ACP ACP Data Engineering & Workflow Automation 2

Question 1

Which Python library is specifically designed for defining, scheduling, and monitoring data pipeline workflows as Directed Acyclic Graphs (DAGs)?

Accepted Answer

All of the above

Answer

Luigi, Apache Airflow, and Prefect are all Python-native workflow orchestration frameworks that model pipelines as DAGs with scheduling and monitoring capabilities.

Question 2

In pandas, which method is used to apply a custom function to every row or column of a DataFrame?

Accepted Answer

df.apply()

Answer

`df.apply()` applies a function along an axis (rows with `axis=1`, columns with `axis=0`), enabling custom transformations across the entire DataFrame.

Question 3

Which pandas method efficiently removes duplicate rows from a DataFrame, keeping only the first occurrence by default?

Accepted Answer

df.drop_duplicates()

Answer

`df.drop_duplicates()` returns a DataFrame with duplicate rows removed, with `keep='first'` as default and options for `keep='last'` or `keep=False` to drop all duplicates.

Question 4

When building a data pipeline, what does the term 'idempotency' mean in the context of pipeline task execution?

Accepted Answer

Running a task multiple times produces the same result as running it once

Answer

An idempotent pipeline task can be safely re-executed without side effects — running it once or ten times yields the same final state, which is critical for reliable data engineering.

Question 5

Which pandas method stacks a DataFrame from wide format (one column per variable) to long format (one row per observation)?

Accepted Answer

df.melt()

Answer

`df.melt()` unpivots a DataFrame from wide to long format by converting specified columns into rows, creating `variable` and `value` columns.

(ACP) Anaconda Certified Professional Practice Test

(ACP) Anaconda Certified Professional Practice Test

ACP ACP Data Engineering & Workflow Automation 2