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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	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 3	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.

Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q56-Q61):

NEW QUESTION # 56
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. Providing the exact k words in the prompt to guide the model's response
C. Limiting the model to only k possible outcomes or answers for a given task
D. The process of training the model on k different tasks simultaneously to improve its versatility

Answer: A

Explanation:
K-shot prompting refers to providing the language model with k examples of the task at hand within the prompt. These examples help guide the model's understanding and output by demonstrating the desired format and approach. This technique is used to improve the model's performance on specific tasks by showing it how to handle similar situations.
Reference
Research papers on few-shot learning and prompting techniques
Technical documentation on using examples in prompts for large language models

NEW QUESTION # 57
Which technique involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response?

A. Chain-of-Through
B. Least to most Prompting
C. In context Learning
D. Step-Bock Prompting

Answer: A

Explanation:
Chain-of-Thought prompting involves prompting the Large Language Model (LLM) to emit intermediate reasoning steps as part of its response. This technique helps the model articulate its thought process and reasoning, leading to more transparent and understandable outputs. By breaking down the problem into smaller, logical steps, the model can provide more accurate and detailed responses.
Reference
Research articles on Chain-of-Thought prompting
Technical guides on enhancing model transparency and reasoning with intermediate steps

NEW QUESTION # 58
Why is it challenging to apply diffusion models to text generation?

A. Because text is not categorical
B. Because diffusion models can only produce images
C. Because text representation is categorical unlike images
D. Because text generation does not require complex models

Answer: C

Explanation:
Diffusion models are primarily used for image generation because they work by incrementally adding noise to a data distribution and then learning to remove it, effectively denoising an image over time. This method works well for continuous data, such as pixel values in images.
However, text is fundamentally categorical, meaning:
Discrete Nature of Text - Unlike images where pixel values change smoothly, text is composed of discrete symbols (words, characters, or tokens), making it difficult to apply continuous noise diffusion.
Tokenization Challenges - Language models work with tokenized words or subwords. Diffusion models would need a way to gradually transition between discrete text tokens, which is not straightforward.
Non-Sequential Nature of Noise Addition - Image-based diffusion models can modify pixel values slightly to learn transformations, but text does not have an equivalent smooth transformation between words.
Alternative Approaches in Text Generation - Due to these challenges, text generation relies more on transformer-based models (like Oracle's AI-driven NLP models), which handle categorical text more effectively than diffusion methods.
🔹 Oracle Generative AI Reference:
Oracle focuses on transformer-based models for text-related AI applications rather than diffusion models, as transformers are more effective in understanding and generating text.

NEW QUESTION # 59
What issue might arise from using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service?

A. Data Leakage
B. Underfitting
C. Model Drift
D. Overfilling

Answer: B

Explanation:
Using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service might result in underfitting. Underfitting occurs when a model is too simplistic to capture the underlying patterns in the data, leading to poor performance on both training and validation data. This is particularly problematic with small data sets because there may not be enough information for the model to learn the necessary patterns and relationships.
Reference
Articles on machine learning challenges with small data sets
Technical documentation on fine-tuning models in OCI

NEW QUESTION # 60
You create a fine-tuning dedicated AI cluster to customize a foundational model with your custom training dat a. How many unit hours arc required for fine-tuning if the cluster is active for 10 hours?

A. 15 unit hours
B. 10 unit hours
C. 30 unit hours
D. 40 unit hours

Answer: B

Explanation:
When you create a fine-tuning dedicated AI cluster and it is active for 10 hours, the number of unit hours required for fine-tuning is equal to the duration for which the cluster is active. Therefore, if the cluster is active for 10 hours, it requires 10 unit hours. This calculation assumes that the unit hour measurement directly corresponds to the active time of the cluster.
Reference
OCI documentation on unit hours and fine-tuning processes
Usage guidelines for dedicated AI clusters in OCI

NEW QUESTION # 61
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