Free Practice Questions for AWS Certified Machine Learning Engineer - Associate (MLA-C01) Certification

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

Skills in:

- Extracting data from storage (for example, Amazon S3, Amazon Elastic Block Store [Amazon EBS], Amazon EFS, Amazon RDS, Amazon DynamoDB) by using relevant AWS service options (for example, Amazon S3 Transfer Acceleration, Amazon EBS Provisioned IOPS) - Choosing appropriate data formats (for example, Parquet, JSON, CSV, ORC) based on data access patterns - Ingesting data into Amazon SageMaker Data Wrangler and SageMaker Feature Store - Merging data from multiple sources (for example, by using programming techniques, AWS Glue, Apache Spark) - Troubleshooting and debugging data ingestion and storage issues that involve capacity and scalability - Making initial storage decisions based on cost, performance, and data structure

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

Skills in:

- Transforming data by using AWS tools (for example, AWS Glue, DataBrew, Spark running on Amazon EMR, SageMaker Data Wrangler) - Creating and managing features by using AWS tools (for example, SageMaker Feature Store) - Validating and labeling data by using AWS services (for example, SageMaker Ground Truth, Amazon Mechanical Turk)

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)

Skills in:

- Validating data quality (for example, by using DataBrew and AWS Glue Data Quality) - Identifying and mitigating sources of bias in data (for example, selection bias, measurement bias) by using AWS tools (for example, SageMaker Clarify) - Preparing data to reduce prediction bias (for example, by using dataset splitting, shuffling, and augmentation) - Configuring data to load into the model training resource (for example, Amazon EFS, Amazon FSx)

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 - Amazon SageMaker AI built-in algorithms and when to apply them

Skills in:

- Assessing available data and problem complexity to determine the feasibility of an ML solution - Comparing and selecting appropriate ML models or algorithms to solve specific problems - Choosing built-in algorithms, foundation models, and solution templates (for example, in SageMaker JumpStart and Amazon Bedrock) - Selecting models or algorithms based on costs - Selecting AI services to solve common business needs

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 AI into SageMaker AI

Skills in:

- Using SageMaker AI built-in algorithms and common ML libraries to develop ML models - Using SageMaker AI script mode with SageMaker AI supported frameworks to train models (for example, TensorFlow, PyTorch) - Using custom datasets to fine-tune pre-trained models (for example, Amazon Bedrock, SageMaker JumpStart) - Performing hyperparameter tuning (for example, by using SageMaker AI automatic model tuning [AMT]) - Integrating automated hyperparameter optimization capabilities - Preventing model overfitting, underfitting, and catastrophic forgetting (for example, by using regularization techniques, feature selection) - Combining multiple training models to improve performance (for example, ensembling, stacking, boosting) - Reducing model size (for example, by altering data types, pruning, updating feature selection, compression) - Managing model versions for repeatability and audits (for example, by using the SageMaker Model Registry)

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

Skills in:

- Selecting and interpreting evaluation metrics and detecting model bias - Assessing tradeoffs between model performance, training time, and cost - Performing reproducible experiments by using AWS services - Comparing the performance of a shadow variant to the performance of a production variant - Using SageMaker Clarify to interpret model outputs - Using SageMaker Model Debugger to debug model convergence

Knowledge of:

- Deployment best practices (for example, versioning, rollback strategies) - AWS deployment services (for example, Amazon SageMaker AI) - 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)

Skills in:

- Evaluating performance, cost, and latency tradeoffs - Choosing the appropriate compute environment for training and inference based on requirements (for example, GPU or CPU specifications, processor family, networking bandwidth) - Selecting the correct deployment orchestrator (for example, Apache Airflow, SageMaker Pipelines) - Selecting multi-model or multi-container deployments - Selecting the correct deployment target (for example, SageMaker AI endpoints, Kubernetes, Amazon Elastic Container Service [Amazon ECS], Amazon Elastic Kubernetes Service [Amazon EKS], AWS Lambda) - Choosing model deployment strategies (for example, real time, batch)

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 AI endpoint auto scaling policies to meet scalability requirements (for example, based on demand, time)

Skills in:

- Applying best practices to enable maintainable, scalable, and cost-effective ML solutions (for example, automatic scaling on SageMaker AI endpoints, dynamically adding Spot Instances, by using Amazon EC2 instances, by using Lambda behind the endpoints) - Automating the provisioning of compute resources, including communication between stacks (for example, by using CloudFormation, AWS CDK) - Building and maintaining containers (for example, Amazon Elastic Container Registry [Amazon ECR], Amazon EKS, Amazon ECS, by using bring your own container [BYOC] with SageMaker AI) - Configuring SageMaker AI endpoints within the VPC network - Deploying and hosting models by using the SageMaker AI SDK - Choosing specific metrics for auto scaling (for example, model latency, CPU utilization, invocations per instance)

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

Skills in:

- Configuring and troubleshooting CodeBuild, CodeDeploy, and CodePipeline, including stages - Applying continuous deployment flow structures to invoke pipelines (for example, Gitflow, GitHub Flow) - Using AWS services to automate orchestration (for example, to deploy ML models, automate model building) - Configuring training and inference jobs (for example, by using Amazon EventBridge rules, SageMaker Pipelines, CodePipeline) - Creating automated tests in CI/CD pipelines (for example, integration tests, unit tests, end-to-end tests) - Building and integrating mechanisms to retrain models

Knowledge of:

- Drift in ML models - Techniques to monitor data quality and model performance - Design principles for ML lenses relevant to monitoring

Skills in:

- Monitoring models in production (for example, by using Amazon SageMaker Model Monitor) - Monitoring workflows to detect anomalies or errors in data processing or model inference - Detecting changes in the distribution of data that can affect model performance (for example, by using SageMaker Clarify) - Monitoring model performance in production by using A/B testing

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)

Skills in:

- Configuring and using tools to troubleshoot and analyze resources (for example, CloudWatch Logs, CloudWatch alarms) - Creating CloudTrail trails - Setting up dashboards to monitor performance metrics (for example, by using Amazon QuickSight, CloudWatch dashboards) - Monitoring infrastructure (for example, by using Amazon EventBridge events) - Rightsizing instance families and sizes (for example, by using SageMaker AI Inference Recommender and AWS Compute Optimizer) - Monitoring and resolving latency and scaling issues - Preparing infrastructure for cost monitoring (for example, by applying a tagging strategy) - Troubleshooting capacity concerns that involve cost and performance (for example, provisioned concurrency, service quotas, auto scaling) - Optimizing costs and setting cost quotas by using appropriate cost management tools (for example, AWS Cost Explorer, AWS Trusted Advisor, AWS Budgets) - Optimizing infrastructure costs by selecting purchasing options (for example, Spot Instances, On-Demand Instances, Reserved Instances, SageMaker AI Savings Plans)

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 AI security and compliance features - Controls for network access to ML resources - Security best practices for CI/CD pipelines

Skills in:

- Configuring least privilege access to ML artifacts - Configuring IAM policies and roles for users and applications that interact with ML systems - Monitoring, auditing, and logging ML systems to ensure continued security and compliance - Troubleshooting and debugging security issues - Building VPCs, subnets, and security groups to securely isolate ML systems