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Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic
Details

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 (Q52-Q57):

NEW QUESTION # 52
In which scenario is soft prompting appropriate compared to other training styles?

A. When the model needs to be adapted to perform well in a domain on which it was not originally trained
B. When the model requires continued pretraining on unlabeled data
C. When there is a significant amount of labeled, task-specific data available
D. When there is a need to add learnable parameters to a Large Language Model (LLM) without task-specific training

Answer: D

Explanation:
Soft prompting is an efficient method for modifying LLM behavior without full retraining. Unlike fine-tuning, soft prompting adds learnable embeddings (soft prompts) to guide the model.
When Soft Prompting is Useful:
Enhances model behavior without full retraining.
Uses small trainable prompt tokens, avoiding large parameter updates.
Works well when labeled, task-specific data is unavailable.
Why Other Options Are Incorrect:
(A) is incorrect because continued pretraining involves modifying core model weights.
(C) is incorrect because adapting a model to a new domain is better suited to fine-tuning or full retraining.
(D) is incorrect because soft prompting is designed for low-data scenarios, while full fine-tuning requires labeled datasets.
🔹 Oracle Generative AI Reference:
Oracle AI supports efficient adaptation methods, including soft prompting and LoRA, to improve LLM flexibility.

NEW QUESTION # 53
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 # 54
How does a presence penalty function in language model generation?

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

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 # 55
What does "k-shot prompting* refer to when using Large Language Models for task-specific applications?

A. Explicitly providing k examples of the intended task in the prompt to guide the models output
B. The process of training the model on k different tasks simultaneously to improve its versatility
C. Limiting the model to only k possible outcomes or answers for a given task
D. Providing the exact k words in the prompt to guide the model's response

Answer: A

NEW QUESTION # 56
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 compress vector data for minimized storage usage
D. To categorize vectors based on their originating data type (text, images, audio)

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 # 57
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