This comprehensive course is designed for cloud professionals, data scientists, and machine learning engineers who want to pass the AWS Certified Machine Learning – Specialty (MLS-C01) exam with confidence. With 1500 carefully crafted practice questions, you will gain the skills to design, implement, and optimize ML solutions on AWS.

The course is divided into six in-depth sections, each containing 250 exam-focused questions. You will cover data engineering, exploratory data analysis, feature engineering, model training, evaluation, deployment, inference, security, compliance, and optimization. Every section aligns with the official MLS-C01 exam blueprint, ensuring you study exactly what AWS tests.

You will work through real-world scenarios using Amazon SageMaker, AWS Glue, Kinesis, Redshift, IAM, KMS, Lambda, API Gateway, Step Functions, CloudWatch, and other AWS services. The questions are designed to improve your problem-solving skills and deepen your understanding of AWS ML workflows.

By completing this course, you will be able to:

• Ingest, process, and prepare data at scale on AWS.

• Choose and train the right ML algorithms for different use cases.

• Deploy models for real-time and batch inference.

• Implement security, compliance, and governance best practices.

• Troubleshoot and optimize ML workloads for performance and cost efficiency.

Whether you are preparing for your first AWS certification or advancing your ML career, this MLS-C01 practice test course gives you the knowledge and confidence to pass the exam on your first attempt.

Who this course is for:

• Cloud engineers, data scientists, ML engineers, and developers preparing for the AWS Certified Machine Learning – Specialty exam (MLS-C01). Ideal for professionals seeking to master ML workflows and deployment on AWS.