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Questions and Answers
What does RAG stand for in the context of service performance measurement?
Why is a vector store preferred over storing text strings in databases?
What is the primary advantage of deep learning transformers?
In the RAG metric system, what happens if a service's reliability drops below 99.6%?
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What does a vector store allow us to retrieve quickly?
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What capability do deep learning transformers provide without prior task knowledge?
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What is the primary purpose of fine-tuning large language models?
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What is the main difference between LLMOps (Language Learning Machine Learning Operations) and MLOps (Machine Learning Operations)?
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What is the main limitation of large language models mentioned in the text?
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Which of the following tasks can be handled using transformers, according to the text?
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What is the primary purpose of both LLMOps and MLOps?
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Study Notes
Job Interview Challenge Topics
Interviews for data science roles often assess candidates' skills through various challenges that test their knowledge of current technologies and methodologies used in the field. Here are some common job interview challenge topics you may encounter, along with explanations of each topic:
RAG and Vector Store
RAG stands for Resilient, Agile, and Growth. This is a metric system designed by Google that helps measure the performance of services over time. It combines three metrics: reliability, agility, and growth. The system provides a single number that is proportional to the service's health level. They also have rules to avoid overpromotion, such as a rule that says if reliability drops below 99.6%, the service automatically gets the lowest score, even if its other two metrics are great.
A vector store is a database where vectors are stored instead of text strings. It allows us to store more complex information, like vectors from image processing algorithms. These databases are essential because they allow us to retrieve similar vectors quickly, which can be useful for recommendation systems, search engines, and many other applications.
Deep Learning Transformers
Deep learning transformers are models that allow us to perform arbitrary computational tasks without knowing what those tasks are beforehand. They learn the entire function approximating any input-output mapping. By using transformers, we can handle tasks like translation, summarization, and OCR. We can even guide them towards specific data types.
Large Language Model Fine-Tuning
Large language models are trained on a vast amount of text data and are capable of generating humanlike text based on prompts. However, these models don't understand the world; they just predict probabilities based on patterns learned from training data. To make them more effective and adaptable, researchers have been fine-tuning them on specific domains, such as code generation or medical diagnosis.
LLMOPS and MLOPs
LLMLops (Language Learning Machine Learning Operations) focuses on how to deploy and run language models, while MLOps (Machine Learning Operations) focuses mostly on model deployment and running machine learning pipelines. Both concepts involve managing the lifecycle of AI models, including continuous integration, testing, and deployment.
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Description
Explore common topics encountered in data science job interviews, including RAG and Vector Store, Deep Learning Transformers, Large Language Model Fine-Tuning, and LLMOPS and MLOPs. Understand the concepts and methodologies used to evaluate candidates' skills in data science roles.
