Stream processing patterns in DynamoDB - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

Identify the correct DynamoDB Stream event type for a new item insertion

When a new item is added to a DynamoDB table with streams enabled, which event type will appear in the stream record?

A MODIFY

B INSERT

C REMOVE

D UPDATE

💡 Hint

Think about what event type corresponds to adding new data.

The INSERT event type is generated when a new item is added to the table. MODIFY is for updates, REMOVE is for deletions, and UPDATE is not a valid DynamoDB stream event type.

🧠 Conceptual

intermediate

Understanding the purpose of DynamoDB Streams in event-driven architectures

Why are DynamoDB Streams commonly used in event-driven architectures?

A To capture and react to data changes in real-time for downstream processing

B To store backups of the entire DynamoDB table periodically

C To improve query performance by caching data

D To encrypt data at rest automatically

💡 Hint

Think about what streams provide besides just storing data.

DynamoDB Streams capture data modification events in real-time, enabling other services to react to changes immediately. They are not used for backups, caching, or encryption.

📝 Syntax

advanced

Identify the correct AWS Lambda event source mapping filter for DynamoDB Streams

Which JSON filter pattern correctly filters DynamoDB Stream events to only process INSERT events in an AWS Lambda event source mapping?

A {"eventName": ["INSERT"]}

B {"eventName": "INSERT"}

C {"Records": [{"eventName": "INSERT"}]}

D {"eventName": {"equals": "INSERT"}}

💡 Hint

AWS Lambda event source filters expect arrays for matching values.

The correct filter pattern uses an array for the eventName key to match INSERT events. Option A is invalid because it uses a string instead of an array. Option A is incorrect because the filter does not include the Records wrapper. Option A uses an invalid syntax.

❓ optimization

advanced

Optimize processing of high-volume DynamoDB Streams to avoid Lambda throttling

You have a DynamoDB table with a high write rate generating many stream records. Which approach best helps avoid AWS Lambda throttling when processing these streams?

A Increase the number of shards by splitting the table into multiple partitions

B Set the Lambda batch size to 1 and increase the number of concurrent Lambda executions

C Use DynamoDB Streams with enhanced fan-out to allow multiple consumers to process records independently

D Disable DynamoDB Streams and poll the table directly for changes

💡 Hint

Consider how to scale stream consumers efficiently.

Enhanced fan-out allows multiple Lambda functions or consumers to receive stream records with dedicated throughput, reducing throttling. Increasing shards by splitting the table is not directly possible. Setting batch size to 1 may increase overhead. Disabling streams removes the event-driven benefit.

🔧 Debug

expert

Diagnose why a Lambda function triggered by DynamoDB Streams processes duplicate records

A Lambda function triggered by DynamoDB Streams sometimes processes the same record multiple times. What is the most likely cause?

A The Lambda function is configured with an incorrect IAM role

B DynamoDB Streams duplicates records by design to ensure delivery

C The table's stream view type is set to KEYS_ONLY instead of NEW_IMAGE

D The Lambda function does not checkpoint processed records, causing retries on failure

💡 Hint

Think about how Lambda handles stream record processing and retries.

Lambda retries processing records if the function does not successfully checkpoint or acknowledge them, leading to duplicates. DynamoDB Streams does not duplicate records arbitrarily. Stream view type affects data content, not duplication. IAM role issues cause permission errors, not duplicates.