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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 (Q36-Q41):

NEW QUESTION # 36
Which is a key characteristic of the annotation process used in T-Few fine-tuning?

A. T-Few fine-tuning uses annotated data to adjust a fraction of model weights.
B. T-Few fine-tuning requires manual annotation of input-output pain.
C. T-Few fine-tuning relies on unsupervised learning techniques for annotation.
D. T- Few fine-tuning involves updating the weights of all layers in the model.

Answer: A

Explanation:
T-Few fine-tuning is a technique that uses annotated data to adjust only a fraction of the model's weights. This method aims to efficiently fine-tune the model with a limited amount of data and computational resources. By updating only a small subset of the parameters, T-Few fine-tuning can achieve significant performance improvements without the need for extensive training data or computational power.
Reference
Research papers on parameter-efficient fine-tuning techniques
Technical guides on T-Few fine-tuning methodology

NEW QUESTION # 37
Which statement is true about string prompt templates and their capability regarding variables?

A. They support any number of variables, including the possibility of having none.
B. They are unable to use any variables.
C. They require a minimum of two variables to function properly.
D. They can only support a single variable at a time.

Answer: A

Explanation:
A string prompt template is a mechanism used to structure prompts dynamically by inserting variables. These templates are commonly used in LLM-powered applications like chatbots, text generation, and automation tools.
How Prompt Templates Handle Variables:
They support an unlimited number of variables or can work without any variables.
Variables are typically denoted by placeholders such as {variable_name} or {{variable_name}} in frameworks like LangChain or Oracle AI.
Users can dynamically populate these placeholders to generate different prompts without rewriting the entire template.
Example of a Prompt Template:
Without variables: "What is the capital of France?"
With one variable: "What is the capital of {country}?"
With multiple variables: "What is the capital of {country}, and what language is spoken there?" Why Other Options Are Incorrect:
(B) is false because templates can work with one or no variables.
(C) is false because templates rely on variables for dynamic input.
(D) is false because templates can handle multiple placeholders.
🔹 Oracle Generative AI Reference:
Oracle integrates prompt engineering capabilities into its AI platforms, allowing developers to create scalable, reusable prompts for various AI applications.

NEW QUESTION # 38
Which is NOT a built-in memory type in LangChain?

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

Answer: A

NEW QUESTION # 39
How do Dot Product and Cosine Distance differ in their application to comparing text embeddings in natural language?

A. Dot Product is used for semantic analysis, whereas Cosine Distance is used for syntactic comparisons.
B. Dot Product measures the magnitude and direction vectors, whereas Cosine Distance focuses on the orientation regardless of magnitude.
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: B

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 # 40
Why is normalization of vectors important before indexing in a hybrid search system?

A. It standardizes vector lengths for meaningful comparison using metrics such as Cosine Similarity.
B. It converts all sparse vectors to dense vectors.
C. It ensures that all vectors represent keywords only.
D. It significantly reduces the size of the database.

Answer: A

Explanation:
Normalization of vectors is crucial in a hybrid search system because it standardizes the lengths of vectors, ensuring they have a unit norm. This standardization is essential for meaningful comparison using similarity metrics such as Cosine Similarity. Without normalization, the magnitudes of vectors could skew the similarity scores, leading to inaccurate comparisons and search results. Normalizing vectors ensures that the similarity measure focuses purely on the direction of the vectors rather than their magnitude.
Reference
Research papers on vector normalization in information retrieval
Technical documentation on hybrid search systems

NEW QUESTION # 41
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1z0-1127-24 Latest Test Simulations | 1z0-1127-24 Test Discount Voucher

Oracle 1z0-1127-24 Exam Syllabus Topics:

Authoritative 1z0-1127-24 Latest Test Simulations Supply you Trusted Test Discount Voucher for 1z0-1127-24: Oracle Cloud Infrastructure 2024 Generative AI Professional to Prepare easily

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q36-Q41):

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