1Z0-1127-24 TEST RESULT - 1Z0-1127-24 EXAM SYLLABUS

1z0-1127-24 Test Result - 1z0-1127-24 Exam Syllabus

1z0-1127-24 Test Result - 1z0-1127-24 Exam Syllabus

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1z0-1127-24 Test Simulates: Oracle Cloud Infrastructure 2024 Generative AI Professional & 1z0-1127-24 Study Guide

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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
  • 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 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 (Q62-Q67):

NEW QUESTION # 62
What is the primary purpose of LangSmith Tracing?

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

Answer: A

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 # 63
What issue might arise from using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service?

  • A. Model Drift
  • B. Underfitting
  • C. Overfilling
  • D. Data Leakage

Answer: B

Explanation:
Using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service might result in underfitting. Underfitting occurs when a model is too simplistic to capture the underlying patterns in the data, leading to poor performance on both training and validation data. This is particularly problematic with small data sets because there may not be enough information for the model to learn the necessary patterns and relationships.
Reference
Articles on machine learning challenges with small data sets
Technical documentation on fine-tuning models in OCI


NEW QUESTION # 64
Which statement is true about string prompt templates and their capability regarding variables?

  • A. They support any number of variables, including the possibility of having none.
  • B. They can only support a single variable at a time.
  • C. They are unable to use any variables.
  • D. They require a minimum of two variables to function properly.

Answer: A

Explanation:
A string prompt template is a mechanism used to structure prompts dynamically by inserting variables. These templates are commonly used in LLM-powered applications like chatbots, text generation, and automation tools.
How Prompt Templates Handle Variables:
They support an unlimited number of variables or can work without any variables.
Variables are typically denoted by placeholders such as {variable_name} or {{variable_name}} in frameworks like LangChain or Oracle AI.
Users can dynamically populate these placeholders to generate different prompts without rewriting the entire template.
Example of a Prompt Template:
Without variables: "What is the capital of France?"
With one variable: "What is the capital of {country}?"
With multiple variables: "What is the capital of {country}, and what language is spoken there?" Why Other Options Are Incorrect:
(B) is false because templates can work with one or no variables.
(C) is false because templates rely on variables for dynamic input.
(D) is false because templates can handle multiple placeholders.
???? Oracle Generative AI Reference:
Oracle integrates prompt engineering capabilities into its AI platforms, allowing developers to create scalable, reusable prompts for various AI applications.


NEW QUESTION # 65
What do prompt templates use for templating in language model applications?

  • A. Python's str.format syntax
  • B. Python's class and object structures
  • C. Python's list comprehension syntax
  • D. Python's lambda functions

Answer: A

Explanation:
Prompt templates are structured text-based input patterns that include placeholders for dynamic variable substitution. These templates help generate prompts for LLMs (Large Language Models) in a systematic and reusable way.
Prompt Template Example using str.format():
template = "What is the capital of {country}?"
formatted_prompt = template.format(country="France")
print(formatted_prompt) # Output: "What is the capital of France?"
Why str.format() is Used:
It allows dynamic insertion of variables.
It is flexible and widely supported in Python-based AI frameworks.
Used in LangChain, OpenAI API, and Oracle AI applications.
Why Other Options Are Incorrect:
(A) Lambda functions are used for anonymous function execution, not string templating.
(C) List comprehensions are used for iterating over lists, not text formatting.
(D) Class and object structures define OOP models, not LLM prompt templates.
???? Oracle Generative AI Reference:
Oracle AI frameworks use Python's str.format() and f-strings for LLM prompt engineering and AI-driven workflow automation.


NEW QUESTION # 66
In LangChain, which retriever search type is used to balance between relevancy and diversity?

  • A. similarity_score_threshold
  • B. mmr
  • C. top k
  • D. similarity

Answer: D


NEW QUESTION # 67
......

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