AWS Certified Solutions Architect Associate SAA-C03 Practice Question
Your application experiences unpredictable surges in read traffic, causing latency spikes. The dataset is too large to be fully cached in memory, and most of the data is not frequently accessed. However, when data is updated, it must reflect immediately across all cache nodes to ensure data consistency. How should you design the caching strategy?
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Increase the number of Amazon RDS read replicas and implement a custom caching layer with application-level logic for data consistency.
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Implement Amazon ElastiCache for Memcached with an eviction policy that considers the frequency of data access, and handle updates using a write-through approach.
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Deploy an Amazon DynamoDB Accelerator (DAX) cluster to work with DynamoDB, providing immediate consistency for all cache nodes with a write-through caching strategy.
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Use Amazon ElastiCache for Redis with a lazy-loading strategy and set up an event-driven mechanism to update cache on data changes.
Amazon DynamoDB Accelerator (DAX) is the right solution because it is specifically designed for Amazon DynamoDB and provides a fully managed, highly available, in-memory cache that delivers up to a 10x performance improvement. It supports write-through and TTL features, ensuring data consistency across all cache nodes even during unpredictable surges in read traffic. Incorrect options include ElastiCache for Redis, which does not inherently ensure data consistency with the primary data store after updates, and Amazon RDS read replicas, which increase read capacity but do not address the need for a caching layer or immediate consistency across cache nodes. Using ElastiCache for Memcached might seem suitable, but it would not provide the immediate data consistency required after updates.
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Why is immediate data consistency important in caching strategies?