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Thiago Robert

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Thanks for the informative blog post! It really clarified my doubts about data sources for the AWS ML exam.

On Identify data sources (for example, content and location, primary sources such as user data).

AWS has built-in mechanisms to handle latency in Kinesis, greatly depending on how you configure your shards and buffering.

On Streaming data ingestion

What kind of disaster recovery strategies are there with BGP over Direct Connect?

On Industry-standard routing protocols that are used in AWS hybrid networks (for example, BGP over Direct Connect)

For beginners, understanding the concept of a loss function is crucial. It's basically how your model knows it's doing well…

On Understand optimization techniques for ML training (for example, gradient decent, loss functions, convergence).

I didn't find this tutorial very helpful. More examples would have been better.

On Querying logs in Amazon S3 for contextual information related to security events (for example, by using Athena)

Could you give me examples of RCA tools other than the AWS native ones?

On Root cause analysis

The blog post was good, but it could use more real-world examples.

On Selecting appropriate protections based on anticipated vulnerabilities and risks (for example, vulnerable software, applications, libraries)

Don't forget about auditing and monitoring. AWS CloudTrail and CloudWatch are quite effective.

On Data ingestion patterns (for example, frequency and data history)

This guide was very helpful. Thanks!

On Differences between AWS managed and customer managed AWS Key Management Service (AWS KMS) keys

I appreciate the detailed explanation. However, I think more examples could have been included.

On Fault-tolerant design patterns (for example, retries with exponential backoff and jitter, dead-letter queues)

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