Verify Integrity of Customer Configuration Files

Your question is Verify Integrity of Customer Configuration Files. Take a moment with it on the right.

Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).

Design a system to verify the integrity of customer configuration files (asymmetric-cryptography based)

Asked in the system design stage. Security engineer interview loop at Datadog.

Address:

Define the signing and verification protocol, including canonicalization, key distribution, rotation, revocation, and replay protection.
Design online and batch verification paths and their interfaces.
Explain how verification results, failures, and audit records are stored.
Discuss availability, latency, compromise recovery, and safe failure behavior.
Explain whether ML belongs in the trust decision, and if used for secondary anomaly detection, define its evaluation and monitoring.

Do not treat an ML prediction as proof of integrity. State assumptions where scale, traffic, or retention requirements are unspecified.

Your question is Verify Integrity of Customer Configuration Files. Take a moment with it on the right.

Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).

Design a system to verify the integrity of customer configuration files (asymmetric-cryptography based)

Asked in the system design stage. Security engineer interview loop at Datadog.

Address:

Define the signing and verification protocol, including canonicalization, key distribution, rotation, revocation, and replay protection.
Design online and batch verification paths and their interfaces.
Explain how verification results, failures, and audit records are stored.
Discuss availability, latency, compromise recovery, and safe failure behavior.
Explain whether ML belongs in the trust decision, and if used for secondary anomaly detection, define its evaluation and monitoring.

Do not treat an ML prediction as proof of integrity. State assumptions where scale, traffic, or retention requirements are unspecified.