AWS Machine Learning Associate Exam Walkthrough 107 Q&A 81 to 100

AWS Machine Learning Associate Exam Walkthrough 107 Q&A 81-100 - October 14
VIEW RECORDING: https://fathom.video/share/oHvXbwzeyV...
Meeting Purpose

Review AWS Machine Learning exam questions 81-100 with detailed explanations and analysis.

Key Takeaways

Covered 20 in-depth AWS ML exam questions, focusing on model evaluation, security, deployment, and optimization
Emphasized practical applications of ML concepts in AWS services like SageMaker, S3, and Glue
Highlighted importance of understanding metrics, data handling, and cost-effective strategies in ML workflows

Topics

Model Evaluation and Metrics

Accuracy: Best for binary classification when both classes equally important
Semi-supervised learning: Ideal for anomaly detection with evolving attack types
Target encoding: Efficient for high-cardinality categorical variables
Precision & Recall, Accuracy & F1 score: Key metrics for binary classification tasks
Object Detection: SageMaker algorithm for item identification and localization in images

Security and Compliance

IAM policies: Granular access control for sensitive data in SageMaker endpoints
Encryption: SSE-KMS requires KMS permissions (Decrypt/Encrypt) for SageMaker execution roles
Network isolation: Separate SageMaker domains provide strongest project isolation
VPC endpoints: Enable private S3 connectivity for network-isolated training jobs

Model Deployment and Optimization

SageMaker inference components: Support accelerated instances, individual scaling per model
SageMaker Savings Plan: Up to 64% discount for consistent, long-term workloads
Target tracking scaling: Efficient auto-scaling for unpredictable traffic spikes
Bayesian optimization: Cost-effective hyperparameter tuning for long-running training jobs

Data Handling and Processing

S3: Foundation for data lakes, cost-effective storage for historical data
Glue: Serverless ETL service for data transformation and feature engineering
Athena: Serverless SQL queries on S3 for exploratory data analysis
SageMaker ML lineage tracking: Automatic capture of dataset-model relationships for auditing

Next Steps

Review and understand each question's explanation thoroughly
Focus on areas where incorrect answers were chosen to identify knowledge gaps
Practice applying concepts to real-world scenarios using AWS services
Continue studying remaining exam topics and take practice tests