Amazon Web Services Data-Engineer-Associate Exam With Confidence Using Practice Dumps

Option C (LAC-A) is the correct choice because the requirement is to always show the Region-level total even when the visual is drilled down into lower levels (for example, country → city → store). A simple calculated field (Option B) is computed at the row level and then aggregated by the visual, so it will change as the drill-down changes the grain. A table calculation (Option A) is evaluated based on the current visual layout and can vary with the fields placed in the visual, which makes it unreliable for enforcing a fixed “Region total regardless of drill-down.”

A level-aware calculation (aggregate) is specifically intended to “lock” an aggregation to a chosen dimensional level (here: Region). That means you can compute revenue aggregated at the Region level and reuse that value across lower drill levels without it recalculating at city/store granularity. A window LAC (LAC-W) is primarily for windowed analytics (running totals, period-over-period, rank, moving averages) over a partition/order, not for enforcing a fixed dimensional aggregation level. Therefore, LAC-A best matches the requirement.

Reserved concurrency allows you to dedicate a portion of your account’s available concurrency to a specific Lambda function. This ensures that the function always has sufficient capacity, regardless of how many other functions are running. It ' s more cost-effective than provisioned concurrency, which reserves warm environments at a higher cost.

“You can use reserved concurrency to guarantee that your critical Lambda functions have the capacity to run as needed without being throttled by other functions.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Provisioned concurrency would be overkill unless latency is also a concern for cold starts, which isn’t stated in this case.

Option D is correct because Amazon DynamoDB is a serverless NoSQL database that is well suited for semi-structured data, and DynamoDB Accelerator (DAX) is designed to improve read performance for read-heavy applications. AWS documentation states that DAX can improve DynamoDB performance from milliseconds to microseconds, and the DynamoDB guide says DAX is most effective for applications with a high read-to-write ratio, which exactly matches this workload of infrequent writes and frequent reads.

Option A and B are incorrect because Amazon S3 and Iceberg are object storage and table-format solutions, not low-latency transactional databases for millisecond retrieval. Option C is not serverless and requires managing a database cluster, even if optimized reads improve performance. By contrast, DynamoDB is fully managed and serverless, and DAX adds the lowest-latency read path for hot data. The uploaded study guide also identifies DynamoDB as the right fit for dynamic or semi-structured data that requires low-latency access at scale.