1Z0-1127-24 MATERIALS | EXAM 1Z0-1127-24 PRACTICE

1z0-1127-24 Materials | Exam 1z0-1127-24 Practice

1z0-1127-24 Materials | Exam 1z0-1127-24 Practice

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The next step to do is to take Oracle 1z0-1127-24. These 1z0-1127-24 practice questions can help you measure your skill to see if it has already met the standard set by Oracle 1z0-1127-24. To optimize the effectiveness, We have made the 1z0-1127-24 Practice Test using the same format as the Oracle Cloud Infrastructure 2024 Generative AI Professional exam. All Oracle Exam Dumps questions appearing on the mock test are the ones we carefully predicted to appear on your upcoming exam.

Oracle 1z0-1127-24 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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.
Topic 3
  • 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.

>> 1z0-1127-24 Materials <<

Exam 1z0-1127-24 Practice & Exam Dumps 1z0-1127-24 Collection

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q52-Q57):

NEW QUESTION # 52
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 less likely to follow the current token.
  • D. The token is more likely to follow the current token.

Answer: D


NEW QUESTION # 53
What is the primary purpose of LangSmith Tracing?

  • A. To monitor the performance of language models
  • B. To generate test cases for language models
  • C. To analyze the reasoning process of language
  • D. To debug issues in language model outputs

Answer: D

Explanation:
The primary purpose of LangSmith Tracing is to debug issues in language model outputs. LangSmith Tracing allows developers to trace and analyze the sequence of operations and decisions made by the model during the generation process. This helps identify and resolve problems, ensuring the model's outputs are accurate and reliable.
Reference
LangSmith documentation on tracing and debugging
Tutorials on using tracing tools for language model development


NEW QUESTION # 54
Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?

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

Answer: C


NEW QUESTION # 55
What does accuracy measure in the context of fine-tuning results for a generative model?

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

Answer: D

Explanation:
Accuracy in machine learning measures the proportion of correct predictions made by a model relative to the total predictions during an evaluation.
How Accuracy is Calculated:

A higher accuracy indicates better model performance.
Used primarily in classification tasks, but it can also assess LLM fine-tuning results.
Why Other Options Are Incorrect:
(A) is incorrect because the number of neural network layers does not define accuracy.
(B) is incorrect because accuracy considers correctness, not just total predictions.
(D) is incorrect because accuracy measures correct predictions, not just incorrect ones.
???? Oracle Generative AI Reference:
Oracle AI assesses model fine-tuning performance using accuracy, loss, and perplexity to improve LLM capabilities.


NEW QUESTION # 56
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?

  • A. By restricting updates to only a specific croup of transformer Layers
  • B. By incorporating additional layers to the base model
  • C. By excluding transformer layers from the fine-tuning process entirely
  • D. By allowing updates across all layers of the model

Answer: A

Explanation:
The utilization of T-Few transformer layers contributes to the efficiency of the fine-tuning process by restricting updates to only a specific group of transformer layers. This selective updating approach allows the model to adapt to new data without the need to retrain all layers, thus saving computational resources and time. By focusing on the most relevant parts of the model, T-Few fine-tuning achieves efficient and effective performance improvements.
Reference
Research papers on T-Few fine-tuning techniques
Technical guides on optimizing transformer models


NEW QUESTION # 57
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