Cost-Effective Ingredient Extraction from S3

SAA-C03 By islever Editorial Last updated

Design secure workloads and applications.

Short answer

A Use S3 Event Notifications to invoke an AWS Lambda function that analyzes objects using Amazon Comprehend to extract ingredient names and store them in DynamoDB.

Question

A company needs to extract the names of ingredients from recipe records that are stored as text files in an Amazon S3 bucket. A web application will use the ingredient names to query an Amazon DynamoDB table and determine a nutrition score. The application can handle non-food records and errors. The company does not have any employees who have machine learning knowledge to develop this solution. Which solution will meet these requirements MOST cost-effectively?
  1. Use S3 Event Notifications to invoke an AWS Lambda function when PutObject requests occur. Program the Lambda function to analyze the object and extract the ingredient names by using Amazon Comprehend. Store the Amazon Comprehend output in the DynamoDB table. Correct Answer
  2. Use an Amazon EventBridge rule to invoke an AWS Lambda function when PutObject requests occur. Program the Lambda function to analyze the object by using Amazon Forecast to extract the ingredient names. Store the Forecast output in the DynamoDB table.
  3. Use S3 Event Notifications to invoke an AWS Lambda function when PutObject requests occur. Use Amazon Polly to create audio recordings of the recipe records. Save the audio files in the S3 bucket. Use Amazon Simple Notification Service (Amazon SNS) to send a URL as a message to employees. Instruct the employees to listen to the audio files and calculate the nutrition score. Store the ingredient names in the DynamoDB table.
  4. Use an Amazon EventBridge rule to invoke an AWS Lambda function when a PutObject request occurs. Program the Lambda function to analyze the object and extract the ingredient names by using Amazon SageMaker. Store the inference output from the SageMaker endpoint in the DynamoDB table.

Key Takeaways

The core concept is identifying a serverless NLP service that requires no ML expertise, while avoiding time-series prediction or heavy model training tools.

This question evaluates the selection of AWS services for text analysis without custom machine learning development, highlighting Amazon Comprehend as the optimal tool for entity extraction.

Common mistake: Candidates often select Amazon SageMaker (Option D) because it is a general-purpose ML platform, failing to recognize that it requires significant engineering effort and knowledge contrary to the prompt's constraints.

Expert Analysis

Why the Answer Is Correct

Option A is correct because Amazon Comprehend is a fully managed natural language processing (NLP) service that can extract entities (like ingredient names) from text files stored in S3. By using S3 Event Notifications to trigger an AWS Lambda function, the solution is event-driven and cost-effective, as you only pay for the compute time used during processing. Comprehend requires no machine learning modeling skills, directly addressing the constraint that the company has no ML knowledge.

Why the Other Options Are Wrong

Option B is incorrect because Amazon Forecast is designed for time-series forecasting and predictive analytics, not for extracting entities from unstructured text. Option C is highly inefficient and costly; using Polly to generate audio and then relying on human labor to listen and process data introduces massive operational overhead and latency, violating the 'most cost-effective' requirement. Option D involves Amazon SageMaker, which is a comprehensive platform for building, training, and deploying ML models; this requires specialized ML knowledge and setup effort, contradicting the requirement for no ML employees.

Community Comment Notes

Community consensus strongly supports Option A. As one user noted, "Amazon Comprehend uses natural language processing (NLP) to extract insights about the content of documents," making it the perfect fit for entity extraction like ingredients. Another commenter pointed out that "Forecast is time-series" and "SageMaker = ML," highlighting why those options were distractors based on tool misuse rather than capability gaps.

Official Reference

https://docs.aws.amazon.com/comprehend/latest/dg/what-is-comprehend.html

This link opens the official vendor documentation in a new tab.

Exam Strategy

Always align the service choice with the skill level required by the scenario. If 'no ML knowledge' is specified, look for managed AI services like Comprehend, Rekognition, or Translate, rather than platforms like SageMaker.

Frequently Asked Questions

Why is Amazon Forecast incorrect for this task?

Forecast is for time-series predictions, not text entity extraction.

Can I use SageMaker if I have no ML staff?

No, SageMaker requires ML expertise to build and train models.

More SAA-C03 FAQ →

Community Votes

A
100%

100% of anonymous learners picked answer A. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 7 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 7

  1. Scheldon 👍 3 Answered A
    AnswerA Amazon Comprehend uses natural language processing (NLP) to extract insights about the content of documents. It develops insights by recognizing the entities, key phrases, language, sentiments, and other common elements in a document. Use Amazon Comprehend to create new products based on understanding the structure of documents. For example, using Amazon Comprehend you can search social networking feeds for mentions of products or scan an entire document repository for key phrases. https://docs.aws.amazon.com/comprehend/latest/dg/what-is.html
  2. fae3297 👍 3 Answered A
    A seems right B. Forecast is time-series C. Using Polly to create audio recordings just to make your employees listen to them seems inefficient to say the least D. Question asks for no ML. SageMaker = ML
  3. Danilus 👍 2 Answered A
    Key: Most cost-effective solution Key: Extract the names of ingredients from recipe records Key: The company does not have any employees with machine learning knowledge Not B because Forecast is used for making predictions in time series, not for extracting text or ingredients. Not C because it would be too expensive and involve too much operational overhead. Not D because SageMaker is designed for creating custom machine learning models, and the company lacks employees with machine learning knowledge. Conclusion: The answer is A because Comprehend is used for natural language processing (NLP), which in this case can extract ingredient names from the recipes effectively. so the answer is A because comprehend is used for nlp (natular languages processing) which is in this case use for to extract the names of the recipes
  4. Besisco 👍 1 Answered A
    I’m fascinated by how self-descriptive the names of AWS ML tools are.
  5. seetpt 👍 5 Answered A
    A correct
Show 2 more comments
  1. asdfcdsxdfc 👍 3
    shouldn't it be A?
  2. seetpt 👍 3 Answered A
    A is correct

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