PASS GUARANTEED QUIZ ORACLE - HIGH PASS-RATE REAL 1Z0-1127-24 EXAM ANSWERS

Pass Guaranteed Quiz Oracle - High Pass-Rate Real 1z0-1127-24 Exam Answers

Pass Guaranteed Quiz Oracle - High Pass-Rate Real 1z0-1127-24 Exam Answers

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Success in the test of the Oracle Cloud Infrastructure 2024 Generative AI Professional (1z0-1127-24) certification proves your technical knowledge and skills. The Oracle Cloud Infrastructure 2024 Generative AI Professional (1z0-1127-24) exam credential paves the way toward landing high-paying jobs or promotions in your organization. Many people who attempt the Oracle Cloud Infrastructure 2024 Generative AI Professional (1z0-1127-24) exam questions don't find updated practice questions. Due to this they don't prepare as per the current Oracle Cloud Infrastructure 2024 Generative AI Professional (1z0-1127-24) examination content and fail the final test. Failure in the Oracle Cloud Infrastructure 2024 Generative AI Professional (1z0-1127-24) exam dumps wastes the money and time of applicants.

Oracle 1z0-1127-24 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Fundamentals of Large Language Models (LLMs): For AI developers and Cloud Architects, this topic discusses LLM architectures and LLM fine-tuning. Additionally, it focuses on prompts for LLMs and fundamentals of code models.
Topic 2
  • Using OCI Generative AI Service: For AI Specialists, this section covers dedicated AI clusters for fine-tuning and inference. The topic also focuses on the fundamentals of OCI Generative AI service, foundational models for Generation, Summarization, and Embedding.
Topic 3
  • Building an LLM Application with OCI Generative AI Service: For AI Engineers, this section covers Retrieval Augmented Generation (RAG) concepts, vector database concepts, and semantic search concepts. It also focuses on deploying an LLM, tracing and evaluating an LLM, and building an LLM application with RAG and LangChain.

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q12-Q17):

NEW QUESTION # 12
How does the architecture of dedicated Al clusters contribute to minimizing GPU memory overhead forT- Few fine-tuned model inference?

  • A. By allocating separate GPUS for each model instance
  • B. By optimizing GPIJ memory utilization for each model's unique para
  • C. By sharing base model weights across multiple fine-tuned model's on the same group of GPUs
  • D. By loading the entire model into G PU memory for efficient processing

Answer: C

Explanation:
The architecture of dedicated AI clusters contributes to minimizing GPU memory overhead for fine-tuned model inference by sharing base model weights across multiple fine-tuned models on the same group of GPUs. This approach allows different fine-tuned models to leverage the shared base model weights, reducing the memory requirements and enabling efficient use of GPU resources. By not duplicating the base model weights for each fine-tuned model, the system can handle more models simultaneously with lower memory overhead.
Reference
Technical documentation on AI cluster architectures
Research articles on optimizing GPU memory utilization in model inference


NEW QUESTION # 13
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours arc required for fine-tuning if the cluster is active for 10 hours?

  • A. 30 unit hours
  • B. 40 unit hours
  • C. 15 unit hours
  • D. 10 unit hours

Answer: D

Explanation:
When you create a fine-tuning dedicated AI cluster and it is active for 10 hours, the number of unit hours required for fine-tuning is equal to the duration for which the cluster is active. Therefore, if the cluster is active for 10 hours, it requires 10 unit hours. This calculation assumes that the unit hour measurement directly corresponds to the active time of the cluster.
Reference
OCI documentation on unit hours and fine-tuning processes
Usage guidelines for dedicated AI clusters in OCI


NEW QUESTION # 14
When should you use the T-Few fine-tuning method for training a model?

  • A. For models that require their own hosting dedicated Al duster
  • B. For data sets with a few thousand samples or less
  • C. For complicated semantical undemanding improvement
  • D. For data sets with hundreds of thousands to millions of samples

Answer: D


NEW QUESTION # 15
Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?

  • A. In context Learning
  • B. Chain-of-Through
  • C. Step-Bock Prompting
  • D. Least to most Prompting

Answer: B

Explanation:
Chain-of-Thought prompting involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response. This technique helps the model articulate its thought process and reasoning, leading to more transparent and understandable outputs. By breaking down the problem into smaller, logical steps, the model can provide more accurate and detailed responses.
Reference
Research articles on Chain-of-Thought prompting
Technical guides on enhancing model transparency and reasoning with intermediate steps


NEW QUESTION # 16
Which role docs a "model end point" serve in the inference workflow of the OCI Generative AI service?

  • A. Evaluates the performance metrics of the custom model
  • B. Serves as a designated point for user requests and model responses
  • C. Updates the weights of the base model during the fine-tuning process
  • D. Hosts the training data for fine-tuning custom model

Answer: B

Explanation:
In the inference workflow of the OCI Generative AI service, a "model endpoint" is a critical component. It serves as a designated point for handling user requests and providing model responses. When users or applications send requests to the model endpoint, the endpoint processes these requests by passing them to the deployed model. The model then generates responses based on the input data, and these responses are returned to the user through the same endpoint. This setup facilitates efficient and scalable interaction with the AI model, ensuring that inference can be performed seamlessly and reliably.
Reference
Oracle Cloud Infrastructure (OCI) Generative AI service documentation
General principles of model deployment and inference in cloud services


NEW QUESTION # 17
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