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In the context of generative AI and large language models, text embeddings are a key component.
What is the primary purpose of text embeddings in a retrieval-augmented generation (RAG) system, and how are they used?
A. Text embeddings generate a one-to-one mapping of words, allowing for the exact reconstruction of the original input text.
B. Text embeddings map words and phrases to high-dimensional vectors that capture their semantic meaning, allowing for efficient document retrieval based on contextual similarity.
C. Text embeddings are used to randomly assign values to words in the corpus, providing variability in how the model generates text.
D. Text embeddings are used to reduce the dimensionality of the input text for faster training of the generative model.
Answer: B
NEW QUESTION # 117
When tuning model parameters for a generative AI prompt, which of the following adjustments would most likely increase the model's tendency to generate coherent but less creative responses?
A. Using Top-k Sampling with a k value of 100
B. Reducing the beam size in beam search from 5 to 1
C. Decreasing the value of the temperature parameter to 0.2
D. Increasing the temperature parameter to 1.5
Answer: C
NEW QUESTION # 118
In planning the deployment of a generative AI model that relies on a large corpus of data, you need to organize and version the data repository used for training prompts.
Which of the following approaches best ensures efficient data versioning, integrity, and easy rollback during prompt refinement?
A. Store all datasets in a monolithic repository and append new data versions as additional files with timestamps.
B. Implement a data versioning system like DVC (Data Version Control) or MLflow, integrated with a cloud storage service, to track data changes and link specific data versions to corresponding model and prompt versions.
C. Use a relational database to store the corpus, but without tracking any changes to the datasets.
D. Store data versions directly in the production environment without versioning to save on storage costs.
Answer: B
NEW QUESTION # 119
You are tasked with deploying a foundation model for text generation on the IBM Watsonx platform. The foundation model has been pre-trained on a large corpus but has not been fine-tuned for your specific use case.
What is the most critical factor to consider when deploying this model to ensure it performs optimally on the Watsonx platform?
A. You should enable quantization of the model to optimize inference performance without compromising accuracy in a production environment.
B. You must ensure that the model is compatible with IBM's foundation model API, as Watsonx only allows deployment of models that integrate with its foundation model architecture.
C. The model's size should be reduced to fit within the memory constraints of the deployment environment, even if it leads to a loss of precision in its outputs.
D. The model should be fine-tuned with domain-specific data before deployment, as Watsonx requires fine-tuning for all foundation models before they can be deployed.
Answer: A
NEW QUESTION # 120
In the context of quantizing large language models (LLMs), which of the following statements best describes the key trade-offs between model size, performance, and accuracy when using quantization techniques?
A. Quantization reduces model size but may lead to a loss of accuracy, especially with aggressive quantization methods.
B. Quantization always improves model performance but significantly increases model size.
C. Quantization eliminates the need for fine-tuning after deployment, ensuring zero accuracy loss.
D. Quantization maintains model accuracy but doubles the computation required for inference.
Answer: A
NEW QUESTION # 121
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