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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q55-Q60):
NEW QUESTION # 55
Given the following code: chain = prompt |11m
- A. Which statement is true about LangChain Expression language (ICED?
- B. LCEL is a legacy method for creating chains in LangChain
- C. LCEL is a declarative and preferred way to compose chains together.
- D. LCEL is a programming language used to write documentation for LangChain.
Answer: D
NEW QUESTION # 56
How does the utilization of T-Few transformer layers contribute to the efficiency of the fine-tuning process?
- A. By incorporating additional layers to the base model
- B. By allowing updates across all layers of the model
- C. By restricting updates to only a specific croup of transformer Layers
- D. By excluding transformer layers from the fine-tuning process entirely
Answer: C
Explanation:
The utilization of T-Few transformer layers contributes to the efficiency of the fine-tuning process by restricting updates to only a specific group of transformer layers. This selective updating approach allows the model to adapt to new data without the need to retrain all layers, thus saving computational resources and time. By focusing on the most relevant parts of the model, T-Few fine-tuning achieves efficient and effective performance improvements.
Reference
Research papers on T-Few fine-tuning techniques
Technical guides on optimizing transformer models
NEW QUESTION # 57
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. 30 unit hours
- B. 40 unit hours
- C. 10 unit hours
- D. 15 unit hours
Answer: C
NEW QUESTION # 58
What does a dedicated RDMA cluster network do during model fine-tuning and inference?
- A. It leads to higher latency in model inference.
- B. It increases G PU memory requirements for model deployment.
- C. It limits the number of fine-tuned model deployable on the same GPU cluster.
- D. It enables the deployment of multiple fine-tuned models.
Answer: D
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. Underfitting
- B. Overfilling
- C. Model Drift
- D. Data Leakage
Answer: B
NEW QUESTION # 60
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