Question 1

Design an end-to-end data pipeline using Glue, Lambda, EC2, S3, Redshift, and Athena.

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 2

Discuss how versioning works in S3 and its use cases, such as data recovery and auditing.

Accepted Answer

Architectural logic: Versioning stores multiple versions per key; overwrite creates new version; delete adds delete marker. Use cases: Recovery (copy prior version over current); audit; accidental overwrite protection. Trade-off: Storage cost increases; lifecycle can transition old versions....

Question 3

What are the methods to copy files to S3 without using the bucket upload feature?

Accepted Answer

**CLI**: aws s3 cp, aws s3 sync. Sync is incremental. **boto3**: upload_file (handles multipart automatically for >8 MB), put_object for small. **S3 Transfer Acceleration**: Uses CloudFront edge for faster uploads from distant locations. **DataSync**: Agent-based for on-prem to S3; handles large datasets, incremental. **Snowball**: Offline for very large (100+ TB); no network....

Question 4

Test SQL skills using advanced window functions such as LAG, LEAD, and DENSE_RANK.

Accepted Answer

LAG, LEAD, and DENSE_RANK are powerful window functions. LAG(column, n) returns the value n rows before the current row; LEAD does the opposite. DENSE_RANK assigns ranks with no gaps for ties. Example—period-over-period comparison: SELECT date, revenue, LAG(revenue, 1) OVER (ORDER BY date) AS prev_revenue, revenue - LAG(revenue, 1) OVER (ORDER BY date) AS growth FROM sales; For ranking within partitions: SELECT employee_id, department_id, salary, DENSE_RANK() OVER (PARTITION BY department_id ORD...

Question 5

Time and cost comparisons for executing the same query in Snowflake and Spark.

Accepted Answer

Snowflake vs Spark for the same query: Time—Snowflake typically executes faster for ad-hoc analytics due to automatic optimization, columnar storage, and managed compute. Spark excels for large batch jobs and complex transformations. Cost—Snowflake charges per credit (compute + storage); costs scale with warehouse size and runtime. Spark on-prem/Databricks has fixed infra cost; pay-per-query in cloud....

Question 6

Write a query to generate the specified output using advanced SQL skills with joins, aggregations, and window functions.

Accepted Answer

Example combining joins, aggregations, windows: WITH base AS (SELECT r.region, p.product, SUM(s.amount) amt FROM sales s JOIN regions r ON s.region_id = r.id JOIN products p ON s.product_id = p.id GROUP BY r.region, p.product), ranked AS (SELECT *, ROW_NUMBER() OVER (PARTITION BY region ORDER BY amt DESC) rn FROM base) SELECT * FROM ranked WHERE rn <= 5. **Why**: JOINs for dimensions; GROUP BY for aggregates; window for ranking. Adjust to required output....

Question 7

Discuss techniques such as partitioning, broadcast joins, and caching to enhance Spark job performance.

Accepted Answer

**Why techniques matter**: Combined effect = 5–50x improvement. **Partitioning**: Co-locate data; partition pruning reduces scan. Align partition key with filter. **Broadcast**: Small table to all executors; no shuffle. **Caching**: Reuse intermediates. **Scalability trade-offs**: Partition by high-cardinality = explosion; broadcast has size limit; cache competes with memory. **Cost implications**: Partition pruning = less scan; broadcast = less shuffle; cache = faster when reused....

Question 8

Explain how Spark processes a 500GB file, covering memory allocation, shuffles, and spillovers to disk.

Accepted Answer

**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.

Processing 500GB in Spark: (1) Read—Spark splits into partitions (e.g., by HDFS block or file); default approximately 128MB/partition yielding around 4000 partitions; (2) Memory—executor memory stores partitions; spill to disk when full (spark.memory.fraction); (3) Shuffles—wide ops trigger shuffle; intermediate data may spill; (4) Tuning—increase partitio...

Carelon Data Engineer Interview Questions

+12 More Questions with Expert Answers

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Carelon Data Engineer Interview Questions

+12 More Questions with Expert Answers

Other Companies