Free Databricks Databricks-Certified-Generative-AI-Engineer-Associate Exam Questions
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Databricks Databricks-Certified-Generative-AI-Engineer-Associate Exam Questions
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Provided By: Databricks
- Exam: Databricks Certified Generative AI Engineer Associate
- Certification: Databricks Certified Associate
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Total Questions: 105
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Updated On: Sep 28, 2024
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Rated: 4.9 |
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Online Users: 210
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Question 1
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As a Gen AI engineer working on batch inference with MLflow, which of the following methods are correctly used for performing batch inference in single-node and multi-node environments? Choose two correct answers.
Answer: A,D
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As a Gen AI engineer working on batch inference with MLflow, which of the following methods are correctly used for performing batch inference in single-node and multi-node environments? Choose two correct answers.
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Question 2
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As a Gen AI engineer utilizing MLflow for managing your model lifecycle, which feature of MLflow is most beneficial for handling both traditional machine learning and Gen AI workflows effectively?
Answer: B
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As a Gen AI engineer utilizing MLflow for managing your model lifecycle, which feature of MLflow is most beneficial for handling both traditional machine learning and Gen AI workflows effectively?
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Question 3
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As a Gen AI engineer developing an intelligent system that integrates various external tools and APIs, which statement best describes the role of agents?
Answer: D
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As a Gen AI engineer developing an intelligent system that integrates various external tools and APIs, which statement best describes the role of agents?
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Question 4
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Why summarizing retrieved documents before feeding them into an AI system is recommended?
Answer: D
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Why summarizing retrieved documents before feeding them into an AI system is recommended?
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Question 5
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How can a Generative AI Engineer handle complex documents (with mixed text and images) for data extraction and chunking for a RAG based LLM application?
Answer: C
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How can a Generative AI Engineer handle complex documents (with mixed text and images) for data extraction and chunking for a RAG based LLM application?