Get certified as AWS Machine Learning — Specialty

In this article, I provide feedback about my experience with AWS Machine Learning Specialty Exam (which I cleared with 95%).

Why you should take it ?

Take this exam if you want to boost your Data Science / Machine Learning Engineering career within the world of AWS or any other cloud provider. The high-level concepts learned while preparing for the ceritifcation can be easily applied to other cloud providers as well.

The offer is pretty similar, only the names of the services do change (e.g. AWS’s SageMaker is equivalent to Azure Machine Learning (Azure) or Google DataLab + Google CloudML combo in the world of GCP)

Future employers / clients will be more confortable hiring you as the certification validates the following abilities:

• Select and justify the appropriate ML approach for a given business problem
• Identify appropriate AWS services to implement ML solutions
• Design and implement scalable, cost-optimized, reliable, and secure ML solutions

Overview of the exam and services to focus on

Unlike any other AWS certification, 80% of the exam questions aren’t about AWS services.

• Machine Learning (80% of exam questions)
• AWS Services (20% of exam questions)

The questions cover 4 different domains (they come in random orders)

• Domain 1: Data Engineering (20% of Examination)
• Domain 2: Exploratory Data Analysis (24% of Examination)
• Domain 3: Modeling (36% of Examination)
• Domain 4: Machine Learning Implementation and Operations (20%)

Here are the key concepts / AWS services to focus on during exam preparations :

Data Engineering:

• AWS Services : Amazon S3 , Amazon FSx For Lustre , Amazon EFS , Amazon EBS , Kinesis (Data Streams, Firehose and Data Analytics) , Amazon DMS , Amazon Data…