Question 1

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 2

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)....

Question 3

How do you clean missing values in a pandas DataFrame?

Accepted Answer

**Methods:** dropna(); fillna(0)/ffill/bfill. Per column: fillna(median). Categorical: mode. **Why:** Document strategy. **Production:** pd.isna(); validate imputation doesn't bias.

Question 4

Write a script to automate daily ingestion of data from an API into a data lake.

Accepted Answer

**Flow:** GET API (paginate if needed) → write to S3/GCS (path: prefix/YYYY/MM/DD/file.json). Schedule via cron or Airflow. Use idempotent path (date-based) for reruns.

**Production:** Retries with backoff. Pagination for large APIs. Incremental: pass since/date param. Secrets from env or secrets manager. Log response size, row count....

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