CERTIFICATION 1Z0-1127-25 EXAM INFOR - TEST 1Z0-1127-25 QUESTIONS

Certification 1Z0-1127-25 Exam Infor - Test 1Z0-1127-25 Questions

Certification 1Z0-1127-25 Exam Infor - Test 1Z0-1127-25 Questions

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q13-Q18):

NEW QUESTION # 13
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

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

Answer: A

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 # 14
What does "k-shot prompting" refer to when using Large Language Models for task-specific applications?

  • A. Providing the exact k words in the prompt to guide the model's response
  • B. Limiting the model to only k possible outcomes or answers for a given task
  • C. The process of training the model on k different tasks simultaneously to improve its versatility
  • D. Explicitly providing k examples of the intended task in the prompt to guide the model's output

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
"k-shot prompting" (e.g., few-shot) involves providing k examples of a task in the prompt to guide the LLM's output via in-context learning, without additional training. This makes Option B correct. Option A (k words) misinterprets-examples, not word count, matter. Option C (training) confuses prompting with fine-tuning. Option D (k outcomes) is unrelated-k refers to examples, not limits. k-shot leverages pre-trained knowledge efficiently.
OCI 2025 Generative AI documentation likely covers k-shot prompting under prompt engineering techniques.


NEW QUESTION # 15
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours are required for fine-tuning if the cluster is active for 10 hours?

  • A. 30 unit hours
  • B. 25 unit hours
  • C. 40 unit hours
  • D. 20 unit hours

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
In OCI, unit hours typically equal actual hours of cluster activity unless specified otherwise (e.g., per GPU scaling). For 10 hours of activity, it's 10 hours × 1 unit/hour = 10 unit hours, but options suggest a multiplier (common in cloud pricing). Assuming a standard 2-unit/hour rate (e.g., for GPU clusters), it's 10 × 2 = 20 unit hours-Option C fits best. Options A, B, and D imply inconsistent rates (2.5, 4, 3).
OCI 2025 Generative AI documentation likely specifies unit hour rates under DedicatedAI Cluster pricing.


NEW QUESTION # 16
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

  • A. Translation models
  • B. Embedding models
  • C. Generation models
  • D. Summarization models

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
OCI Generative AI typically offers pretrained models for summarization (A), generation (B), and embeddings (D), aligning with common generative tasks. Translation models (C) are less emphasized in generative AI services, often handled by specialized NLP platforms, making C the NOT category. While possible, translation isn't a core OCI generative focus based on standard offerings.
OCI 2025 Generative AI documentation likely lists model categories under pretrained options.


NEW QUESTION # 17
Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?

  • A. It updates all the weights of the model uniformly.
  • B. It increases the training time as compared to Vanilla fine-tuning.
  • C. It does not update any weights but restructures the model architecture.
  • D. It selectively updates only a fraction of the model's weights.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
T-Few fine-tuning, a Parameter-Efficient Fine-Tuning (PEFT) method, updates only a small fraction of an LLM's weights, reducing computational cost and overfitting risk compared to Vanilla fine-tuning (all weights). This makes Option C correct. Option A describes Vanilla fine-tuning. Option B is false-T-Few updates weights, not architecture. Option D is incorrect-T-Few typically reduces training time. T-Few optimizes efficiency.
OCI 2025 Generative AI documentation likely highlights T-Few under fine-tuning options.


NEW QUESTION # 18
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