MLS-C01 Exam Information and Guideline
AWS Certified Machine Learning Specialty 2025
Below are complete topics detail with latest syllabus and course outline, that will help you good knowledge about exam objectives and topics that you have to prepare. These contents are covered in questions and answers pool of exam.
Exam Code: MLS-C01
Exam Name: AWS Certified Machine Learning - Specialty
Duration: 180 minutes (3 hours)
Format: 65 multiple-choice and multiple-response questions
Passing Score: 750 (on a scale of 100–1000)
- Data Engineering (20%)
- Exploratory Data Analysis (24%)
- Modeling (36%)
- Machine Learning Implementation and Operations (20%)
Domain 1: Data Engineering
Task Statement 1.1: Create data repositories for ML.
- Identify data sources
- content and location
- primary sources such as user data
- Determine storage mediums
- databases
- Amazon S3
- Amazon Elastic File System [Amazon EFS]
- Amazon Elastic Block Store [Amazon EBS]
Task Statement 1.2: Identify and implement a data ingestion solution.
- Identify data job styles and job types
- batch load
- streaming
- Orchestrate data ingestion pipelines (batch-based ML workloads and streaming-based ML workloads).
- Amazon Kinesis
- Amazon Data Firehose
- Amazon EMR
- AWS Glue
- Amazon Managed Service for Apache Flink
• Schedule jobs.
Task Statement 1.3: Identify and implement a data transformation solution.
- Transform data in transit
- ETL
- AWS Glue
- Amazon EMR
- AWS Batch
- Handle ML-specific data by using MapReduce
- Apache Hadoop
- Apache Spark
- Apache Hive
Domain 2: Exploratory Data Analysis
Task Statement 2.1: Sanitize and prepare data for modeling.
- Identify and handle missing data, corrupt data, and stop words.
- Format, normalize, augment, and scale data.
- Determine whether there is sufficient labeled data.
- Identify mitigation strategies.
- Use data labelling tools (for example, Amazon Mechanical Turk).
Task Statement 2.2: Perform feature engineering.
- Identify and extract features from datasets, including from data sources such as text, speech, images, and public datasets.
- Analyze and evaluate feature engineering concepts
- binning
- tokenization
- outliers
- synthetic features
- one-hot encoding
- reducing dimensionality of data
Task Statement 2.3: Analyze and visualize data for ML.
- Create graphs
- scatter plots
- time series
- histograms
- box plots
- Interpret descriptive statistics
- correlation
- summary statistics
- p-value
- Perform cluster analysis
- hierarchical
- diagnosis
- elbow plot
- cluster size
Domain 3: Modeling
Task Statement 3.1: Frame business problems as ML problems.
- Determine when to use and when not to use ML.
- Know the difference between supervised and unsupervised learning.
- Select from among classification, regression, forecasting, clustering, recommendation, and foundation models.
Task Statement 3.2: Select the appropriate model(s) for a given ML problem.
- XGBoost
- logistic regression
- k-means
- linear regression
- decision trees
- random forests
- RNN
- CNN
- ensemble
- transfer learning
- large language models (LLMs)
- Express the intuition behind models
Task Statement 3.3: Train ML models.
- Split data between training and validation (for example, cross validation).
- Understand optimization techniques for ML training
- gradient descent
- loss functions
- convergence
- Choose appropriate compute resources (for example GPU or CPU, distributed or non-distributed).
- Choose appropriate compute platforms (Spark or non-Spark).
- Update and retrain models.
- Batch or real-time/online
Task Statement 3.4: Perform hyperparameter optimization.
- Perform regularization.
- Dropout
- L1/L2
- Perform cross-validation.
- Initialize models.
- Understand neural network architecture (layers and nodes), learning rate, and activation functions.
- Understand tree-based models (number of trees, number of levels).
- Understand linear models (learning rate).
Task Statement 3.5: Evaluate ML models.
- Avoid overfitting or underfitting.
- Detect and handle bias and variance.
- Evaluate metrics
- area under curve [AUC]-receiver operating characteristics [ROC]
- accuracy
- precision
- recall
- Root Mean Square Error [RMSE]
- F1 score
- Interpret confusion matrices.
- Perform offline and online model evaluation (A/B testing).
- Compare models by using metrics
- time to train a model
- quality of model
- engineering costs
- Perform cross-validation.
Domain 4: Machine Learning Implementation and Operations
Task Statement 4.1: Build ML solutions for performance, availability, scalability, resiliency, and fault tolerance.
- Log and monitor AWS environments.
- AWS CloudTrail and Amazon CloudWatch
- Build error monitoring solutions.
- Deploy to multiple AWS Regions and multiple Availability Zones.
- Create AMIs and golden images.
- Create Docker containers.
- Deploy Auto Scaling groups.
- Rightsize resources
- instances
- Provisioned IOPS
- volumes
- Perform load balancing.
- Follow AWS best practices.
Task Statement 4.2: Recommend and implement the appropriate ML services and features for a given problem.
- ML on AWS (application services), for example:
- Amazon Polly
- Amazon Lex
- Amazon Transcribe
- Amazon Q
- Understand AWS service quotas.
- Determine when to build custom models and when to use Amazon SageMaker built-in algorithms.
- Understand AWS infrastructure (for example, instance types) and cost considerations.
- Use Spot Instances to train deep learning models by using AWS Batch.
Task Statement 4.3: Apply basic AWS security practices to ML solutions.
- AWS Identity and Access Management (IAM)
- S3 bucket policies
- Security groups
- VPCs
- Encryption and anonymization
Task Statement 4.4: Deploy and operationalize ML solutions.
- Expose endpoints and interact with them.
- Understand ML models.
- Perform A/B testing.
- Retrain pipelines.
- Debug and troubleshoot ML models.
- Detect and mitigate drops in performance.
- Monitor performance of the model.
- Ingestion and collection
- Processing and ETL
- Data analysis and visualization
- Model training
- Model deployment and inference
- Operationalizing ML
- AWS ML application services
- Language relevant to ML (for example, Python, Java, Scala, R, SQL)
- Notebooks and integrated development environments (IDEs)
- Amazon Athena
- Amazon Data Firehose
- Amazon EMR
- AWS Glue
- Amazon Kinesis
- Amazon Kinesis Data Streams
- AWS Lake Formation
- Amazon Managed Service for Apache Flink
- Amazon OpenSearch Service
- Amazon QuickSight
- AWS Batch
- Amazon EC2
- AWS Lambda
- Amazon Elastic Container Registry (Amazon ECR)
- Amazon Elastic Container Service (Amazon ECS)
- Amazon Elastic Kubernetes Service (Amazon EKS)
- AWS Fargate
- Amazon Redshift
- AWS IoT Greengrass
- Amazon Bedrock
- Amazon Comprehend
- AWS Deep Learning AMIs (DLAMI)
- Amazon Forecast
- Amazon Fraud Detector
- Amazon Lex
- Amazon Kendra
- Amazon Mechanical Turk
- Amazon Polly
- Amazon Q
- Amazon Rekognition
- Amazon SageMaker
- Amazon Textract
- Amazon Transcribe
- Amazon Translate
- AWS CloudTrail
- Amazon CloudWatch
- Amazon VPC
- AWS Identity and Access Management (IAM)
- Amazon Elastic Block Store (Amazon EBS)
- Amazon Elastic File System (Amazon EFS)
- Amazon FSx
- Amazon S3
- AWS Data Pipeline
- AWS DeepRacer
- Amazon Machine Learning (Amazon ML)