Q: What is AIOps?
A: AIOps (Artificial Intelligence for IT Operations) uses ML to:
- Automate IT operations
- Detect anomalies before users notice
- Correlate events across systems
- Suggest or execute remediation
Q: What are the key components of an AIOps platform?
A:
- Data Ingestion: Metrics, logs, traces
- Anomaly Detection: ML models for unusual behavior
- Event Correlation: Connect related events
- Root Cause Analysis: Identify problem source
- Automated Remediation: Self-healing actions
Q: How do you detect anomalies in time-series data?
A:
- Statistical: Z-score, IQR, ARIMA
- ML-based: Isolation Forest, One-class SVM
- Deep Learning: LSTM autoencoders, Transformers
- Seasonal: STL decomposition + threshold
Q: What's the difference between supervised and unsupervised anomaly detection?
| Supervised | Unsupervised |
|---|---|
| Needs labeled data | No labels needed |
| Detects known patterns | Detects unknown patterns |
| Classification problem | Clustering/density based |
Q: How do you safely implement auto-remediation?
A:
- Start with low-risk actions (restart, scale)
- Implement safeguards (cooldowns, limits)
- Require human approval for risky actions
- Comprehensive logging
- Easy rollback mechanism
Q: Design an auto-scaling system using ML.
A:
- Collect historical metrics (CPU, requests, latency)
- Train model to predict future load
- Proactively scale before demand spike
- Continuously retrain on new data
- Fall back to reactive scaling if prediction fails
Q: How would you reduce alert fatigue?
A:
- ML-based alert correlation
- Automatic severity adjustment
- Deduplication and grouping
- Context-aware routing
- Feedback loops (was alert actionable?)
Next: Return to Interview Overview.