RELIABLE 1Z0-1127-25 EXAM SAMPLE, LATEST 1Z0-1127-25 EXAM VCE

Reliable 1Z0-1127-25 Exam Sample, Latest 1Z0-1127-25 Exam Vce

Reliable 1Z0-1127-25 Exam Sample, Latest 1Z0-1127-25 Exam Vce

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q51-Q56):

NEW QUESTION # 51
What does in-context learning in Large Language Models involve?

  • A. Conditioning the model with task-specific instructions or demonstrations
  • B. Pretraining the model on a specific domain
  • C. Training the model using reinforcement learning
  • D. Adding more layers to the model

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In-context learning is a capability of LLMs where the model adapts to a task by interpreting instructions or examples provided in the input prompt, without additional training. This leverages the model's pre-trained knowledge, making Option C correct. Option A refers to domain-specific pretraining, not in-context learning. Option B involves reinforcement learning, a different training paradigm. Option D pertains to architectural changes, not learning via context.
OCI 2025 Generative AI documentation likely discusses in-context learning in sections on prompt-based customization.


NEW QUESTION # 52
How does a presence penalty function in language model generation when using OCI Generative AI service?

  • A. It penalizes a token each time it appears after the first occurrence.
  • B. It penalizes all tokens equally, regardless of how often they have appeared.
  • C. It only penalizes tokens that have never appeared in the text before.
  • D. It applies a penalty only if the token has appeared more than twice.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
A presence penalty in LLMs (including OCI's service) reduces the probability of tokens that have already appeared in the output, applying the penalty each time they reoccur after their first use. This discourages repetition, making Option D correct. Option A is false, as penalties depend on prior appearance, not uniform application. Option B is the opposite-penalizing unused tokens isn't the goal. Option C is incorrect, as the penalty isn't threshold-based (e.g., more than twice) but applied per reoccurrence. This enhances output diversity.
OCI 2025 Generative AI documentation likely details presence penalty under generation parameters.


NEW QUESTION # 53
What is the function of "Prompts" in the chatbot system?

  • A. They handle the chatbot's memory and recall abilities.
  • B. They are responsible for the underlying mechanics of the chatbot.
  • C. They are used to initiate and guide the chatbot's responses.
  • D. They store the chatbot's linguistic knowledge.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompts in a chatbot system are inputs provided to the LLM to initiate and steer its responses, often including instructions, context, or examples. They shape the chatbot's behavior without altering its core mechanics, making Option B correct. Option A is false, as knowledge is stored in the model's parameters. Option C relates to the model's architecture, not prompts. Option D pertains to memory systems, not prompts directly. Prompts are key for effective interaction.
OCI 2025 Generative AI documentation likely covers prompts under chatbot design or inference sections.


NEW QUESTION # 54
What is the purpose of memory in the LangChain framework?

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

Answer: D

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 # 55
What is the purpose of Retrieval Augmented Generation (RAG) in text generation?

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

Answer: D

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 # 56
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