AWS Certified Associate SAA-C03 Exam Questions and Answers PDF

Predictive scaling automatically analyzes historical workload patterns, forecasts future capacity needs, and launches instances ahead of time. AWS documentation states that predictive scaling is designed for workloads with recurring, cyclical patterns—such as scheduled weekly batch jobs.

It also supports " pre-launching " capacity before peak demand. This eliminates manual trend analysis and delivers the lowest operational overhead.

Scheduled scaling (Option B) works but requires manual calculation of capacity numbers and updating if patterns change. Predictive scaling removes this burden entirely.

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A. Fixed desired instances:Does not adapt to traffic load fluctuations, leading to inefficiencies.

B. Larger EC2 instances:Increases costs unnecessarily.

C. Target tracking scaling policy:Adjusts capacity based on actual demand, optimizing costs.

D. Instance store volumes:Not persistent and unsuitable for shared data across instances.

This question centers on improving scalability and global access performance.

Auto Scaling groups enable EC2 instances to scale dynamically in response to demand, ensuring availability during peak hours without manual intervention. Amazon RDS read replicas offload read traffic, improving read throughput and reducing latency on the primary database instance. Deploying Amazon CloudFront with S3 as origin accelerates delivery of static transaction documents globally by caching content at edge locations, reducing latency for users worldwide.

Option B focuses on vertical scaling (larger instances) and caching with ElastiCache, but it does not address global content delivery optimally. AWS Global Accelerator accelerates network traffic but is better suited for accelerating TCP and UDP traffic; CloudFront is generally preferred for HTTP content delivery.

Option C migrates workloads to Lambda and Aurora global databases, which is an advanced and potentially costly redesign that may not be necessary. Option D suggests moving to ECS and multi-AZ RDS but does not address global content delivery efficiently.

Therefore, option A uses proven scalability and caching best practices aligned with AWS Well-Architected Framework pillars for performance and operational excellence.

AWS documentation states that Amazon Macie is the managed service designed to automatically identify and classify sensitive data stored in Amazon S3, including PII and healthcare-related identifiers.

Storing logs in Amazon S3 provides scalable, durable storage, and Amazon Athena can directly query JSON data stored in S3 using SQL.

Amazon Inspector (Option D) is for vulnerability scanning and does not identify sensitive data. DynamoDB with KMS (Option C) cannot detect sensitive information. EBS (Option B) requires custom tooling and does not support serverless SQL querying.

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