1Z0-1127-25 QUESTIONS PDF - YOUR POWERFUL WEAPON TO PASS ORACLE CLOUD INFRASTRUCTURE 2025 GENERATIVE AI PROFESSIONAL

1Z0-1127-25 Questions Pdf - Your Powerful Weapon to Pass Oracle Cloud Infrastructure 2025 Generative AI Professional

1Z0-1127-25 Questions Pdf - Your Powerful Weapon to Pass Oracle Cloud Infrastructure 2025 Generative AI Professional

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Oracle Cloud Infrastructure 2025 Generative AI Professional Sample Questions (Q63-Q68):

NEW QUESTION # 63
What is prompt engineering in the context of Large Language Models (LLMs)?

  • A. Training the model on a large dataset
  • B. Adding more layers to the neural network
  • C. Adjusting the hyperparameters of the model
  • D. Iteratively refining the ask to elicit a desired response

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Prompt engineering involves crafting and refining input prompts to guide an LLM to produce desired outputs without altering its internal structure or parameters. It's an iterative process that leverages the model's pre-trained knowledge, making Option A correct. Option B is unrelated, as adding layers pertains to model architecture design, not prompting. Option C refers to hyperparameter tuning (e.g., temperature), not prompt engineering. Option D describes pretraining or fine-tuning, not prompt engineering.
OCI 2025 Generative AI documentation likely covers prompt engineering in sections on model interaction or inference.


NEW QUESTION # 64
What is the main advantage of using few-shot model prompting to customize a Large Language Model (LLM)?

  • A. It eliminates the need for any training or computational resources.
  • B. It significantly reduces the latency for each model request.
  • C. It allows the LLM to access a larger dataset.
  • D. It provides examples in the prompt to guide the LLM to better performance with no training cost.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Few-shot prompting involves providing a few examples in the prompt to guide the LLM's behavior, leveraging its in-context learning ability without requiring retraining or additional computational resources. This makes Option C correct. Option A is false, as few-shot prompting doesn't expand the dataset. Option B overstates the case, as inference still requires resources. Option D is incorrect, as latency isn't significantly affected by few-shot prompting.
OCI 2025 Generative AI documentation likely highlights few-shot prompting in sections on efficient customization.


NEW QUESTION # 65
Which is a distinctive feature of GPUs in Dedicated AI Clusters used for generative AI tasks?

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

Answer: A

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 # 66
What is the purpose of Retrievers in LangChain?

  • A. To retrieve relevant information from knowledge bases
  • B. To combine multiple components into a single pipeline
  • C. To train Large Language Models
  • D. To break down complex tasks into smaller steps

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Retrievers in LangChain fetch relevant information (e.g., documents, embeddings) from external knowledge bases (like vector stores) to provide context for LLM responses, especially in RAG setups. This makes Option B correct. Option A (training) is unrelated-Retrievers operate at inference. Option C (task breakdown) pertains to prompting techniques, not retrieval. Option D (pipeline combination) describes chains, not Retrievers specifically. Retrievers enhance context awareness.
OCI 2025 Generative AI documentation likely defines Retrievers under LangChain components.


NEW QUESTION # 67
What does accuracy measure in the context of fine-tuning results for a generative model?

  • A. The depth of the neural network layers used in the model
  • B. How many predictions the model made correctly out of all the predictions in an evaluation
  • C. The proportion of incorrect predictions made by the model during an evaluation
  • D. The number of predictions a model makes, regardless of whether they are correct or incorrect

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation=
Accuracy in fine-tuning measures the proportion of correct predictions (e.g., matching expected outputs) out of all predictions made during evaluation, reflecting model performance-Option C is correct. Option A (total predictions) ignores correctness. Option B (incorrect proportion) is the inverse-error rate. Option D (layer depth) is unrelated to accuracy. Accuracy is a standard metric for generative tasks.OCI 2025 Generative AI documentation likely defines accuracy under fine-tuning evaluation metrics.


NEW QUESTION # 68
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