Question 1

When would you choose a Snowflake schema over a Star schema?

Accepted Answer

Star: One fact, denormalized dimensions—simple, fewer joins, fast. Snowflake: Normalized dimensions (e.g., dim_product → dim_category → dim_category_group)—more joins, less redundancy. Why Snowflake: When dimension tables are large and shared—a single dim_category serves many products; denormalizing would duplicate millions of rows. Avoids inconsistent attributes across copies (e.g., category name updated in one place). Storage: Snowflake saves space when hierarchy is deep and wide....

Question 2

How do you ensure data quality and validation in a fast-moving team?

Accepted Answer

Situation: At [Company], our data team shipped 15+ pipelines weekly; quality incidents were causing downstream analytics and ML models to fail silently. Task: I was tasked with implementing a scalable quality framework without slowing velocity. Action: I designed a tiered validation strategy: (1) Schema validation at ingestion—Great Expectations run as pre-commit hooks and in CI. (2) Critical-field blocking—null or out-of-range on key columns fails the pipeline....

Question 3

Tell me about a time when a Spark job failed in production. How did you fix it?

Accepted Answer

Situation: Spark job OOM due to key skew—one partition had 80% of data. Task: Diagnose and fix. Action: Used Spark UI; identified skewed stage. Fixed with salting, increased executor memory, broadcast joins for dimensions. Deployed; added skew monitoring and data quality check....

Question 4

What storage format would you choose for analytics-heavy workloads and why?

Accepted Answer

Situation: Analytics workload. Task: Format selection. Action: Why: columnar (compression, predicate pushdown). Parquet/ORC. Lakehouse: Delta/Iceberg for ACID, time travel. Partition by date; Z-order for filters. Avoid CSV/JSON for large scans....

Question 5

What happens if the NameNode goes down?

Accepted Answer

NameNode: HDFS metadata (directory, block locations). Single point. If down: HDFS unavailable. Mitigation: HA with standby + ZooKeeper failover; shared edits. Backup: namespace to NFS; Secondary NameNode checkpoints. WHY: Always HA in prod; test failover; runbooks....

Question 6

What's the time and space complexity of both solutions?

Accepted Answer

**Example—two sum:** Brute O(n²) time, O(1) space. Optimized O(n) time, O(n) space (hash map). **Merge intervals:** Sort O(n log n) time, O(1) or O(n) space. **Always state both.** **Production:** Choose from input size; optimize when profiling shows bottleneck. Document in comments....

Question 7

Given a list of intervals, merge the overlaps. How do you optimize it?

Accepted Answer

**Algorithm:** Sort by start. If overlaps last, extend; else append. O(n log n). **Code:** sort; res=[intervals[0]]; for s,e: if s<=res[-1][1]: res[-1][1]=max(...); else: res.append. **Why:** Sort enables linear merge....

Question 8

How would you test these functions with edge cases?

Accepted Answer

**Edge cases:** Empty; single element; boundaries; duplicates; negative; null/NaN; type errors; large input. **Tools:** pytest parametrize; Hypothesis. **Example:** @pytest.mark.parametrize('input,expected', [([],0), ([1],1)]). **Why:** Prevent regressions....

Microsoft Data Engineer Interview Questions

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Microsoft Data Engineer Interview Questions

+1 More Questions with Expert Answers

Other Companies