION # 66
What is the purpose of memory in the LangChain framework?

A. To store various types of data and provide algorithms for summarizing past interactions
B. To perform complex calculations unrelated to user interaction
C. To act as a static database for storing permanent records
D. To retrieve user input and provide real-time output only

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 # 67
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