NASA·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

NASA security analyst interview, one technical question that combined SQL/query writing with threat analysis judgment. Pretty focused session, felt more like a practical skills check than a traditional interview.

Questions Asked (1)

Write a query to detect repeated authentication failures from the same IP address within a 10-minute window, then walk through how you'd distinguish malicious activity from a user just forgetting their password.

Root Cause AnalysisSystem DesignTechnical Trade-offs

Author's notes

The query part I was fine with, window functions over a timestamp column grouped by IP, flag anything above a threshold.

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AI HintsAI Generated

Suggested Approach

Start by writing a clear SQL query that groups authentication failures by IP address and counts events within a 10-minute sliding window, using window functions or self-joins. Then, discuss how to distinguish malicious activity from benign user errors by analyzing patterns such as failure frequency, timing, username diversity, and success rates after failures. Finally, tie your answer to NASA's context by emphasizing reliability, security, and the importance of minimizing false positives in mission-critical systems.

Pro tip: Mention that you would validate the query against historical data and tune thresholds to balance security and user experience, showing you understand operational trade-offs. Also, highlight the importance of logging and monitoring to continuously improve detection.

1. Clarify requirements and assumptions

Confirm the schema (e.g., table with timestamp, IP, username, success flag) and define what constitutes a 'repeated failure' (e.g., >5 failures in 10 minutes). State any assumptions about data availability and time granularity.

2. Write the SQL query

Use a window function like COUNT(*) OVER (PARTITION BY ip ORDER BY timestamp RANGE BETWEEN INTERVAL '10 minutes' PRECEDING AND CURRENT ROW) to count failures per IP in a sliding window, then filter for counts exceeding a threshold. Alternatively, use a self-join or GROUP BY with time buckets.

3. Explain the query logic

Walk through how the query identifies IPs with repeated failures within any 10-minute window, ensuring it captures sliding windows rather than fixed buckets. Mention performance considerations like indexing on timestamp and IP.

4. Distinguish malicious vs. benign

Discuss heuristics: malicious activity often shows high frequency, many distinct usernames, consistent timing (e.g., every few seconds), and failures across multiple accounts; benign errors typically involve a single username, irregular intervals, and eventual success. Also consider IP reputation and geolocation.

5. Propose mitigation and monitoring

Suggest actions like rate limiting, CAPTCHA, or temporary IP blocking for suspicious patterns, while allowing legitimate users to retry. Emphasize logging, alerting, and continuous tuning to reduce false positives.

Key Points to Mention

Use of window functions (e.g., COUNT OVER with RANGE) for sliding window analysis
Threshold selection and tuning based on historical data and risk tolerance
Differentiating factors: number of distinct usernames, timing regularity, success rate after failures
Consideration of IP reputation, geolocation, and user agent anomalies
Performance optimization: indexing on timestamp and IP, avoiding full table scans
Operational trade-offs: security vs. user experience, false positives vs. false negatives

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.