Free Top Amazon/AWS DEA-C01 Actual Exam Questions - Question 9 Discussion
build a data lake on AWS. The company wants to load data warehouse tables into Amazon S3 and
synchronize the tables with incremental data that arrives from the data warehouse every day.
Each table has a column that contains monotonically increasing values. The size of each table is less
than 50 GB. The data warehouse tables are refreshed every night between 1 AM and 2 AM. A
business intelligence team queries the tables between 10 AM and 8 PM every day.
Which solution will meet these requirements in the MOST operationally efficient way?
C fits best here since tables are small and a full refresh nightly is simplest.
Maybe C works best here since the tables are small and refreshed nightly. Just overwriting the entire dataset with DMS full load avoids the complexity of incremental logic and fits the BI team's access timing well.
A/C? DMS with CDC (A) seems too complex for once-a-day loads on small tables. Just overwriting with DMS full load (C) is simpler and fits the daily refresh window well.
Not B, because using Glue with job bookmarks requires custom logic and might add complexity. Option C looks simpler by just overwriting the full dataset daily, which fits the nightly refresh and small table size.
Makes sense to go with option B here. Using Glue with a job bookmark on the monotonically increasing column is pretty straightforward and fits well since the data arrives once daily and tables are under 50 GB. This avoids the complexity of CDC and DMS, while still letting you incrementally load only new data without overwriting everything, which seems more efficient operationally. Plus, Glue integrates nicely with S3 and is serverless, so less maintenance hassle.
A/C? Using DMS with CDC (A) might be overkill since the data arrives once daily and tables are small. Just doing a full load refresh with DMS (C) could simplify things without extra incremental logic.
Option D also works well since the tables are small and refreshed during a fixed window; overwriting with AWS Glue daily keeps it straightforward without managing CDC or bookmarks, minimizing operational overhead.
C makes sense because the tables are under 50 GB and refresh just once a day. Doing a full load daily means no complex incremental logic to maintain, which keeps operations simpler. Since the BI team queries start well after the data refresh, overwriting the data in S3 overnight won’t affect them. Options A and B add unnecessary complexity with incremental loads or Glue job bookmarks, which might cause headaches if data arrives late or out of order. D is similar to C but Glue jobs might be more manual to manage compared to DMS full load, which is pretty streamlined for this use case.
C feels best—simple full reloads under 50 GB nightly keep things consistent and low maintenance.
Maybe C is better since full loads of under 50 GB each night aren’t too heavy, and overwriting ensures data consistency without complex logic. It’s simpler and more operationally efficient compared to handling incremental syncs.
I’m thinking option A makes sense here. Using AWS DMS for full load plus CDC can handle both the initial data dump and incremental changes efficiently. Since the data warehouse refresh happens in a known window, DMS can keep the S3 copy updated without needing to overwrite everything daily. Plus, DMS is designed to capture changes reliably, which might be smoother than relying entirely on Glue job bookmarks for incrementals. This setup reduces manual overhead and keeps the data lake in sync with minimal fuss.
I'm leaning toward B here. Using AWS Glue with job bookmarks on the monotonically increasing column sounds like a clean way to handle incremental loads without extra overhead, making it operationally efficient. Anyone else think this beats using DMS for this use case?