Free Top Amazon/AWS DEA-C01 Actual Exam Questions - Question 12 Discussion
analysis.
A data engineering team needs to use Amazon QuickSight to perform the analysis and build
dashboards. A data engineer needs to extract the data from the SaaS applications and make the data
available for QuickSight queries.
Which solution will meet these requirements in the MOST operationally efficient way?
Maybe D is the simplest if the SaaS apps already support exports, but it’s manual and won’t scale well for frequent updates or multiple apps. Not very efficient long term.
It’s C. AppFlow automates data extraction and transfer to S3 without custom coding, so it’s way less hands-on than building Lambda functions or managing Athena connectors. Much better for operational efficiency.
B imo, because using Athena federated queries with Lambda connectors lets you query SaaS data live without managing data storage or ETL pipelines. It keeps things simple and efficient.
A. Using Lambda to call APIs and manually manage S3 and Glue seems like a lot of overhead compared to using built-in tools like AppFlow or Athena connectors. This feels less operationally efficient overall.
Option B avoids managing data storage and lets QuickSight query live data directly.
I think C fits best since AppFlow handles SaaS data loads without custom code. C
B. Running federated queries via Athena connectors avoids the need to store data separately, which makes it more streamlined and less operational overhead than managing S3 buckets or AppFlow schedules.
A/B? Using Lambda with Athena data source connectors (B) seems pretty smooth since it lets you run federated queries directly on the SaaS apps without storing extra copies. That could cut down on storage and make data fresher. On the other hand, Lambda functions calling APIs and storing in S3 (A) might mean more management overhead and added ETL steps. So if the SaaS apps support it, B looks more operationally efficient because it reduces data movement and maintenance.
C. Besides operational efficiency, AppFlow is designed specifically for SaaS integrations and can handle data extraction and scheduling without needing custom code like Lambda would. That reduces maintenance overhead. Also, using Glue to catalog the data makes it easy for QuickSight to query. Lambda options (A or B) are more hands-on and could get complicated if APIs change or if scaling is needed. D is the least practical since manual Excel exports don’t fit well with automation and timely analysis. So overall, C feels like the cleanest, most maintainable choice here.
Maybe A could work since Lambda can automate API calls directly, but it might require more maintenance compared to AppFlow. Still, it’s pretty flexible for different SaaS sources.
Option C seems best-AppFlow automates SaaS data transfers efficiently.