Data Engineering

mechanical gears to symbolize data engineering

The Data Engineering domain of the AWS Machine Learning Specialist certification exam comprises obtaining the data, transforming it and transferring it to a repository. Twenty percent of the exam marks come from this knowledge domain which is divided into three subdomains.

The data repository (subdomain 1.1) is where the raw and processed data is stored. S3 is the repository of choice for Machine Learning in AWS although some other data stores are also mentioned. The data ingestion subdomain (1.2) is concerned with getting the raw data into the repository. This can be via batch processing or streaming data. With batch processing data is collected and grouped at a point in time and passed to the data store. Streaming data is constantly being collected and fed into the data store. The third subdomain (1.3) focuses on how raw data is transformed into data that can be used for ML processing. The transformation process changes the data structure. The data may also need to be cleaned, de-duplicated, incomplete data managed and have it’s attributes standardised.

Once these data engineering processes are complete the data is ready for further pre-processing prior to being fed into a Machine Learning algorithm. This preprocessing is covered by the second knowledge domain, Exploratory Data Analysis.

  1. For description of the exam structure see this article:
  2. The AWS exam guide pdf can be downloaded from:

AWS Certified Machine Learning Study Guide: Specialty (MLS-C01) Exam

This study guide provides the domain-by-domain specific knowledge you need to build, train, tune, and deploy machine learning models with the AWS Cloud. The online resources that accompany this Study Guide include practice exams and assessments, electronic flashcards, and supplementary online resources. It is available in both paper and kindle version for immediate access. (Vist Amazon books)

Sample Data Engineering questions

This test is five questions randomly taken from 17 questions of the three sub-domains.

Data engineering study guides

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Data Engineering (domain 1)

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