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.
- 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
Contains affiliate links. If you go to Whizlab’s website and make a purchase I may receive a small payment. The purchase price to you will be unchanged. Thank you for your support.
Whizlabs AWS Certified Machine Learning Specialty
Practice Exams with 271 questions, Video Lectures and Hands-on Labs from Whizlabs
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
Kinesis KPL vs API
The Kinesis Producer Library (KPL) and the Kinesis API can both be used to send data to Kinesis Data Streams. The advantage of the KPL is it provides a lot of added features, such as failed transmission handling built in. If you use the Kinesis API you have to code these features yourself. The advantages…
Ingesting data for Machine Learning
These revision notes describe the AWS services used to ingest streaming data for Machine Learning.
Data transformation 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…
Machine Learning data repositories compared
These revision notes describe the AWS services available for storing data in data repositories for use in Machine Learning
Identify data sources
Obtaining large specialised datasets is a must to experiment and train Machine Learning models so they can recognise patterns in real world data and infer a prediction. Datasets can also be used as a source of labeled data to train models to generalise unlabeled real world data. Fortunately there are many data sources for datasets…
Batch processing for Machine Learning
For Machine Learning AWS glue and AWS Database Migration Service are used to ingest data. Batch processing refers to processing usually performed to a specific schedule. Before the batch process starts data is waiting and often any new data will have to wait for the next batch processing to be processed. In AWS any compute…
