1Z0-1127-25 EXAM PASSING SCORE - 1Z0-1127-25 TEST PASSING SCORE

1Z0-1127-25 Exam Passing Score - 1Z0-1127-25 Test Passing Score

1Z0-1127-25 Exam Passing Score - 1Z0-1127-25 Test Passing Score

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q12-Q17):

NEW QUESTION # 12
What is the characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

  • A. It selectively updates only a fraction of weights to reduce the number of parameters.
  • B. It selectively updates only a fraction of weights to reduce computational load and avoid overfitting.
  • C. It updates all the weights of the model uniformly.
  • D. It increases the training time as compared to Vanilla fine-tuning.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few fine-tuning (a Parameter-Efficient Fine-Tuning method) updates a small subset of the model's weights, reducing computational cost and mitigating overfitting compared to Vanilla fine-tuning, which updates all weights. This makes Option C correct. Option A describes Vanilla fine-tuning, not T-Few. Option B is incomplete, as it omits the overfitting benefit. Option D is false, as T-Few typically reduces training time due to fewer updates. T-Few balances efficiency and performance.
OCI 2025 Generative AI documentation likely describes T-Few under fine-tuningoptions.


NEW QUESTION # 13
Which is a distinctive feature of GPUs in Dedicated AI Clusters used for generative AI tasks?

  • A. GPUs are shared with other customers to maximize resource utilization.
  • B. Each customer's GPUs are connected via a public Internet network for ease of access.
  • C. GPUs are used exclusively for storing large datasets, not for computation.
  • D. The GPUs allocated for a customer's generative AI tasks are isolated from other GPUs.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In Dedicated AI Clusters (e.g., in OCI), GPUs are allocated exclusively to a customer for their generative AI tasks, ensuring isolation for security, performance, and privacy. This makes Option B correct. Option A describes shared resources, not dedicated clusters. Option C is false, as GPUs are for computation, not storage. Option D is incorrect, as public Internet connections would compromise security and efficiency.
OCI 2025 Generative AI documentation likely details GPU isolation under DedicatedAI Clusters.


NEW QUESTION # 14
When does a chain typically interact with memory in a run within the LangChain framework?

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

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In LangChain, a chain interacts with memory after receiving user input (to retrieve context) but before execution (to inform processing), and again after core logic (to update memory) but before output (to maintain state). This makes Option C correct. Option A misses pre-execution context. Option B misplaces timing. Option D overstates-interaction is at specific stages, not continuous. Memory ensures context-aware responses.
OCI 2025 Generative AI documentation likely details memory interaction under LangChain chain execution.


NEW QUESTION # 15
When does a chain typically interact with memory in a run within the LangChain framework?

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

Answer: D

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 # 16
What happens if a period (.) is used as a stop sequence in text generation?

  • A. The model stops generating text after it reaches the end of the first sentence, even if the token limit is much higher.
  • B. The model generates additional sentences to complete the paragraph.
  • C. The model stops generating text after it reaches the end of the current paragraph.
  • D. The model ignores periods and continues generating text until it reaches the token limit.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
A stop sequence in text generation (e.g., a period) instructs the model to halt generation once it encounters that token, regardless of the token limit. If set to a period, the model stops after the first sentence ends, making Option D correct. Option A is false, as stop sequences are enforced. Option B contradicts the stop sequence's purpose. Option C is incorrect, as it stops at the sentence level, not paragraph.
OCI 2025 Generative AI documentation likely explains stop sequences under text generation parameters.


NEW QUESTION # 17
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