Amazon AWS Certified Machine Learning Engineer - Associate (MLA-C01) Exam Questions

Amazon AWS Certified Machine Learning Engineer - Associate Exam Objectives

1. 1

   1.0 Domain 1: Data Preparation for Machine Learning (ML)

   • Task 1.1: Statement 1.1: Ingest and store data. Knowledge of: Data formats and ingestion mechanisms (for example, validated and non-validated formats, Apache Parquet, JSON, CSV, Apache ORC, Apache Avro, RecordIO) How to use the core AWS data sources (for example, Amazon S3, Amazon Elastic File System [Amazon EFS], Amazon FSx for NetApp ONTAP) How to use AWS streaming data sources to ingest data (for example, Amazon Kinesis, Apache Flink, Apache Kafka) AWS storage options, including use cases and tradeoffs
   • Task 1.2: Statement 1.2: Transform data and perform feature engineering. Knowledge of: Data cleaning and transformation techniques (for example, detecting and treating outliers, imputing missing data, combining, deduplication) Feature engineering techniques (for example, data scaling and standardization, feature splitting, binning, log transformation, normalization) Encoding techniques (for example, one-hot encoding, binary encoding, label encoding, tokenization) Tools to explore, visualize, or transform data and features (for example, SageMaker Data Wrangler, AWS Glue, AWS Glue DataBrew) Services that transform streaming data (for example, AWS Lambda, Spark) Data annotation and labeling services that create high-quality labeled datasets
   • Task 1.3: Statement 1.3: Ensure data integrity and prepare data for modeling. Knowledge of: Pre-training bias metrics for numeric, text, and image data (for example, class imbalance [CI], difference in proportions of labels [DPL]) Strategies to address CI in numeric, text, and image datasets (for example, synthetic data generation, resampling) Techniques to encrypt data Data classification, anonymization, and masking Implications of compliance requirements (for example, personally identifiable information [PII], protected health information [PHI], data residency)
2. 2

   2.0 Domain 2: ML Model Development

   • Task 2.1: Statement 2.1: Choose a modeling approach. Knowledge of: Capabilities and appropriate uses of ML algorithms to solve business problems How to use AWS artificial intelligence (AI) services (for example, Amazon Translate, Amazon Transcribe, Amazon Rekognition, Amazon Bedrock) to solve specific business problems How to consider interpretability during model selection or algorithm selection SageMaker built-in algorithms and when to apply them
   • Task 2.2: Statement 2.2: Train and refine models. Knowledge of: Elements in the training process (for example, epoch, steps, batch size) Methods to reduce model training time (for example, early stopping, distributed training) Factors that influence model size Methods to improve model performance Benefits of regularization techniques (for example, dropout, weight decay, L1 and L2) Hyperparameter tuning techniques (for example, random search, Bayesian optimization) Model hyperparameters and their effects on model performance (for example, number of trees in a tree-based model, number of layers in a neural network) Methods to integrate models that were built outside SageMaker into SageMaker
   • Task 2.3: Statement 2.3: Analyze model performance. Knowledge of: Model evaluation techniques and metrics (for example, confusion matrix, heat maps, F1 score, accuracy, precision, recall, Root Mean Square Error [RMSE], receiver operating characteristic [ROC], Area Under the ROC Curve [AUC]) Methods to create performance baselines Methods to identify model overfitting and underfitting Metrics available in SageMaker Clarify to gain insights into ML training data and models Convergence issues
3. 3

   3.0 Domain 4: ML Solution Monitoring, Maintenance, and Security

   • Task 3.1: Statement 4.1: Monitor model inference. Knowledge of: Drift in ML models Techniques to monitor data quality and model performance Design principles for ML lenses relevant to monitoring
   • Task 3.2: Statement 4.2: Monitor and optimize infrastructure and costs. Knowledge of: Key performance metrics for ML infrastructure (for example, utilization, throughput, availability, scalability, fault tolerance) Monitoring and observability tools to troubleshoot latency and performance issues (for example, AWS X-Ray, Amazon CloudWatch Lambda Insights, Amazon CloudWatch Logs Insights) How to use AWS CloudTrail to log, monitor, and invoke re-training activities Differences between instance types and how they affect performance (for example, memory optimized, compute optimized, general purpose, inference optimized) Capabilities of cost analysis tools (for example, AWS Cost Explorer, AWS Billing and Cost Management, AWS Trusted Advisor) Cost tracking and allocation techniques (for example, resource tagging)
   • Task 3.3: Statement 4.3: Secure AWS resources. Knowledge of: IAM roles, policies, and groups that control access to AWS services (for example, AWS Identity and Access Management [IAM], bucket policies, SageMaker Role Manager) SageMaker security and compliance features Controls for network access to ML resources Security best practices for CI/CD pipelines
4. 4

   4.0 Domain 3: Deployment and Orchestration of ML Workflows

   • Task 4.1: Statement 3.1: Select deployment infrastructure based on existing architecture and requirements. Knowledge of: Deployment best practices (for example, versioning, rollback strategies) AWS deployment services (for example, SageMaker) Methods to serve ML models in real time and in batches How to provision compute resources in production environments and test environments (for example, CPU, GPU) Model and endpoint requirements for deployment endpoints (for example, serverless endpoints, real-time endpoints, asynchronous endpoints, batch inference) How to choose appropriate containers (for example, provided or customized) Methods to optimize models on edge devices (for example, SageMaker Neo)
   • Task 4.2: Statement 3.2: Create and script infrastructure based on existing architecture and requirements. Knowledge of: Difference between on-demand and provisioned resources How to compare scaling policies Tradeoffs and use cases of infrastructure as code (IaC) options (for example, AWS CloudFormation, AWS Cloud Development Kit [AWS CDK]) Containerization concepts and AWS container services How to use SageMaker endpoint auto scaling policies to meet scalability requirements (for example, based on demand, time)
   • Task 4.3: Statement 3.3: Use automated orchestration tools to set up continuous integration and continuous delivery (CI/CD) pipelines. Knowledge of: Capabilities and quotas for AWS CodePipeline, AWS CodeBuild, and AWS CodeDeploy Automation and integration of data ingestion with orchestration services Version control systems and basic usage (for example, Git) CI/CD principles and how they fit into ML workflows Deployment strategies and rollback actions (for example, blue/green, canary, linear) How code repositories and pipelines work together