TEST 1Z0-1127-24 QUESTIONS VCE, 1Z0-1127-24 TRUSTWORTHY PRACTICE

Test 1z0-1127-24 Questions Vce, 1z0-1127-24 Trustworthy Practice

Test 1z0-1127-24 Questions Vce, 1z0-1127-24 Trustworthy Practice

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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
  • 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
  • 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.

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q43-Q48):

NEW QUESTION # 43
In the context of generating text with a Large Language Model (LLM), what does the process of greedy decoding entail?

  • A. Selecting a random word from the entire vocabulary at each step
  • B. Choosing the word with the highest probability at each step of decoding
  • C. Using a weighted random selection based on a modulated distribution
  • D. Picking a word based on its position in a sentence structure

Answer: B

Explanation:
Greedy Decoding is a simple and fast text generation strategy where the model always selects the word with the highest probability at each step.
How Greedy Decoding Works:
At each step of text generation, the model picks the most probable next word.
No consideration is given to long-term coherence or fluency.
This method can lead to repetitive and suboptimal outputs due to the lack of exploration.
Limitations of Greedy Decoding:
May miss optimal sentence structures because it only considers the next word, not the full sequence.
Less diversity in generated text, as it follows the highest-probability path rigidly.
Better alternatives exist: Beam Search, Top-k Sampling, and Temperature Scaling provide more refined results.
Why Other Options Are Incorrect:
(A) is incorrect because greedy decoding does not select random words.
(C) is incorrect because word choice is based on probability, not sentence structure.
(D) is incorrect because weighted random selection is used in sampling methods like Top-k or Top-p (nucleus sampling).
???? Oracle Generative AI Reference:
Oracle AI incorporates Greedy Decoding, Beam Search, and Stochastic Sampling in its text generation models to optimize for accuracy and diversity.


NEW QUESTION # 44
What do prompt templates use for templating in language model applications?

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

Answer: D

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 # 45
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

  • A. Top p limits token selection based on the sum of their probabilities.
  • B. Top p determines the maximum number of tokens per response.
  • C. Top p selects tokens from the "Top k' tokens sorted by probability.
  • D. Top p assigns penalties to frequently occurring tokens.

Answer: A


NEW QUESTION # 46
What is the primary purpose of LangSmith Tracing?

  • A. To analyze the reasoning process of language
  • B. To generate test cases for language models
  • C. To monitor the performance of language models
  • 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 # 47
Which is NOT a built-in memory type in LangChain?

  • A. Conversation Summary Memory
  • B. Conversation ImgeMemory
  • C. Conversation Token Buffer Memory
  • D. Conversation Buffer Memory

Answer: B


NEW QUESTION # 48
......

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