AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 64

An ML engineer needs to use AWS services to identify and extract meaningful unique keywords from documents.
Which solution will meet these requirements with the LEAST operational overhead?

Answer options

• A. Use the Natural Language Toolkit (NLTK) library on Amazon EC2 instances for text pre-processing. Use the Latent Dirichlet Allocation (LDA) algorithm to identify and extract relevant keywords.
• B. Use Amazon SageMaker and the BlazingText algorithm. Apply custom pre-processing steps for stemming and removal of stop words. Calculate term frequency-inverse document frequency (TF-IDF) scores to identify and extract relevant keywords.
• C. Store the documents in an Amazon S3 bucket. Create AWS Lambda functions to process the documents and to run Python scripts for stemming and removal of stop words. Use bigram and trigram techniques to identify and extract relevant keywords.
• D. Use Amazon Comprehend custom entity recognition and key phrase extraction to identify and extract relevant keywords.

Correct answer: D

Explanation

The correct answer is D because Amazon Comprehend provides a fully managed service for entity recognition and key phrase extraction, minimizing operational overhead. Options A, B, and C require significant setup, management, and processing of infrastructure, making them less efficient in terms of operational overhead.