DAX (DynamoDB Accelerator) caching - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

How does DAX improve DynamoDB read performance?

DAX is a caching service for DynamoDB. Which of the following best explains how DAX improves read performance?

A DAX stores frequently accessed data in memory close to the application, reducing read latency by avoiding repeated calls to DynamoDB.

B DAX compresses data stored in DynamoDB tables to reduce storage size and speed up reads.

C DAX automatically indexes all DynamoDB tables to speed up query operations.

D DAX duplicates DynamoDB tables in another region to improve read speed.

💡 Hint

Think about what caching means and how it helps with repeated data access.

DAX caches frequently read data in memory, so the application can get data faster without always querying DynamoDB. This reduces latency and improves performance.

❓ query_result

intermediate

What is the output of a DAX cache hit?

Assume a DynamoDB table has an item with key 'UserID' = 123 and attribute 'Name' = 'Alice'. A DAX client queries for this item twice in a row. What happens on the second query?

A The second query returns a partial item missing some attributes.

B The second query returns the item from DAX cache instantly without calling DynamoDB.

C The second query returns an error because DAX only caches writes.

D The second query always calls DynamoDB again to ensure fresh data.

💡 Hint

Think about what a cache hit means in caching systems.

On the second query, DAX returns the cached item from memory, avoiding a call to DynamoDB and speeding up the response.

📝 Syntax

advanced

Identify the correct way to initialize a DAX client in Python

Which of the following Python code snippets correctly initializes a DAX client to connect to a DAX cluster endpoint?

DynamoDB

from amazon_dax import AmazonDaxClient

# Choose the correct initialization

A client = AmazonDaxClient('dax-cluster.example.com:8111')

B client = AmazonDaxClient(endpoint_url='dax-cluster.example.com:8111')

C client = AmazonDaxClient(url='dax-cluster.example.com:8111')

D client = AmazonDaxClient(endpoints=['dax-cluster.example.com:8111'])

💡 Hint

Check the official parameter name for the DAX client constructor.

The AmazonDaxClient expects the parameter 'endpoints' as a list of cluster endpoint strings. Using 'endpoint_url' or 'url' is incorrect.

❓ optimization

advanced

How to reduce stale data risk when using DAX caching?

DAX caches data to improve read speed, but cached data can become stale if the underlying DynamoDB table changes. Which strategy best reduces the risk of stale data?

A Manually clear the entire DAX cache every hour regardless of data changes.

B Disable DAX caching for all read operations to always get fresh data.

C Configure DAX with a short TTL (time-to-live) so cached items expire quickly and refresh from DynamoDB often.

D Use DAX only for write operations and never for reads.

💡 Hint

Think about how caching systems handle data freshness with expiration times.

Setting a short TTL ensures cached data expires quickly, so DAX fetches fresh data from DynamoDB more often, reducing stale data risk.

🔧 Debug

expert

Why does this DAX query sometimes return outdated data?

An application uses DAX caching for a DynamoDB table. After updating an item in DynamoDB directly (not through DAX), subsequent reads via DAX sometimes return old data. What is the most likely cause?

A DAX cache is not automatically invalidated when DynamoDB is updated outside DAX, causing stale reads until cache expires.

B DAX does not support caching for tables with more than 1000 items.

C The application is using the wrong partition key in queries, causing inconsistent results.

D DAX requires manual cache refresh commands after every write to DynamoDB.

💡 Hint

Consider how DAX cache invalidation works when updates bypass DAX.

DAX cache invalidation only happens when writes go through DAX. Updates made directly to DynamoDB do not notify DAX, so cached data can become stale until TTL expires.