Free Practice Dell EMC NCA-GENL Exam Questions 2025

You are working with a Large Language Model (LLM) trained on general-purpose data, but now you need it to perform well on legal document processing. After fine-tuning the model on a legal dataset, it still struggles with accurately interpreting certain legal terminologies and produces inconsistent outputs. What would be the most effective next step to improve the model's performance?

You are tasked with developing a generative AI model for a client that needs to generate creative marketing content based on customer behavior data. The data includes customer purchase history, browsing patterns, and social media interactions. Which of the following approaches would be most effective in ensuring that the generated content is both relevant and creative? (Select two)

You are developing a generative AI model that needs to generate high-quality images from textual descriptions in real-time. Which two of the following approaches will best optimize the performance and quality of your model given the hardware constraints of a GPU with limited memory? (Select two)

You have trained two different generative AI models, Model X and Model Y, on the same dataset. To compare their performance, you calculate the Mean Squared Error (MSE) and the proportion of explained variance (R²). Model X has a lower MSE but also a lower R² compared to Model Y. Which model is likely better at explaining the variability in the data?

You are developing a system that processes large volumes of legal documents to extract named entities such as company names, contract dates, and legal terms. The senior developer asks you to choose the most appropriate Python package to handle this task efficiently, with a focus on accuracy and performance. Which Python package would be most appropriate for implementing named entity recognition (NER) in this scenario?