NCA-GENM Question 125
Select 2You are working on a generative AI model that generates coherent text based on user input. To enhance the model's understanding of natural language semantics, you decide to use Python libraries for preprocessing and vector representation. Which combination of tools would be most effective for tokenization, similarity computation, and storing vector embeddings?
- A
spaCy for tokenization, NumPy for similarity computation, and a vector database for embedding storage
- B
NumPy for tokenization, spaCy for similarity computation, and a vector database for embedding storage
- C
spaCy for tokenization, a vector database for similarity computation, and NumPy for embedding storage
- D
spaCy for tokenization, a vector database for embedding storage, and NumPy for similarity computation
Show answer and explanation
Correct answers: A, D
Explanation
When leveraging Python natural language packages for generative AI tasks, it is important to use the right tools for each stage of the pipeline. spaCy excels at tokenization due to its robust NLP capabilities, NumPy is highly efficient for mathematical operations like similarity computation, and vector databases are purpose-built for storing and querying vector embeddings efficiently. Combining these tools ensures an optimal and scalable workflow for generative AI applications.
- A. Correct.
This is correct because spaCy is well-suited for tokenization, NumPy can be used for similarity computations, and vector databases are specialized for storing and querying vector embeddings.
- B. Incorrect.
This is incorrect because NumPy is not designed for tokenization, and spaCy is better suited for this task. Additionally, NumPy is not used for embedding storage.
- C. Incorrect.
This is incorrect because a vector database is not typically used for similarity computation, and NumPy is not used for embedding storage. This workflow is inefficient and misuses the tools.
- D. Correct.
This is correct because spaCy can handle tokenization effectively, vector databases are ideal for embedding storage, and NumPy is a powerful tool for performing similarity computations.