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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q34-Q39):

NEW QUESTION # 34
What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?

A. The percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation
B. The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model
C. The improvement in accuracy achieved by the model during training on the user-uploaded dataset
D. The level of incorrectness in the model's predictions, with lower values indicating better performance

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Loss measures the discrepancy between a model's predictions and true values, with lower values indicating better fit-Option D is correct. Option A (accuracy difference) isn't loss-it's a derived metric. Option B (error percentage) is closer to error rate, not loss. Option C (accuracy improvement) is a training outcome, not loss's definition. Loss is a fundamental training signal.
OCI 2025 Generative AI documentation likely defines loss under fine-tuning metrics.

NEW QUESTION # 35
What is the purpose of Retrieval Augmented Generation (RAG) in text generation?

A. To generate text using extra information obtained from an external data source
B. To retrieve text from an external source and present it without any modifications
C. To store text in an external database without using it for generation
D. To generate text based only on the model's internal knowledge without external data

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
RAG enhances text generation by combining an LLM's internal knowledge with external data retrieved from sources (e.g., vector databases), improving accuracy and relevance. This makes Option B correct. Option A describes standalone LLMs, not RAG. Option C misrepresents RAG's purpose-data is used, not just stored. Option D is incorrect-RAG generates new text, not just retrieves. RAG is ideal for dynamic, informed responses.
OCI 2025 Generative AI documentation likely explains RAG under advanced generation techniques.

NEW QUESTION # 36
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

A. "Top p" assigns penalties to frequently occurring tokens.
B. "Top p" determines the maximum number of tokens per response.
C. "Top p" selects tokens from the "Top k" tokens sorted by probability.
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 # 37
When does a chain typically interact with memory in a run within the LangChain framework?

A. Continuously throughout the entire chain execution process.
B. After user input but before chain execution, and again after core logic but before output.
C. Only after the output has been generated.
D. Before user input and after chain execution.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, a chain interacts with memory after receiving user input (to load prior context) but before execution (to inform the process), and again after the core logic (to update memory with new context) but before the final output. This ensures context continuity, making Option C correct. Option A is too late, missing pre-execution context. Option B is misordered. Option D overstates interaction, as it's not continuous but at specific points. Memory integration is key for stateful chains.
OCI 2025 Generative AI documentation likely details memory interaction under LangChain workflows.

NEW QUESTION # 38
What differentiates Semantic search from traditional keyword search?

A. It relies solely on matching exact keywords in the content.
B. It involves understanding the intent and context of the search.
C. It is based on the date and author of the content.
D. It depends on the number of times keywords appear in the content.

Answer: B

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 # 39
......

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