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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)