A company has an Amazon DynamoDB table that contains records of users that have signed up for a trial of the company's product. The company is using a spreadsheet to track data about the product trial

Question

Options

• ACreate a DynamoDB Accelerator (DAX) cluster from the table. Set the view type to old image.
• BCreate a DynamoDB Accelerator (DAX) cluster from the table. Set the view type to new image.
• CEnable a DynamoDB stream for the table. Set the view type to new image. Create an AWS
• DEnable a DynamoDB stream for the table. Set the view type to old image. Create an AWS

• A
  3% (1)
• B
  10% (3)
• C
  81% (25)
• D
  6% (2)

Explanation

Option C is correct because DynamoDB Streams captures item-level changes (inserts, updates, deletes) in real time - exactly what's needed to detect when trials begin, change, or end. Setting the view type to new image means each stream record contains the item's state after the change, giving you the current data to push into the spreadsheet. The truncated answer implies an AWS Lambda function consumes the stream and performs the spreadsheet update.

Option D is wrong because the old image view type captures the item's state before the change, which is the stale data you're trying to replace - useless for keeping a spreadsheet current.

Options A and B are both wrong because DAX (DynamoDB Accelerator) is an in-memory read cache designed to reduce read latency; it has no mechanism for capturing or emitting change events. "View type" is not a DAX concept at all - it belongs to DynamoDB Streams, making these options nonsensical.

Memory tip: Think of Streams as a changelog and DAX as a fast-read cache. When you need to react to changes, always reach for Streams. Then remember "new image = what it looks like NOW" - the version you actually want to sync.

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