Free Practice Questions for AWS Certified Machine Learning Engineer - Associate (MLA-C02) Certification

Skill 1.1.1: Extract data from data sources (for example, Amazon S3, Amazon EBS, Amazon EFS, Amazon RDS, Amazon DynamoDB, Amazon OpenSearch Service). Skill 1.1.2: Make storage decisions and configure storage services based on cost, performance, data structure, and data compliance. Skill 1.1.3: Troubleshoot and debug data ingestion and storage issues that involve capacity and scalability. Skill 1.1.4: Use AWS streaming data sources to ingest data (for example, by using Amazon Kinesis, Apache Flink, Apache Kafka). Skill 1.1.5: Ingest from and write by using appropriate data formats (for example, Apache Parquet, JSON, CSV, ORC) based on data access patterns. Skill 1.1.6: Merge data from multiple sources (for example, by using programming techniques, AWS Glue, Apache Spark). Skill 1.1.7: Configure scalable vector databases for AI applications (for example, OpenSearch Service, Amazon RDS with pgvector, Amazon S3) based on specifications. Skill 1.1.8: Ingest and store diverse data types (for example, text, images, audio) for AI and ML applications. Skill 1.1.9: Ingest data into SageMaker Feature Store.

Skill 1.2.1: Transform data by using AWS tools (for example, AWS Glue, AWS Glue DataBrew, Spark on Amazon EMR, SageMaker Data Wrangler). Skill 1.2.2: Create and manage features by using AWS tools (for example, SageMaker Feature Store). Skill 1.2.3: Transform streaming data (for example, by using AWS Lambda, Spark). Skill 1.2.4: Perform feature engineering (for example, scaling, standardization, feature splitting, binning, log transformation, normalization). Skill 1.2.5: Configure and use embedding models to transform text and image data into numerical representations. Skill 1.2.6: Apply advanced text pre-processing techniques (for example, tokenization, domain-specific augmentation). Skill 1.2.7: Prepare documents for Retrieval Augmented Generation (RAG) applications (for example, chunking strategies, metadata extraction). Skill 1.2.8: Mask, redact, and anonymize data. Skill 1.2.9: Prepare data for FM fine-tuning, continuous pre-training, and model distillation.

Skill 1.3.1: Validate data quality (for example, by using DataBrew, AWS Glue Data Quality). Skill 1.3.2: Label and annotate data. Skill 1.3.3: Identify and mitigate sources of bias in data by using AWS tools and techniques (for example, dataset splitting, shuffling, augmentation). Skill 1.3.4: Optimize multimodal data distributions by applying bias metrics across numeric, text, and image assets. Skill 1.3.5: Resolve class imbalance in numeric, text, and image datasets. Skill 1.3.6: Validate AI training data integrity (for example, prompt-response pair validation, content safety screening). Skill 1.3.7: Clean data (for example, by detecting outliers, imputing missing data, deduplication).

Skill 2.1.1: Evaluate and select appropriate FMs from Amazon Bedrock based on task requirements and performance criteria. Skill 2.1.2: Identify fine-tuning strategies for pre-trained FMs to meet business needs. Skill 2.1.3: Compare and select appropriate ML models, generative AI (GenAI) models, algorithms, and solution templates to meet business needs (for example, interpretability, domain-specific performance, latency). Skill 2.1.4: Evaluate tradeoffs between custom solutions, managed services, pre-trained models, and FMs to meet business needs. Skill 2.1.5: Select Retrieval Augmented Generation (RAG) architecture patterns based on use case requirements. Skill 2.1.6: Assess tradeoffs between ML model performance, training time, and cost. Skill 2.1.7: Assess tradeoffs between AI model performance, latency, and cost. Skill 2.1.8: Apply AWS AI services to solve specific business problems (for example, Amazon Textract, Amazon Rekognition, Amazon Comprehend, Amazon Transcribe).

Skill 2.2.1: Apply Amazon SageMaker AI built-in algorithms and common ML libraries. Skill 2.2.2: Configure SageMaker AI script mode with supported frameworks for simplicity and performance. Skill 2.2.3: Implement hyperparameter optimization (for example, SageMaker AI automatic model tuning [AMT]). Skill 2.2.4: Implement training time reduction techniques (for example, early stopping, distributed training). Skill 2.2.5: Prevent model overfitting, underfitting, and catastrophic forgetting. Skill 2.2.6: Combine multiple ML models to improve performance or reduce cost. Skill 2.2.7: Adjust fundamental hyperparameters (for example, epoch, steps, batch size). Skill 2.2.8: Apply customization techniques for AI solutions (for example, task-specific prompt engineering, fine-tuning). Skill 2.2.9: Optimize retrieval components and embedding models.

Skill 2.3.1: Perform reproducible experiments (for example, by using MLflow on SageMaker AI, Amazon Bedrock evaluations, Amazon Bedrock Prompt Management). Skill 2.3.2: Create model performance baselines and implement drift detection. Skill 2.3.3: Compare the performance of shadow variants to production variants. Skill 2.3.4: Explain model outputs. Skill 2.3.5: Debug model convergence issues. Skill 2.3.6: Apply comprehensive model evaluation techniques for traditional ML and GenAI models. Skill 2.3.7: Implement integrated human evaluation frameworks (for example, human-in-the-loop workflows, text generation quality assessment). Skill 2.3.8: Apply natural language processing (NLP) evaluation metrics (for example, bilingual evaluation understudy [BLEU], Recall-Oriented Understudy for Gisting Evaluation [ROUGE], BERTScore, semantic similarity). Skill 2.3.9: Perform AI evaluation (for example, model output assessment, content quality validation, bias detection, LLM-as-a-judge frameworks). Skill 2.3.10: Configure RAG system monitoring, including retrieval accuracy assessment.

Skill 3.1.1: Select appropriate compute environments and deployment targets. Skill 3.1.2: Select deployment orchestrators and multi-model or multi-container deployment strategies. Skill 3.1.3: Select model inference strategies (for example, real-time and batch processing). Skill 3.1.4: Evaluate and select appropriate foundation model (FM) deployment options. Skill 3.1.5: Deploy models that were built outside of AWS into AWS environments (for example, Amazon SageMaker AI, Amazon Bedrock Custom Model Import). Skill 3.1.6: Deploy and configure agents for specific tasks, integration with other services and tools, and agent communication protocols. Skill 3.1.7: Configure FM deployment, model hosting, and resource allocation. Skill 3.1.8: Apply Retrieval Augmented Generation (RAG) system configurations (for example, retrieval strategies, reranking).

Skill 3.2.1: Optimize resource provisioning between on-demand and provisioned resources for performance and cost efficiency. Skill 3.2.2: Automate compute resource provisioning with integrated communication between stacks and orchestration services. Skill 3.2.3: Build and maintain containers for ML and AI workloads. Skill 3.2.4: Configure SageMaker AI endpoints within VPC network environments. Skill 3.2.5: Deploy and host models programmatically (for example, by using the SageMaker AI SDK for Python, AWS CLI, Boto3). Skill 3.2.6: Select specific metrics for auto scaling implementations. Skill 3.2.7: Create and manage Amazon Bedrock knowledge bases with vector database configurations, document indexing, and retrieval optimization. Skill 3.2.8: Implement retrieval pipelines to meet business needs. Skill 3.2.9: Implement agent state management systems. Skill 3.2.10: Implement AI-specific resource scaling for GPU workloads. Skill 3.2.11: Deploy agentic workflow infrastructure.

Skill 3.3.1: Implement automated deployment strategies and rollback actions. Skill 3.3.2: Configure and troubleshoot AWS CodeBuild, AWS CodeCommit, AWS CodeDeploy, AWS CodePipeline, and AWS CodeConnections. Skill 3.3.3: Configure training and inference jobs. Skill 3.3.4: Configure automated testing strategies within CI/CD pipelines for traditional ML and AI workloads. Skill 3.3.5: Build and integrate mechanisms to re-train models. Skill 3.3.6: Manage model versions for repeatability and audits (for example, SageMaker Model Registry, MLflow on SageMaker AI). Skill 3.3.7: Manage prompts (for example, Amazon Bedrock Prompt Management). Skill 3.3.8: Implement automated agent deployment pipelines and agent version management. Skill 3.3.9: Implement AI model testing frameworks, including prompt testing. Skill 3.3.10: Configure FM deployment automation with fine-tuned model versioning. Skill 3.3.11: Configure AI-specific pipeline orchestration for RAG system updates and knowledge base refresh cycles.

Skill 4.1.1: Monitor model performance in production by using Amazon CloudWatch generative AI observability, Amazon Bedrock Model Evaluation, and drift detection pipelines. Skill 4.1.2: Monitor workflows to detect anomalies or errors in data processing or model inference. Skill 4.1.3: Detect changes in data distribution that can affect model performance. Skill 4.1.4: Monitor model performance in production by using A/B testing. Skill 4.1.5: Monitor and automate the management of agent performance and coordination (for example, coordination failure detection, truncated streaming, tool failures). Skill 4.1.6: Configure AI-specific performance monitoring for foundation models (FMs), such as Amazon Bedrock evaluations.

Skill 4.2.1: Select inference instance families to optimize performance and cost. Skill 4.2.2: Configure and use tools to troubleshoot and analyze resources (for example, Amazon CloudWatch, Amazon Bedrock AgentCore Observability, AWS X-Ray). Skill 4.2.3: Set up dashboards to monitor performance metrics. Skill 4.2.4: Optimize capacity for cost, performance, and reliability. Skill 4.2.5: Optimize costs and set cost quotas by using appropriate cost management tools. Skill 4.2.6: Optimize infrastructure costs by selecting purchasing options. Skill 4.2.7: Evaluate cost implications of using FMs for inference in production. Skill 4.2.8: Monitor agent resource consumption patterns. Skill 4.2.9: Manage FM inference costs with usage optimization. Skill 4.2.10: Monitor AI-specific cost patterns (for example, token usage optimization, embedding computation costs, vector database storage optimization).

Skill 4.3.1: Secure continuous integration and continuous delivery (CI/CD) pipelines by checking for code and image vulnerabilities (for example, by using Amazon CodeGuru, Amazon Inspector). Skill 4.3.2: Configure least privilege access to ML and AI artifacts. Skill 4.3.3: Configure IAM policies and roles for users and applications in ML and AI systems. Skill 4.3.4: Configure comprehensive monitoring, auditing, compliance, and logging for ML and AI systems (for example, AWS CloudTrail, AWS Config). Skill 4.3.5: Troubleshoot and debug security issues in ML and AI systems. Skill 4.3.6: Create VPCs, subnets, and security groups to securely isolate ML and AI systems. Skill 4.3.7: Identify and mitigate security risks and vulnerabilities in ML and AI systems. Skill 4.3.8: Select the appropriate credential type to access FMs (for example, Amazon Bedrock API keys, IAM credentials). Skill 4.3.9: Implement safeguards and sensitive data protection to meet application requirements and responsible AI policies (for example, by using Amazon Bedrock Guardrails).