PASS GUARANTEED ORACLE - 1Z0-1127-24 LATEST EXAM DISCOUNT VOUCHER

Pass Guaranteed Oracle - 1z0-1127-24 Latest Exam Discount Voucher

Pass Guaranteed Oracle - 1z0-1127-24 Latest Exam Discount Voucher

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1z0-1127-24 Official Cert Guide & Exam Questions 1z0-1127-24 Vce

Unlike many other learning materials, our Oracle Cloud Infrastructure 2024 Generative AI Professional guide torrent is specially designed to help people pass the exam in a more productive and time-saving way. On the other hand, 1z0-1127-24 exam study materials are aimed to help users make best use of their sporadic time by adopting flexible and safe study access. People always tend to neglect the great power of accumulation, thus the 1z0-1127-24 Certification guide can not only benefit one's learning process but also help people develop a good habit of preventing delays. Our 1z0-1127-24 exam questions will help you obtain the certification.

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.

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q50-Q55):

NEW QUESTION # 50
How does the Retrieval-Augmented Generation (RAG) Token technique differ from RAG Sequence when generating a model's response?

  • A. RAG Token does not use document retrieval but generates responses based on pre-existing knowledge only.
  • B. Unlike RAG Sequence, RAG Token generates the entire response at once without considering individual parts.
  • C. RAG Token retrieves documents oar/at the beginning of the response generation and uses those for the entire content
  • D. RAG Token retrieves relevant documents for each part of the response and constructs the answer incrementally.

Answer: C


NEW QUESTION # 51
Which is NOT a built-in memory type in LangChain?

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

Answer: B

Explanation:
In LangChain, "Conversation Image Memory" is not a built-in memory type. The built-in memory types in LangChain include:
Conversation Token Buffer Memory: This memory type stores a buffer of tokens from the conversation history.
Conversation Buffer Memory: This memory type retains a buffer of conversation history, typically in the form of text.
Conversation Summary Memory: This memory type summarizes the conversation history to keep track of key points and information.
These memory types help manage and utilize conversation history in various ways to enhance the performance of conversational models.
Reference
LangChain documentation on memory types
Technical guides on implementing memory in conversational AI systems


NEW QUESTION # 52
How does the structure of vector databases differ from traditional relational databases?

  • A. A vector database stores data in a linear or tabular format.
  • B. It is based on distances and similarities in a vector space.
  • C. It uses simple row-based data storage.
  • D. It is not optimized for high-dimensional spaces.

Answer: B

Explanation:
Vector databases are specialized database systems designed to store and retrieve high-dimensional vector embeddings. Unlike traditional relational databases (RDBMS), which organize data into tables with rows and columns, vector databases function using mathematical distances in a multi-dimensional vector space.
How Vector Databases Differ:
Optimized for High-Dimensional Spaces: Designed to efficiently search for similar embeddings in large AI-driven applications (e.g., recommendation systems, image search).
Similarity-Based Retrieval: Uses distance metrics such as cosine similarity, Euclidean distance, or Manhattan distance to find the closest vectors.
Indexing Techniques: Implements approximate nearest neighbor (ANN) algorithms to speed up searches.
Why Other Options Are Incorrect:
(A) is incorrect because vector databases are optimized for high-dimensional spaces.
(C) & (D) are incorrect because vector databases do not use row-based or tabular storage.
???? Oracle Generative AI Reference:
Oracle integrates vector databases into its AI and ML solutions, enabling efficient similarity searches and AI-driven applications.


NEW QUESTION # 53
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 level of incorrectness in the models predictions, with lower values indicating better performance
  • B. The difference between the accuracy of the model at the beginning of training and the accuracy of the deployed model
  • C. The improvement in accuracy achieved by the model during training on the user-uploaded data set
  • D. The percentage of incorrect predictions made by the model compared with the total number of predictions in the evaluation

Answer: A


NEW QUESTION # 54
How do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language?

  • A. Dot Product calculates the literal overlap of words, whereas Cosine Distance evaluates the stylistic similarity.
  • B. Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.
  • C. Dot Product assesses the overall similarity in content, whereas Cosine Distance measures topical relevance.
  • D. Dot Product measures the magnitude and direction vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude.

Answer: D

Explanation:
Dot Product and Cosine Distance are both metrics used to compare text embeddings, but they operate differently:
Dot Product: Measures the magnitude and direction of the vectors. It takes into account both the size (magnitude) and the angle (direction) between the vectors. This can result in higher similarity scores for longer vectors, even if they point in similar directions.
Cosine Distance: Focuses on the orientation of the vectors regardless of their magnitude. It measures the cosine of the angle between two vectors, which normalizes the vectors to unit length. This makes it a measure of the angle (or orientation) between the vectors, providing a similarity score that is independent of the vector lengths.
Reference
Research papers on text embedding comparison metrics
Technical documentation on vector similarity measures


NEW QUESTION # 55
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