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including low-resource languages. The model needs to be deployed on a cloud infrastructure that
charges based on the number of parameters and inference time. Which approach would be the most
effective for selecting an appropriate model, considering both multilingual support and cost efficiency?
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feeding the data into a machine learning model?
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Explanation:
Introducing a third prompt that combines elements from the first two strategies allows for a more
nuanced comparison and can help identify a more effective approach by blending successful aspects of
the original prompts.
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?
Quiz
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)
content generation.
data.
customer feedback.
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regions. The key metrics include response accuracy, latency, and user satisfaction. You need to present
your findings in a way that clearly shows regional differences in these metrics to help guide future
deployment strategies. Which visualization technique would best allow stakeholders to compare
performance metrics across different regions?
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model's performance degrades significantly when deployed on different geographic servers. Which of the
following is the most likely reason for this performance inconsistency?
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personalized product recommendations based on customer browsing history. What is the most important
factor to ensure the recommendations are relevant and personalized?
before training the model.
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Explanation:
Transformer-XL addresses the issue of context fragmentation by enabling models to retain longer-term
dependencies, which allows for more coherent long-form text generation.
A customer service department uses a Large Language Model (LLM) to analyze and categorize
incoming support emails into various topics like billing, technical issues, and product information.
However, the model struggles with correctly categorizing emails that contain jargon or ambiguous terms.
What is the best approach to improve the model’s categorization performance?
ambiguous terms.
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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)
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