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.
- 1.1 Create data repositories for machine learning.
- 1.2 Identify and implement a data-ingestion solution.
- 1.3 Identify and implement a data-transformation solution.
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.
- For description of the exam structure see this article: https://www.mlexam.com/aws-machine-learning-exam-syllabus/.
- The AWS exam guide pdf can be downloaded from: https://d1.awsstatic.com/training-and-certification/docs-ml/AWS-Certified-Machine-Learning-Specialty_Exam-Guide.pdf
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Whizlabs AWS Certified Machine Learning Specialty
Whizlab’s AWS Certified Machine Learning Specialty Practice tests are designed by experts to simulate the real exam scenario. The questions are based on the exam syllabus outlined by official documentation. These practice tests are provided to the candidates to gain more confidence in exam preparation and self-evaluate them against the exam content.
Practice test content
- Free Practice test – 15 questions
- Practice test 1 – 65 questions
- Practice test 2 – 65 questions
- Practice test 3 – 65 questions
Section test content
- Core ML Concepts – 10 questions
- Data Engineering – 11 questions
- Exploratory Data Analysis – 13 questions
- Modeling – 15 questions
- Machine Learning Implementation and Operations – 12 questions
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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These revision notes describe the AWS services used to ingest streaming data for Machine Learning.
This Study Guide is about transforming raw data so it is ready for Machine Learning. There are two types of transformation: Identify and implement a data-transformation solution is sub-domain 1.3 of the Data Engineering knowledge domain. For more information about the exam structure see: AWS Machine Learning exam syllabus Questions To confirm your understanding scroll to…
These revision notes describe the AWS services available for storing data in data repositories for use in Machine Learning
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