2025 1Z0-1127-25 KNOWLEDGE POINTS | HIGH-QUALITY 1Z0-1127-25 VALID TEST PREPARATION: ORACLE CLOUD INFRASTRUCTURE 2025 GENERATIVE AI PROFESSIONAL

2025 1Z0-1127-25 Knowledge Points | High-quality 1Z0-1127-25 Valid Test Preparation: Oracle Cloud Infrastructure 2025 Generative AI Professional

2025 1Z0-1127-25 Knowledge Points | High-quality 1Z0-1127-25 Valid Test Preparation: Oracle Cloud Infrastructure 2025 Generative AI Professional

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q35-Q40):

NEW QUESTION # 35
What is the purpose of memory in the LangChain framework?

  • A. To perform complex calculations unrelated to user interaction
  • B. To retrieve user input and provide real-time output only
  • C. To act as a static database for storing permanent records
  • 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 # 36
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

  • A. Stored in an unencrypted form in Object Storage
  • B. Stored in Object Storage encrypted by default
  • C. Shared among multiple customers for efficiency
  • D. Stored in Key Management service

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In OCI, fine-tuned models are stored in Object Storage, encrypted by default, ensuring privacy and security per cloud best practices-Option B is correct. Option A (shared) violates privacy. Option C (unencrypted) contradicts security standards. Option D (Key Management) stores keys, not models. Encryption protects customer data.
OCI 2025 Generative AI documentation likely details storage security under fine-tuning workflows.


NEW QUESTION # 37
What does the Ranker do in a text generation system?

  • A. It interacts with the user to understand the query better.
  • B. It generates the final text based on the user's query.
  • C. It sources information from databases to use in text generation.
  • D. It evaluates and prioritizes the information retrieved by the Retriever.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In systems like RAG, the Ranker evaluates and sorts the information retrieved by the Retriever (e.g., documents or snippets) based on relevance to the query, ensuring the most pertinent data is passed to the Generator. This makes Option C correct. Option A is the Generator's role. Option B describes the Retriever. Option D is unrelated, as the Ranker doesn't interact with users but processes retrieved data. The Ranker enhances output quality by prioritizing relevant content.
OCI 2025 Generative AI documentation likely details the Ranker under RAG pipeline components.


NEW QUESTION # 38
What does a higher number assigned to a token signify in the "Show Likelihoods" feature of the language model token generation?

  • A. The token is unrelated to the current token and will not be used.
  • B. The token will be the only one considered in the next generation step.
  • C. The token is more likely to follow the current token.
  • D. The token is less likely to follow the current token.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In "Show Likelihoods," a higher number (probability score) indicates a token's greater likelihood of following the current token, reflecting the model's prediction confidence-Option B is correct. Option A (less likely) is the opposite. Option C (unrelated) misinterprets-likelihood ties tokens contextually. Option D (only one) assumes greedy decoding, not the feature's purpose. This helps users understand model preferences.
OCI 2025 Generative AI documentation likely explains "Show Likelihoods" under token generation insights.


NEW QUESTION # 39
In which scenario is soft prompting especially appropriate compared to other training styles?

  • A. When the model needs to be adapted to perform well in a different domain it was not originally trained on.
  • B. When there is a significant amount of labeled, task-specific data available.
  • C. When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training.
  • D. When the model requires continued pre-training on unlabeled data.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Soft prompting (e.g., prompt tuning) involves adding trainable parameters (soft prompts) to an LLM's input while keeping the model's weights frozen, adapting it to tasks without task-specific retraining. This is efficient when fine-tuning or large datasets aren't feasible, making Option C correct. Option A suits full fine-tuning, not soft prompting, which avoids extensive labeled data needs. Option B could apply, but domain adaptation often requires more than soft prompting (e.g., fine-tuning). Option D describes continued pretraining, not soft prompting. Soft prompting excels in low-resource customization.
OCI 2025 Generative AI documentation likely discusses soft prompting under parameter-efficient methods.


NEW QUESTION # 40
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