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Microsoft·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

Microsoft ML engineer interview with a focus on evaluation metrics for a retrieval API. The question was deceptively practical and the edge case follow-up is where things got interesting.

Questions Asked (2)

Q1

You have 10 image files with ground-truth labels for whether each contains a dog. Given an API that returns k file IDs predicted to be dogs, write pseudocode to compute precision and recall against the ground truth.

Product Analytics & MetricsAlgorithms & Data Structures
Author's notes

Felt pretty solid on this part.

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AI HintsAI Generated

Suggested Approach

Clarify the definitions of precision and recall, then walk through the pseudocode step by step: compute true positives by intersecting the API's predicted dog IDs with the ground-truth dog IDs, compute false positives as predicted IDs not in ground truth, and false negatives as ground-truth dog IDs not predicted. Finally, calculate precision as TP/(TP+FP) and recall as TP/(TP+FN), handling edge cases like zero denominators.

Pro tip: Mention that in real-world ML systems, you should also consider the confidence threshold and class imbalance; here, since the API returns a fixed set of k predictions, precision and recall are computed at that specific operating point.

1. Define inputs and ground truth

State that you have 10 image files, each with a ground-truth label (dog or not dog). The API returns a set of k file IDs predicted as dogs.

2. Compute true positives, false positives, false negatives

Iterate through the API predictions: if a predicted ID is in the ground-truth dog set, it's a true positive; otherwise, it's a false positive. Then, iterate through ground-truth dog IDs: if not in predictions, it's a false negative.

3. Calculate precision and recall

Precision = TP / (TP + FP). Recall = TP / (TP + FN). Handle division by zero by returning 0 or undefined as appropriate.

4. Return or output the metrics

Return the precision and recall values, possibly with a note on the number of predictions and ground-truth positives.

Key Points to Mention

  • Precision measures how many of the predicted dogs are actually dogs; recall measures how many of the actual dogs were predicted.
  • True positives are the intersection of predicted dog IDs and ground-truth dog IDs.
  • False positives are predicted dog IDs not in ground truth; false negatives are ground-truth dog IDs not predicted.
  • Edge cases: zero predicted positives (precision undefined) or zero ground-truth positives (recall undefined).
  • The API returns exactly k predictions, so precision and recall are evaluated at that fixed threshold.
  • Use sets for efficient intersection and difference operations.

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

Q2

How would you make the metric computation robust when the API behaves unexpectedly, such as returning None, throwing an exception, returning fewer or more than k items, returning duplicates, or returning unknown file IDs?

API & IntegrationsTechnical Trade-offsProduct Analytics & Metrics
Author's notes

This is where I started fumbling a bit.

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AI HintsAI Generated

Suggested Approach

Start by acknowledging that robust metric computation requires defensive programming and clear failure handling. Then outline a layered strategy: validate inputs, handle exceptions gracefully, enforce contract expectations (e.g., exactly k items, no duplicates, known IDs), and log anomalies for monitoring. Emphasize that metrics should be computed only on valid data, with fallbacks or alerts for invalid cases.

Pro tip: Treat the API as an untrusted source: assume it can fail in any way and design your metric pipeline to be idempotent and observable. Proactively suggest adding synthetic tests that simulate each failure mode to ensure robustness.

1. Define expected API contract and failure modes

Clearly specify what a correct API response looks like (e.g., exactly k items, unique IDs, known file IDs) and enumerate all possible deviations. This sets the foundation for validation.

2. Implement input validation and sanitization

Check for None, empty responses, wrong types, and unexpected lengths. Filter out duplicates and unknown IDs, and handle exceptions with try-except blocks. Log all anomalies for debugging.

3. Design fallback and error handling strategies

Decide how to proceed when validation fails: skip metric computation, use a default value, retry with backoff, or raise an alert. Ensure the system degrades gracefully without crashing.

4. Compute metrics on validated data only

Apply metric calculations exclusively to the cleaned, validated dataset. Document any assumptions and ensure the computation is deterministic and reproducible.

5. Monitor, log, and iterate

Emit detailed logs and metrics about API failures and data quality issues. Use this feedback to improve the API or the validation logic over time.

Key Points to Mention

  • Defensive programming: validate all inputs and handle exceptions gracefully.
  • Contract enforcement: ensure exactly k items, no duplicates, and known file IDs.
  • Fallback strategies: skip, default, retry, or alert on invalid data.
  • Observability: log anomalies and monitor failure rates for continuous improvement.
  • Testing: simulate each failure mode with unit and integration tests.
  • Idempotency: ensure metric computation can be safely retried without side effects.

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