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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 All Questions

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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 topic 1 question 40 discussion

Exam question from Amazon's AWS Certified Machine Learning Engineer - Associate MLA-C01
Question #: 40
Topic #: 1
[All AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions]

A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 ТВ in size and consists of CSV, JSON, Apache Parquet, and simple text files.
The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.
Which solution will meet these requirements?

  • A. Process data at each step by using Amazon SageMaker Data Wrangler. Automate the process by using Data Wrangler jobs.
  • B. Use Amazon SageMaker notebooks for each data processing step. Automate the process by using Amazon EventBridge.
  • C. Process data at each step by using AWS Lambda functions. Automate the process by using AWS Step Functions and Amazon EventBridge.
  • D. Use Amazon SageMaker Pipelines to create a pipeline of data processing steps. Automate the pipeline by using Amazon EventBridge.
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Suggested Answer: D 🗳️
by GiorgioGss at Nov. 27, 2024, 10:33 p.m.
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Saransundar
1 month ago
Selected Answer: D
Large datasets + Multiple file formats + Complex automation & orchestration of ML workflows + NLP Transformation ---> Sagemaker pipelines + Event bridge for trigger
upvoted 1 times
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GiorgioGss
1 month, 1 week ago
Selected Answer: D
At first sight it sounds like C but because of "complex manipulations that can take hours to finish" for sure sounds like D.
upvoted 1 times
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