Question 1

What is the difference between repartition and coalesce in Apache Spark?

Accepted Answer

**Repartition(n)**: Performs a full shuffle to redistribute data across exactly `n` partitions. Can increase or decrease partition count. Uses hash partitioning by default—all rows are exchanged across the network.

**Coalesce(n)**: Merges existing partitions into fewer partitions without a full shuffle. Only decreases partition count....

Question 2

What is the difference between SparkSession and SparkContext in Spark?

Accepted Answer

**SparkContext** (Spark 1.x): Low-level entry point for RDD operations. Manages cluster connections, configuration, and RDD creation. One active SparkContext per JVM. RDD-only.

**SparkSession** (Spark 2.0+): Unified entry point subsuming SparkContext, SQLContext, HiveContext, StreamingContext. Provides DataFrame, Dataset, SQL, and Structured Streaming APIs....

Question 3

What is the difference between narrow and wide transformations in Apache Spark? Explain with examples.

Accepted Answer

**Narrow transformations**: Each input partition maps to at most one output partition. No shuffle. Examples: map, filter, flatMap, mapPartitions.

**Wide transformations**: Require data from multiple input partitions to produce one output partition. Trigger shuffle. Examples: groupByKey, reduceByKey, join, distinct, repartition.

**Architectural Logic (Why This Matters)**: Spark pipelines narrow transformations and executes them in a single stage....

Question 4

What are Airflow Operators? Give examples.

Accepted Answer

Airflow Operators define a single unit of work in a DAG—each operator performs one atomic, idempotent task. **Why they matter**: They encapsulate work so DAGs remain declarative and schedulable; the scheduler doesn't need to understand task logic. **Examples**: BashOperator, PythonOperator, SqlOperator, HTTPOperator, DockerOperator, KubernetesPodOperator, Sensor. **Scalability**: Heavy logic should live in external scripts or services; operators should only orchestrate....

Question 5

Write a Python function to check if a string is a palindrome.

Accepted Answer

**Architectural logic**: A palindrome reads the same forwards and backwards. We need to normalize (case, non-alphanumeric) and compare. **Approach 1 (string ops)**: `cleaned = "".join(c.lower() for c in s if c.isalnum()); return cleaned == cleaned[::-1]`—O(n) time, O(n) space. **Approach 2 (two-pointer)**: Compare from both ends; O(n) time, O(1) space if not normalizing....

Question 6

What is the difference between a list and a tuple in Python?

Accepted Answer

List: Mutable, []; tuple: immutable, (). Why it matters: Mutability drives use—lists for collections that change; tuples for fixed data, dict keys (hashable), multiple return values. Performance: Tuples are slightly faster (less overhead, fixed size). Hashability: Tuples can be dict keys/set members; lists cannot. In data pipelines: Tuples for schema-like rows (column names); lists for buffers, accumulators....

Question 7

Explain the difference between shallow copy and deep copy in Python.

Accepted Answer

Shallow (copy.copy()): New top-level object; nested objects are references. Nested mutations affect original. Deep (copy.deepcopy()): Recursive copy; fully independent. Why it matters: Shallow is O(n) for top level only; deep is O(n) for entire structure—can be slow for large nested dicts. Use shallow when: No nested mutables or shared refs OK. Use deep when: Need full isolation (e.g., config that will be modified)....

Question 8

Write a Python function to find the first non-repeating character in a string.

Accepted Answer

Approach: Two-pass—count chars, then find first with count 1. Code: def first_non_repeating(s): counts = {}; [counts.update({c: counts.get(c, 0) + 1}) for c in s]; return next((c for c in s if counts[c] == 1), None). Or: from collections import Counter; counts = Counter(s); return next((c for c in s if counts[c] == 1), None). Complexity: O(n) time, O(k) space. Why: Single pass can't know if char is unique until full scan....

Question 9

What are decorators in Python, and how do they work?

Accepted Answer

Decorator: Higher-order function that wraps another function; @decorator syntax. Implementation: def timer(f): def wrapper(*args, **kwargs): start=time(); r=f(*args,**kwargs); print(time()-start); return r; return wrapper. Why: Cross-cutting concerns—logging, retries, auth, caching—without cluttering business logic. In data pipelines: @retry, @logged, @rate_limited on API fetchers. Scalability: Decorators add indirection; overuse can make stack traces deep....

Question 10

Explain the difference between args and kwargs in Python.

Accepted Answer

*args: Variable positional args, received as tuple. **kwargs: Variable keyword args, received as dict. Why: Flexible signatures—wrappers, decorators, config-driven functions. Example: def run_pipeline(*sources, **config): ... allows run_pipeline('s3://a','s3://b', parallelism=4). Order: (regular, *args, keyword-only, **kwargs). Pitfall: Passing through—func(*args, **kwargs)—preserves interface. In data pipelines: **kwargs for optional connector params (region, timeout, retries)....

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