Prepare for AWS Certification and Build Real-World ML Pipelines with Confidence
This advanced AWS ML and Data Engineering Practice Tests course with 6 practice tests and 330+ practice questions is designed to help you master AWS-based data engineering and machine learning workflows. Whether you’re preparing for the AWS Machine Learning Specialty certification, tackling a technical job interview, or building cloud-native ML systems, this course provides the rigorous, practical training you need.
If your goal is AWS ML certification, a high-impact job offer, or just professional mastery, this course equips you to deliver scalable, secure, and high-performing data engineering solutions in the AWS ecosystem.
Enroll now to become a certified and confident AWS ML professional.
Gururo is a PMI Authorized Training Partner
At-a-glance
Best for
- Data engineers
- ML engineers
- Cloud developers
- Anyone preparing
Why Gururo?
- Lowest Cost
- PMI Authorized Training Partner (ATP)
- 24*7 Support
- 365 days access
Course Details
- 6 full-length AWS ML practice exams
- 330+ challenging questions
- Instant Access
- Certificate of Completion
Highlights
- Realistic Exam Simulation
- Aligned with actual interview blueprint
- Progress Tracking & Review option
- Unlimited Attempts
What You’ll Learn
- Design and implement scalable data pipelines using AWS services like Glue, Kinesis, and Lambda.
- Apply AWS machine learning tools such as SageMaker for model training, deployment, and monitoring.
- Architect end-to-end ML solutions using real-time and batch data ingestion strategies.
- Utilize AWS Glue and EMR to transform and prepare data for machine learning workloads.
- Automate feature engineering workflows with AWS Step Functions and pipelines.
- Implement security, access control, and encryption for ML and data services on AWS.
- Optimize storage and processing costs across S3, Redshift, and Athena for large datasets.
- Monitor, troubleshoot, and debug ML pipelines using CloudWatch, CloudTrail, and X-Ray.
- Prepare confidently for AWS Machine Learning Specialty and Data Engineering interviews.
- Evaluate trade-offs in scalability, performance, and model accuracy in AWS-based ML systems.
What You’ll Gain:
- Knowledge of critical AWS tools for ML and data engineering: Glue, SageMaker, EMR, Redshift, Kinesis, and Step Functions.
- Practice designing ETL pipelines and data lakes tailored for machine learning.
- Hands-on understanding of deploying models and monitoring performance in production.
- Familiarity with security best practices, IAM roles, and fine-grained access policies.
- Readiness for both certification exams and employer interviews involving AWS ML use cases.
- Confidence in selecting the right services and optimizing costs for data-heavy applications.
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Course Requirements / Prerequisites
- Familiarity with core AWS services including S3, IAM, and EC2 is recommended.
- Basic understanding of Python and SQL will be beneficial for hands-on practice.
- Access to an AWS account for testing and experimentation is encouraged.
- Prior experience with machine learning concepts such as training, inference, and evaluation.
- Understanding of data formats (JSON, Parquet, CSV) and basic ETL principles.
- Comfortable with navigating the AWS Console and using the AWS CLI.
- Willingness to troubleshoot and explore AWS documentation as needed.
- Interest in automating ML pipelines and data workflows using cloud-native tools.
- Curiosity about real-world deployment scenarios and scalable architectures.
- No formal AWS certification required—this course helps prepare for one.
Who Should Take This Course?
- Data engineers looking to specialize in ML workflows using AWS services.
- ML engineers preparing for the AWS Machine Learning Specialty certification.
- Cloud developers who want to integrate ML capabilities into serverless applications.
- Data scientists aiming to deploy and manage models efficiently on AWS.
- Solution architects building scalable, production-ready data pipelines.
- IT professionals transitioning into cloud-based machine learning roles.
- DevOps engineers working with data processing and orchestration on AWS.
- Analysts and BI professionals seeking to enhance pipelines with ML insights.
- Freelancers and consultants preparing for client engagements in AWS ML projects.
- Anyone preparing for AWS job interviews that include data engineering or ML system design.
