The AWS Certified Machine Learning Specialty certification is for developers and data scientists who want to validate their machine learning skills on the AWS platform. Specifically, the exam tests a candidate’s ability to design, build, deploy and maintain machine learning solutions. This path covers the four key test domains: Data Engineering, Exploratory Data Analysis, Modeling, and Machine Learning Implementation and Operations. The information and resources contained in this path will be vital in preparing to take the AWS Certified Machine Learning Specialty exam.

Just like the real exam, this practice exam tests four different domains:

• Data Engineering

• Exploratory Data Analysis

• Modeling

• Machine Learning Implementation and Operations

You'll need deep and broad knowledge of SageMaker and AWS's other machine learning services, including Rekognition, Translate, Polly, and Comprehend. You'll need to know how to process big data using Kinesis, S3, Glue, and Athena. And you'll need a strong knowledge of AWS security, including use of KMS and IAM.

But AWS knowledge is not enough to pass this practice exam, or the real thing! You also need deep knowledge on data science, feature engineering and tuning your machine learning models. Do you really understand regularization techniques and how to use them? Do you really understand precision, recall, and AUC? Do you know how different deep learning models work, and how they are used? This practice exam will let you find out. Every question includes an explanation of the correct answer as well.