Question 1

Describe a time when you had to deal with a difficult coworker.

Accepted Answer

Situation: A senior engineer was dismissive of my code reviews and suggestions for pipeline optimization, creating friction in the team. Task: Earn respect and align on technical direction without escalating conflict. Action: I focused on technical evidence rather than opinion—ran benchmarks (e.g., 30% faster shuffle with partition pruning), shared results in design docs, and asked for their feedback on the methodology....

Question 2

Data Lakehouse architecture in Azure?

Accepted Answer

**Section 1 — The Context (The 'Why')**
The primary challenge for this design in Cloud/Tools is balancing scale, cost, and reliability. At scale, naive approaches fail: single points of failure cause cascades, schema evolution breaks consumers, and over-provisioning explodes cost. Failure modes include silent data loss from non-idempotent writes, cascading job failures from tight coupling, and operational burden from manual intervention....

Question 3

Describe Amazon Athena and how it interacts with S3.

Accepted Answer

Architectural logic: Athena is serverless Presto/Trino—no cluster to manage; you pay per TB scanned. Why this model: Perfect for ad-hoc analytics on data lakes; decouples storage (S3) from compute. S3 interaction: Athena reads directly from S3; no ETL load step. Glue Catalog provides schema; queries scan only relevant partitions and columnar bytes. Scalability: Unlimited concurrency; each query spins up its own cluster....

Question 4

Describe step scaling policies vs. target tracking policies in AWS Auto Scaling.

Accepted Answer

Architectural logic: Step scaling = magnitude-based (add 2 if CPU>70%, add 4 if >85%); Target tracking = maintain metric at target (e.g., 70% CPU). Why choose: Target = simpler, steady-state; Step = tiered response to severity. Scalability: Both scale out; Step can over-provision during spikes. Cost: Target tracking tends to right-size; Step can over-scale....

Question 5

Explain AWS Lake Formation and its benefits.

Accepted Answer

Architectural logic: Lake Formation = governance layer over data lake. Benefits: Centralized table/column permissions; Glue Catalog integration; row/column-level security; audit. Why: IAM alone is coarse; Lake Formation enables fine-grained access (e.g., analyst sees only non-PII columns). Cost: No extra for Lake Formation; Glue/catalog costs apply....

Question 6

Explain the difference between S3 One Zone-IA and S3 Standard-IA.

Accepted Answer

Architectural difference: Standard-IA = 3+ AZs, 99.9% availability; One Zone-IA = 1 AZ, 99.5% availability, lower cost. Use Standard-IA when: high durability required. Use One Zone-IA when: data reproducible, cost critical. Trade-off: Durability vs cost....

Question 7

How do you handle cost optimization in AWS EMR clusters?

Accepted Answer

Strategies: (1) Spot for task nodes; On-Demand for master/core if needed. (2) Right-size instance types. (3) Auto-scaling. (4) Transient clusters—run job, terminate. (5) Spot blocks for 1–6h jobs. (6) Savings Plans. Trade-off: Spot interruptions; design for fault tolerance....

Question 8

How do you secure data at rest and in transit for AWS RDS?

Accepted Answer

At rest: Encryption (AWS-managed or KMS). In transit: SSL/TLS; rds.force_ssl=1; ACM. Best practice: Encrypt new instances; enforce SSL in parameter group; VPC + security groups; IAM auth optional; rotate credentials; CloudTrail and Enhanced Monitoring.

Persistent Systems Data Engineer Interview Questions

+4 More Questions with Expert Answers

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Persistent Systems Data Engineer Interview Questions

+4 More Questions with Expert Answers

Other Companies