RELIABLE 1Z0-1127-24 EXAM PRACTICE | 1Z0-1127-24 CERTIFICATION EXAM

Reliable 1z0-1127-24 Exam Practice | 1z0-1127-24 Certification Exam

Reliable 1z0-1127-24 Exam Practice | 1z0-1127-24 Certification Exam

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Oracle 1z0-1127-24 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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.

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q42-Q47):

NEW QUESTION # 42
Given a block of code:
qa = Conversational Retrieval Chain, from 11m (11m, retriever-retv, memory-memory) when does a chain typically interact with memory during execution?

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

Answer: A

Explanation:
In a Conversational Retrieval Chain using LangChain, the chain typically interacts with memory at two key points: after the user input but before the chain execution, and again after the core logic but before the output is generated. This approach allows the system to update the memory with relevant context before executing the chain's main logic and then update the memory again with any new information or context gained during the execution before producing the final output.
Reference
LangChain documentation on Conversational Retrieval Chains
Technical guides on managing memory in conversational AI models


NEW QUESTION # 43
Which statement describes the difference between Top V and Top p" in selecting the next token in the OCI Generative AI Generation models?

  • A. Top k selects the next token based on its position in the list of probable tokens, whereas "Top p" selects based on the cumulative probability of the Top token.
  • B. Top K considers the sum of probabilities of the top tokens, whereas Top" selects from the Top k" tokens sorted by probability.
  • C. Top k and Top p" both select from the same set of tokens but use different methods to prioritize them based on frequency.
  • D. Top k and "Top p" are identical in their approach to token selection but differ in their application of penalties to tokens.

Answer: B


NEW QUESTION # 44
What does a cosine distance of 0 indicate about the relationship between two embeddings?

  • A. They are unrelated
  • B. They have the same magnitude
  • C. They are similar in direction
  • D. They are completely dissimilar

Answer: C


NEW QUESTION # 45
Which is a key characteristic of the annotation process used in T-Few fine-tuning?

  • A. T-Few fine-tuning requires manual annotation of input-output pain.
  • B. T-Few fine-tuning uses annotated data to adjust a fraction of model weights.
  • C. T- Few fine-tuning involves updating the weights of all layers in the model.
  • D. T-Few fine-tuning relies on unsupervised learning techniques for annotation.

Answer: D


NEW QUESTION # 46
What does "Loss" measure in the evaluation of OCI Generative AI fine-tuned models?
The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model

  • A. The improvement in accuracy achieved by the model during training on the user-uploaded data set
  • B. The percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation
  • C. The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model
  • D. The level of incorrectness in the models predictions, with lower values indicating better performance

Answer: D


NEW QUESTION # 47
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