A company is launching a new product and needs to build a mechanism to monitor comments about the company and its new product on social media. The company needs to be able to evaluate the sentiment…

Question

Options

• ATrain a model in Amazon SageMaker by using the BlazingText algorithm to detect sentiment in
• BTrain a model in Amazon SageMaker by using the semantic segmentation algorithm to model the
• CTrigger an AWS Lambda function when social media posts are added to the S3 bucket.
• DTrigger an AWS Lambda function when social media posts are added to the S3 bucket.

• A
  6% (2)
• B
  3% (1)
• C
  14% (5)
• D
  77% (27)

Explanation

The correct approach is to trigger an AWS Lambda function when new social media posts land in S3, have that function call Amazon Comprehend's DetectSentiment API on each post, and then store the results for visualization and alerting (typically using Amazon QuickSight for dashboards and Amazon CloudWatch for threshold alarms). Amazon Comprehend is a fully managed NLP service that requires no ML expertise or model training - sentiment analysis is available as an API call. This satisfies the requirement to minimize infrastructure and data science resources. Option A (training a custom BlazingText model) requires labeled training data and data science effort, which contradicts the 'minimize resources' requirement. Option B (semantic segmentation) is a computer vision technique for image analysis, completely irrelevant to text sentiment. The Lambda + Comprehend + visualization pipeline can be built and operational in hours, making it the fastest and lowest-overhead solution.

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