1Z0-1127-24 TEST DURATION & 1Z0-1127-24 VALID TEST LABS

1z0-1127-24 Test Duration & 1z0-1127-24 Valid Test Labs

1z0-1127-24 Test Duration & 1z0-1127-24 Valid Test Labs

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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 (Q21-Q26):

NEW QUESTION # 21
How are documents usually evaluated in the simplest form of keyword-based search?

  • A. By the complexity of language used in the documents
  • B. Based on the number of images and videos contained in the documents
  • C. According to the length of the documents
  • D. Based on the presence and frequency of the user-provided keywords

Answer: D

Explanation:
In the simplest form of keyword-based search, documents are evaluated based on keyword matching and term frequency. This approach does not account for context, semantics, or the meaning behind the words, but rather focuses on:
Presence of Keywords - If a document contains the search term, it is considered relevant.
Term Frequency (TF) - The more a keyword appears in a document, the higher the ranking in basic search algorithms.
Inverse Document Frequency (IDF) - Words that are common across many documents (e.g., "the," "is") are given less weight, while rare words are prioritized.
Boolean Matching - Some basic search engines support logical operators like AND, OR, and NOT to refine keyword searches.
Exact Match vs. Partial Match - Some systems prioritize exact keyword matches, while others allow partial or fuzzy matches.
???? Oracle Generative AI Reference:
Oracle has implemented semantic search and advanced AI-driven document search techniques in its cloud solutions, but traditional keyword-based search still forms the foundation of many enterprise search mechanisms.


NEW QUESTION # 22
Given the following prompts used with a Large Language Model, classify each as employing the Chain-of- Thought, Least-to-most, or Step-Back prompting technique.
L Calculate the total number of wheels needed for 3 cars. Cars have 4 wheels each. Then, use the total number of wheels to determine how many sets of wheels we can buy with $200 if one set (4 wheels) costs $50.
2. Solve a complex math problem by first identifying the formula needed, and then solve a simpler version of the problem before tackling the full question.
3. To understand the impact of greenhouse gases on climate change, let's start by defining what greenhouse gases are. Next, well explore how they trap heat in the Earths atmosphere.

  • A. 1:Chain-of-Thought ,2:Step-Back, 3:Least-to most
  • B. 1:Least-to-most, 2 Chain-of-Thought, 3:Step-Back
  • C. 1:Step-Back, 2:Chain-of-Thought, 3:Least-to-most
  • D. 1:Chain-of-throught, 2: Least-to-most, 3:Step-Back

Answer: C


NEW QUESTION # 23
How does the architecture of dedicated Al clusters contribute to minimizing GPU memory overhead forT- Few fine-tuned model inference?

  • A. By allocating separate GPUS for each model instance
  • B. By loading the entire model into G PU memory for efficient processing
  • C. By sharing base model weights across multiple fine-tuned model's on the same group of GPUs
  • D. By optimizing GPIJ memory utilization for each model's unique para

Answer: C


NEW QUESTION # 24
Which is a cost-related benefit of using vector databases with Large Language Models (LLMs)?

  • A. They increase the cost due to the need for real- time updates.
  • B. They are more expensive but provide higher quality data.
  • C. They offer real-time updated knowledge bases and are cheaper than fine-tuned LLMs.
  • D. They require frequent manual updates, which increase operational costs.

Answer: C

Explanation:
Using vector databases with Large Language Models (LLMs) offers cost-related benefits, particularly by providing real-time updated knowledge bases. This approach can be more cost-effective than fine-tuning LLMs frequently, as vector databases allow for the dynamic retrieval of information without the need for constant retraining. This reduces operational costs while maintaining access to up-to-date data.
Reference
Articles on the cost efficiency of vector databases
Research on integrating vector databases with LLMs for real-time updates


NEW QUESTION # 25
What is the purpose of the "stop sequence" parameter in the OCI Generative AI Generation models?

  • A. It com rob the randomness of the model* output, affecting its creativity.
  • B. It specifies a string that tells the model to stop generating more content
  • C. It determines the maximum number of tokens the model can generate per response.
  • D. It assigns a penalty to frequently occurring tokens to reduce repetitive text.

Answer: B

Explanation:
The "stop sequence" parameter in the OCI Generative AI Generation models is used to specify a string that signals the model to stop generating further content. When the model encounters this string during the generation process, it terminates the response. This parameter is useful for controlling the length and content of the generated text, ensuring that the output meets specific requirements or constraints.
Reference
OCI Generative AI service documentation
General principles of sequence generation in AI models


NEW QUESTION # 26
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