Q1. Difference between HashMap and ConcurrentHashMap?
👉 HashMap is not thread-safe. Concurrent modifications lead to ConcurrentModificationException.
👉 ConcurrentHashMap uses segment/stripe locking (Java 8 → CAS + synchronized blocks) allowing concurrent reads and controlled writes.

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Q2. How do you avoid deadlocks in Java?
👉 Strategies:

· Lock ordering (always acquire locks in a fixed sequence).

· Use tryLock with timeout.

· Minimize lock scope.

· Prefer concurrent data structures over manual synchronization.

Use consistent lock ordering, use tryLock with timeout, avoid nested locks, break locks into smaller scopes, prefer lock-free or higher-level concurrency utilities.

boolean got = lock1.tryLock(500, TimeUnit.MILLISECONDS);

if (got) {

    try {

        if (lock2.tryLock(500, TimeUnit.MILLISECONDS)) {

            try { /* do work */ } finally { lock2.unlock(); }

        } else {

           // fallback

        }

    } finally { lock1.unlock(); }

}

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Q3. Difference between parallelStream() and stream()?
👉 stream() is sequential, runs on single thread.
👉 parallelStream() uses ForkJoinPool, splits workload across multiple threads.
⚠️ Best for CPU-bound tasks, not for I/O-heavy workloads.

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Q4. How do you implement retry with exponential backoff in Java?
👉 Use ScheduledExecutorService or libraries like Resilience4j. Example: retry after 1s, 2s, 4s, … until max retries.

---

Q5. REST API idempotency – how to design PUT vs POST?
👉 POST: create (non-idempotent).
👉 PUT: create/update (idempotent – same request multiple times = same result).
👉 Use Idempotency-Key header for banking/payment APIs.

---

Section 2 – Spark

Q6. RDD vs DataFrame vs Dataset?
👉 RDD: low-level, type-unsafe.
👉 DataFrame: high-level API, optimized by Catalyst, untyped.
👉 Dataset: combines RDD (type safety) + DataFrame (optimizations).

---

Q7. How do you handle skewed data in Spark?
👉 Techniques:

· Salting keys.

· Repartitioning/shuffle partition tuning.

· Use broadcast joins for small tables.

· Skew join optimization in Spark SQL.

---

Q8. What is checkpointing in Spark Streaming?
👉 Saves state + metadata to HDFS (or other reliable storage).
👉 Used for recovery after failures.
👉 Two types: Metadata checkpointing, Data checkpointing.

---

Q9. How do you achieve fault tolerance in Spark Streaming?
👉 Input data must be replayable (Kafka/Flume).
👉 Spark uses lineage + checkpointing to recompute lost partitions.

---

Q10. Spark job optimization techniques?

· Cache/persist intermediate results.

· Minimize shuffles (use mapPartitions, reduceByKey).

· Use partition pruning, predicate pushdown.

· Avoid UDFs when SQL functions exist.

---

Section 3 – Hadoop Ecosystem

Q11. HDFS block size and why large?
👉 Default: 128MB/256MB. Large blocks → fewer seeks, better throughput.

---

Q12. NameNode vs DataNode?
👉 NameNode: stores metadata.
👉 DataNode: stores actual blocks.

---

Q13. Hive – external vs managed table?
👉 Managed: Hive controls lifecycle (drop = delete data).
👉 External: Hive stores only metadata, data remains even if dropped.

---

Q14. ORC vs Parquet?
👉 ORC: optimized for Hive, good compression, lightweight metadata.
👉 Parquet: language-agnostic, nested data support, better for Spark.

---

Q15. HBase row-key design – best practices?
👉 Short, unique, evenly distributed (avoid hotspotting).
👉 Reverse timestamps for time-series data.

---

Section 4 – Messaging Systems

Q16. Kafka vs RabbitMQ difference?
👉 Kafka: distributed log, high throughput, partitioned, replay capability.
👉 RabbitMQ: traditional broker, push-based, supports complex routing.

---

Q17. How does Kafka ensure ordering?
👉 Messages are ordered within a partition.
👉 To guarantee ordering → all related keys must go to the same partition.

---

Q18. How do you handle offset management in Kafka?
👉 Auto commit vs manual commit.
👉 Best practice: commit offset after processing the message.

---

Q19. Kafka “exactly once semantics” – how?
👉 Idempotent producer (enable.idempotence=true).
👉 Transactions API for producer-consumer pipeline.

---

Q20. What if Kafka broker goes down?
👉 ISR (in-sync replicas) ensures another replica is promoted as leader.
👉 Producer/consumer retries with leader election.

---

Section 5 – DevOps / CI-CD / TDD

Q21. Maven vs SBT vs Ant?
👉 Maven: convention over configuration, XML based.
👉 SBT: Scala-based, better for Spark/Scala projects.
👉 Ant: older, no dependency management.

---

Q22. Jenkins pipeline stages for Spark job?

1. Checkout code (Git).

2. Compile & run unit tests.

3. Build JAR with Maven.

4. Run integration tests on staging cluster.

5. Deploy to production (submit via spark-submit).

---

Q23. Git – difference between merge and rebase?
👉 Merge: keeps branch history.
👉 Rebase: linearizes history (cleaner, but dangerous if misused).

---

Q24. JUnit 5 improvements over JUnit 4?
👉 @ParameterizedTest, @Nested, @DisplayName, better assertions.

---

Q25. How do you ensure test coverage for streaming apps?
👉 Use embedded Kafka for testing.
👉 Use MemoryStream in Spark Structured Streaming.
👉 Validate outputs against expected results.

---

Section 6 – Scenario/Case Studies

Q26. A Spark Streaming job is lagging behind real-time. What do you check?

· Check Kafka consumer lag (kafka-consumer-groups.sh).

· Check batch interval & processing time.

· Increase parallelism (spark.streaming.kafka.maxRatePerPartition).

· Optimize transformations (reduce shuffles).

---

Q27. You get OutOfMemoryError in Spark. How to debug?

· Increase executor memory.

· Enable Kryo serialization.

· Use persist(StorageLevel.DISK_ONLY) for large datasets.

· Avoid wide transformations.

---

Q28. You have 1TB JSON logs in HDFS, query is slow. How to improve?
👉 Convert JSON → ORC/Parquet (columnar, compressed).
👉 Partition by date/time.
👉 Use vectorized queries in Hive/Spark.

---

Q29. Kafka consumer is reprocessing same messages after restart. Why?
👉 Offsets not committed properly.
👉 Solution: commit offsets after message is processed.

---

Q30. Spark job with shuffle stage takes too long. How to optimize?
👉 Increase shuffle partitions.
👉 Use map-side combine.
👉 Avoid skew by salting.

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Section 7 – Behavioral / Banking Domain

Q31. Tell me about a time you optimized a big data pipeline.
👉 Example: Reduced Spark shuffle time by tuning partitions + using broadcast joins → improved SLA by 40%.

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Q32. How do you ensure data security in Big Data pipelines?
👉 Encrypt data at rest (HDFS TDE) & in transit (TLS).
👉 Mask PII fields before ingestion.
👉 Use Ranger/Atlas for access control & lineage.

---

Q33. How do you ensure regulatory compliance (GDPR/CCPA)?
👉 Right-to-erasure implementation (delete user data from HDFS/Hive).
👉 Audit logs for all access.

---

✅ Quick Tips Before Monjin Video Interview

· Monjin = scenario-based + coding → expect to share screen & code live (Java + Spark + Kafka basics).

· Keep 2–3 real project examples (data pipeline, Kafka ingestion, Spark optimization).

· Stress on performance tuning, scalability, resilience – Synechron’s banking clients need this.

· Keep buzzwords handy: "idempotency", "backpressure handling", "predicate pushdown", "exactly-once semantics".

Q4. How do you optimize Spring Boot APIs?

Key points:

· pagination

· caching

· async processing

· DB indexing

· connection pooling

· avoid N+1 queries

· DTO projections

· lazy loading

· Kafka for async workloads

Mention:

· HikariCP

· Redis

· query tuning

· actuator metrics

Q5. What is the difference between synchronized and ReentrantLock?

You already prefer synchronized blocks.

Answer:

synchronized	ReentrantLock
JVM managed	API based
simpler	advanced features
automatic release	manual unlock
less flexible	tryLock(), fairness

Then say:

"In most enterprise applications I prefer synchronized blocks when simple thread safety is sufficient because the locking scope remains smaller and code stays maintainable."

That sounds architect-level.

Procedure	Function
performs action	returns value
can have OUT params	must return value
called independently	callable in SQL

Q7. How do you optimize Oracle queries?

Mention:

· indexes

· explain plan

· avoid full table scan

· bind variables

· partitioning

· bulk collect

· forall

· normalized schema

· proper joins

Very important:
Mention composite indexes and execution plan analysis.

Angular Questions

Q10. Difference between Observable and Promise

Observable	Promise
multiple values	single value
cancellable	not cancellable
lazy	eager
RxJS operators	limited

Angular HttpClient uses Observables.