RELIABLE 1Z0-1127-24 - RELIABLE ORACLE CLOUD INFRASTRUCTURE 2024 GENERATIVE AI PROFESSIONAL REAL TEST

Reliable 1z0-1127-24 - Reliable Oracle Cloud Infrastructure 2024 Generative AI Professional Real Test

Reliable 1z0-1127-24 - Reliable Oracle Cloud Infrastructure 2024 Generative AI Professional Real Test

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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
  • 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
  • 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.

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q59-Q64):

NEW QUESTION # 59
Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?

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

Answer: D

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 # 60
What is the primary function of the "temperature" parameter in the OCI Generative AI Generation models?

  • A. Controls the randomness of the model's output, affecting its creativity
  • B. Determines the maximum number of tokens the model can generate per response
  • C. Assigns a penalty to tokens that have already appeared in the preceding text
  • D. Specifies a string that tells the model to stop generating more content

Answer: A


NEW QUESTION # 61
How are fine-tuned customer models stored to enable strong data privacy and security in the OCI Generative AI service?

  • A. Stored in Object Storage encrypted by default
  • B. Stored in Key Management service
  • C. Shared among multiple customers for efficiency
  • D. Stored in an unencrypted form in Object Storage

Answer: A


NEW QUESTION # 62
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. Picking a word based on its position in a sentence structure
  • C. Choosing the word with the highest probability at each step of decoding
  • D. Using a weighted random selection based on a modulated distribution

Answer: C

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 # 63
How does a presence penalty function in language model generation?

  • A. It penalizes all tokens equally, regardless of how often they have appeared.
  • B. It penalizes only tokens that have never appeared in the text before.
  • C. It penalizes a token each time it appears after the first occurrence.
  • D. It applies a penalty only if the token has appeared more than twice.

Answer: C

Explanation:
A presence penalty is a mechanism used in language model generation to discourage repetition of words or phrases in generated text. This is crucial for improving diversity in AI-generated responses.
How It Works:
The presence penalty increases the loss associated with words that have already appeared in the output.
The model is less likely to generate the same word multiple times, leading to more diverse responses.
Unlike frequency penalties, which increase with repeated occurrences, presence penalties apply as soon as a word appears.
Key Use Cases:
Avoiding redundant phrases in AI-generated text.
Enhancing creative writing applications where repetitive wording is undesirable.
Making chatbot conversations more engaging and natural.
???? Oracle Generative AI Reference:
Oracle's generative AI models implement presence and frequency penalties as part of their fine-tuning and model inference processes to balance text coherence and diversity.


NEW QUESTION # 64
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