Free AWS MLA-C01 Exam Questions

Core AWS ML Technologies:

• Amazon SageMaker - Built-in algorithms, script mode, automatic model tuning, Model Registry, Pipelines, Data Wrangler, Feature Store, Ground Truth, Clarify, Model Monitor, Model Debugger, Inference Recommender, Neo
• Data Services - Amazon S3, EFS, FSx, EBS, RDS, DynamoDB, Kinesis, AWS Glue, Glue DataBrew, Glue Data Quality, EMR, Athena, Redshift, Lake Formation, OpenSearch, QuickSight
• AI Services - Amazon Bedrock, Rekognition, Transcribe, Translate, Comprehend, Lex, Kendra, Textract, Personalize, Fraud Detector, Lookout services, Augmented AI (A2I)
• Deployment & Orchestration - SageMaker endpoints (real-time, batch, asynchronous, serverless), SageMaker Pipelines, CodePipeline, CodeBuild, CodeDeploy, EventBridge, Step Functions, Lambda, ECS, EKS, ECR
• Monitoring & Security - CloudWatch, CloudTrail, X-Ray, SageMaker Model Monitor, SageMaker Clarify, IAM, KMS, Secrets Manager, VPC, security groups, network ACLs
• Infrastructure as Code - CloudFormation, AWS CDK, Systems Manager, Auto Scaling, Spot Instances, Reserved Instances, Savings Plans

Exam Sections (4 Main Domains with Weightings):

1. Domain 1: Data Preparation for Machine Learning (ML) (28%) - Ingest and store data (S3, EFS, FSx, Kinesis, data formats like Parquet, JSON, CSV, ORC, Avro, RecordIO). Transform data and perform feature engineering (data cleaning, transformation, encoding, SageMaker Data Wrangler, Feature Store, Glue, DataBrew). Ensure data integrity and prepare data for modeling (bias metrics, data quality validation, SageMaker Clarify, Ground Truth, encryption, compliance).
2. Domain 2: ML Model Development (26%) - Choose a modeling approach (ML algorithms, AWS AI services, SageMaker built-in algorithms, SageMaker JumpStart, Bedrock foundation models, interpretability). Train and refine models (SageMaker script mode, TensorFlow, PyTorch, hyperparameter tuning with AMT, fine-tuning pre-trained models, regularization, ensembling, model versioning with Model Registry). Analyze model performance (evaluation metrics, confusion matrix, F1 score, accuracy, precision, recall, RMSE, ROC, AUC, SageMaker Clarify, Model Debugger, shadow variants).
3. Domain 3: Deployment and Orchestration of ML Workflows (22%) - Select deployment infrastructure (SageMaker endpoints, real-time vs batch, serverless, asynchronous, containers, SageMaker Neo for edge). Create and script infrastructure (CloudFormation, CDK, auto scaling policies, VPC configuration, ECR, ECS, EKS, Lambda). Use automated orchestration tools for CI/CD pipelines (CodePipeline, CodeBuild, CodeDeploy, version control, Gitflow, GitHub Flow, EventBridge, SageMaker Pipelines, automated testing, model retraining).
4. Domain 4: ML Solution Monitoring, Maintenance, and Security (24%) - Monitor model inference (SageMaker Model Monitor, data drift detection, SageMaker Clarify, A/B testing, anomaly detection). Monitor and optimize infrastructure and costs (CloudWatch, CloudTrail, X-Ray, performance metrics, Inference Recommender, Compute Optimizer, Cost Explorer, Trusted Advisor, Budgets, Spot Instances, Savings Plans). Secure AWS resources (IAM roles and policies, SageMaker security features, VPC, subnets, security groups, encryption, least privilege access, auditing and logging).

Key Skills Tested:

• Ingest, transform, validate, and prepare data for ML modeling
• Select general modeling approaches, train models, tune hyperparameters, analyze model performance, and manage model versions
• Choose deployment infrastructure and endpoints, provision compute resources, and configure auto scaling
• Set up CI/CD pipelines to automate orchestration of ML workflows
• Monitor models, data, and infrastructure to detect issues
• Secure ML systems and resources through access controls, compliance features, and best practices
• Use SageMaker capabilities for model building, training, and deployment
• Apply data engineering fundamentals for ML data pipelines
• Implement software engineering best practices for ML code development and deployment
• Optimize ML infrastructure costs and performance