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| 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.
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| 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.
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| 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 (Q10-Q15):
NEW QUESTION # 10
What does a higher number assigned to a token signify in the "Show Likelihoods" feature of the language model token generation?
- A. The token will be the only one considered in the next generation step.
- B. The token is more likely to follow the current token.
- C. The token is unrelated to the current token and will not be used.
- D. The token is less likely to follow the current token.
Answer: B
Explanation:
In the "Show Likelihoods" feature of language model token generation, a higher number assigned to a token indicates that the token is more likely to follow the current token. This likelihood is based on the model's probability distribution, where tokens with higher probabilities are considered more likely to be the next in the sequence. This feature helps in understanding the model's decision-making process and the relative probabilities of different tokens.
Reference
Technical documentation on language model token generation
Research articles on probability distributions in generative models
NEW QUESTION # 11
How does the integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models(LLMS) fundamentally alter their responses?
- A. It limits their ability to understand and generate natural language.
- B. It shifts the basis of their responses from pretrained internal knowledge to real-time data retrieval.
- C. It enables them to bypass the need for pretraining on large text corpora.
- D. It transforms their architecture from a neural network to a traditional database system.
Answer: B
Explanation:
The integration of a vector database into Retrieval-Augmented Generation (RAG)-based Large Language Models (LLMs) fundamentally alters their responses by shifting the basis from pretrained internal knowledge to real-time data retrieval. This means that instead of relying solely on the knowledge encoded in the model during training, the LLM can retrieve and incorporate up-to-date and relevant information from an external database in real time. This enhances the model's ability to generate accurate and contextually relevant responses.
Reference
Research papers on Retrieval-Augmented Generation (RAG) techniques
Technical documentation on integrating vector databases with LLMs
NEW QUESTION # 12
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. Model Drift
- C. Underfitting
- D. Overfilling
Answer: D
NEW QUESTION # 13
What does "k-shot prompting* refer to when using Large Language Models for task-specific applications?
- A. Providing the exact k words in the prompt to guide the model's response
- B. Explicitly providing k examples of the intended task in the prompt to guide the models output
- 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: B
NEW QUESTION # 14
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?
- A. By restricting updates to only a specific croup of transformer Layers
- B. By allowing updates across all layers of the model
- C. By incorporating additional layers to the base model
- D. By excluding transformer layers from the fine-tuning process entirely
Answer: A
NEW QUESTION # 15
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