AWS Machine Learning Specialty
AWS MLS-C01 Practice Exam
130+ Questions with detailed explanations. Realistic timed simulation.
About this Exam
The AWS Certified Machine Learning Specialty (MLS-C01) validates your ability to design, implement, deploy, and maintain machine learning solutions on AWS. This exam is significantly more technical than the associate-level certifications and requires deep understanding of the entire ML pipeline: data collection, data engineering, exploratory data analysis, feature engineering, model training, hyperparameter tuning, evaluation, and deployment. You will need to know when to use SageMaker, Comprehend, Rekognition, Translate, Polly, Lex, and other AI/ML services. The exam heavily tests your ability to choose the right algorithm for a given problem — classification, regression, clustering, forecasting, and anomaly detection. Expect questions on data preparation techniques including handling missing values, feature scaling, dimensionality reduction with PCA, and dealing with imbalanced datasets. This certification is ideal for data scientists and ML engineers who build production ML systems on AWS.
What You Will Learn
Exam Format
Passing Score
750 out of 1000
Questions
65 questions (50 scored, 15 unscored)
Time Limit
180 minutes
Format
Multiple choice and multiple response
Who Should Take This Exam
- Data scientists deploying ML models on AWS
- Machine learning engineers building production ML pipelines
- Software engineers transitioning into ML roles
- Technical leads overseeing ML projects on AWS infrastructure
Recommended Prerequisites
- 2+ years of hands-on experience with ML/deep learning workloads on AWS
- Strong understanding of ML algorithms (supervised, unsupervised, reinforcement learning)
- Experience with data engineering pipelines (S3, Glue, Kinesis, Athena)
- Proficiency in Python and familiarity with ML frameworks (TensorFlow, PyTorch, scikit-learn)
Exam Tips
Know SageMaker inside out: built-in algorithms (XGBoost, Linear Learner, BlazingText), training jobs, endpoints, and batch transform
Understand data splits (training, validation, test) and cross-validation techniques
Study confusion matrices, precision, recall, F1 score, and AUC-ROC — you will be asked to interpret model performance
Learn when to use Kinesis Data Streams vs Kinesis Firehose for real-time ML data ingestion
Related Certifications
Exam Outline
Duration
180 Minutes
Questions
130+ Questions
Format
Multiple Choice
Safe & secure mock environment