Free AWS AIP-C01 Actual Exam Questions - Question 7 Discussion

A, because IoT Core with Firehose simplifies direct streaming to S3 without extra processing layers.

It’s A since IoT Core with Firehose handles unstable connections better than D’s extra Lambda step.

C/D? Using API Gateway and Lambda (C) might add unnecessary complexity and management overhead compared to IoT Core. D’s approach with IoT Core and Kinesis plus Lambda adds more moving parts, which could increase management and latency. A stands out for minimal infrastructure because Firehose is fully managed and built for reliable delivery. B involves managing Greengrass on devices, which goes against minimizing infrastructure management. So, between avoiding extra layers and keeping it scalable and secure, A looks cleaner.

A, because Firehose handles buffering well with unstable connectivity.

A. Besides minimizing infrastructure management, A leverages AWS IoT Core and Firehose, which are fully managed services designed for high scalability and secure ingestion of telemetry data. This removes the need to manage servers or write complex Lambda functions as in C and D. Also, Firehose handles buffering and retries, which is ideal for unstable connectivity scenarios. While B offers local preprocessing, it requires managing Greengrass on each device, adding overhead that the question wants to avoid. So A seems like the cleanest fit for scalable, secure, and low-management telemetry inge

A/B? A handles data reliably with less setup, but B’s local preprocessing could help with unstable connections if devices support it. Still, B might need more management, which the question wants to minimize.

Option D could be useful if you want more control over processing before storing, but it adds complexity with Kinesis and Lambda. The question emphasizes minimizing management and handling unstable connectivity, which IoT Core plus Firehose (A) does natively by buffering data automatically. Also, Kinesis Data Streams don’t have built-in retries on connection drops like Firehose. So while D might give more flexibility, it doesn’t align as well with the “minimal management” and “unstable connection” parts of the requirements.

Good point on unstable connectivity—A’s use of IoT Core with Firehose means it can buffer and retry automatically, which none of the other options handle as smoothly. So yeah, A makes the most sense here.

Makes sense to skip B since Greengrass means more management on each device, which contradicts minimizing infrastructure. A looks simpler for scaling and handling disconnections well. Picking A.

A/B? Option A seems solid for unstable connectivity since IoT Core with Firehose can buffer and retry sending data to S3 automatically, which helps with remote devices. B could work too, but managing Greengrass on every device adds complexity and more infrastructure to handle, which the question wants to minimize. C and D involve more Lambda and Kinesis setup, which might be overkill if the goal is just reliable storage with minimal management. So between A and B, I’d go with A for simplicity and built-in resilience.

A. This setup uses AWS IoT Core with Firehose, which is built for reliable data ingestion and delivery to S3, plus it handles intermittent connections better than API Gateway or direct uploads.

A/D? While D is great for automation and minimal intervention, I’m not sure if Autopilot fully supports fine-tuning large language models specifically. A offers SageMaker Studio, which provides an interactive environment that’s still low-code and could simplify the process without needing full custom scripts. It might be a better balance between automation and control, especially if the team wants some hands-on tuning but less manual setup than B or C.

Makes sense to pick D for the no-code automation aspect that fits perfectly. D

Option D fits best since Autopilot automates training with minimal coding needed.

D Autopilot sounds best for low-code, minimal manual setup.