Question 14
Domain 2: ML Model DevelopmentA company is gathering audio, video, and text data in various languages. The company needs to use a large language model (LLM) to summarize the gathered data that is in Spanish. Which solution will meet these requirements in the LEAST amount of time?
Correct answer: B
Explanation
Amazon Transcribe can turn audio into text, and Amazon Translate can convert Spanish into English, which prepares the data for an LLM. Amazon Bedrock with the Jurassic model can then summarize the English text, meeting the need to process multilingual data in the least time by using managed services instead of building a custom workflow.
Why each option is right or wrong
A. Train and deploy a model in Amazon SageMaker to convert the data into English text. Train and deploy an LLM in SageMaker to summarize the text.
Training two SageMaker models adds major build time; requirement asks for the fastest solution.
B. Use Amazon Transcribe and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Jurassic model to summarize the text.
Amazon Transcribe is the managed service for speech-to-text, and Amazon Translate supports Spanish-to-English translation, so the audio and Spanish text can be normalized into English without building custom NLP pipelines. Amazon Bedrock provides immediate access to foundation models such as Jurassic for summarization, which is faster than training or hosting a bespoke model and therefore best fits the requirement to complete the workflow in the least time.
C. Use Amazon Rekognition and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Anthropic Claude model to summarize the text.
Amazon Rekognition focuses on image/video analysis, not converting spoken audio into text.
D. Use Amazon Comprehend and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Stable Diffusion model to summarize the text.
Amazon Comprehend analyzes text, and Stable Diffusion is for image generation, not text summarization.