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Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic	Details
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.

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q60-Q65):

NEW QUESTION # 60
Which statement is true about the "Top p" parameter of the OCI Generative AI Generation models?

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

Answer: D

NEW QUESTION # 61
In the simplified workflow for managing and querying vector data, what is the role of indexing?

A. To convert vectors into a nonindexed format for easier retrieval
B. To map vectors to a data structure for faster searching, enabling efficient retrieval
C. To categorize vectors based on their originating data type (text, images, audio)
D. To compress vector data for minimized storage usage

Answer: B

Explanation:
Vector indexing plays a crucial role in vector search and retrieval systems, particularly in AI-driven databases. The key functions of vector indexing include:
Efficient Search and Retrieval - Vector indexing structures (such as HNSW, FAISS, or Annoy) help organize vector embeddings to enable fast retrieval of similar vectors.
Mapping to Searchable Data Structures - The process involves creating indexes that efficiently store and map vectors, reducing computational overhead when searching for similar embeddings.
Handling High-Dimensional Data - Since vector embeddings (used in NLP, image recognition, etc.) are often high-dimensional, indexing helps compress and cluster similar vectors, improving retrieval speed.
Used in Vector Databases - Many AI applications, including Oracle's AI-driven database solutions, use indexing techniques for faster similarity searches.
🔹 Oracle Generative AI Reference:
Oracle integrates vector search within its AI and database services, allowing enterprises to efficiently manage and retrieve vectorized data.

NEW QUESTION # 62
ow do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language?

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

Answer: A

NEW QUESTION # 63
What is the purpose of the "stop sequence" parameter in the OCI Generative AI Generation models?

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

Answer: A

NEW QUESTION # 64
What distinguishes the Cohere Embed v3 model from its predecessor in the OCI Generative AI service?

A. Improved retrievals for Retrieval Augmented Generation (RAG) systems
B. Emphasis on syntactic clustering of word embedding's
C. Support for tokenizing longer sentences
D. Capacity to translate text in over u languages

Answer: A

NEW QUESTION # 65
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High Pass-Rate Latest 1z0-1127-24 Exam Question - Pass 1z0-1127-24 Exam

Oracle 1z0-1127-24 Exam Syllabus Topics:

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q60-Q65):

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