5.4. Reflection Checkpoint
Key Takeaways
Before proceeding, ensure you can:
- Distinguish between data drift, concept drift, and prediction drift—and identify which SageMaker tools detect each
- Configure SageMaker Model Monitor end-to-end: data capture, baselining, scheduled monitoring, and CloudWatch alarms
- Select the appropriate testing strategy (A/B testing, shadow testing, canary deployment) based on risk tolerance
- Map observability needs to the correct AWS tool: CloudWatch for metrics, CloudWatch Logs for debugging, X-Ray for tracing, CloudTrail for auditing
- Choose the right pricing model (Spot, On-Demand, Savings Plans, Serverless) for each ML workload type
- Diagnose latency issues by distinguishing between cold starts, model size, scaling lag, and quota limits
- Apply defense-in-depth security: IAM (identity) + VPC (network) + KMS (data) working together
- Distinguish between VPC mode and network isolation and know when each is required
- Select the correct encryption approach based on key control requirements
- Map auditing needs to CloudTrail (who did what), Config (is it configured correctly), and Macie (what data is present)
Connecting Forward
In the next phase, you'll consolidate everything you've learned into exam-ready strategies. Phase 6 provides time management techniques, decision tree cheat sheets for rapid service selection, and mixed-topic practice questions that simulate the actual exam experience. The goal is to convert your understanding into exam reflexes.
Self-Check Questions
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A production model's accuracy has dropped from 94% to 82% over the past month. CloudWatch shows no infrastructure issues (CPU, memory, latency all normal). Describe your diagnostic approach using SageMaker tools, including what you'd check first, second, and third.
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A team runs three daily training jobs (4 hours each, GPU-intensive) and maintains two real-time endpoints (one high-traffic 24/7, one low-traffic business hours only). Design a cost optimization strategy using at least three different pricing mechanisms.
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A healthcare ML system processes patient records containing PHI. The system includes S3 storage, SageMaker training, a real-time endpoint, and a CodePipeline CI/CD workflow. Describe the security controls needed at each layer (identity, network, data) for each component.
