ACP ACP Data Engineering & Workflow Automation

Question 1

Which pandas method reads a CSV file into a DataFrame and can handle large files by specifying a `chunksize` parameter?

Accepted Answer

pd.read_csv()

Answer

`pd.read_csv()` is the standard function for loading CSV data into a DataFrame, and its `chunksize` parameter returns an iterator of DataFrame chunks for memory-efficient processing.

Question 2

In an ETL pipeline using pandas, which operation is used to combine two DataFrames based on a shared key column (similar to a SQL JOIN)?

Accepted Answer

pd.merge()

Answer

`pd.merge()` performs database-style joins between DataFrames on one or more key columns, supporting inner, outer, left, and right join types.

Question 3

Which Python library provides the `Pipeline` class to chain preprocessing steps and a final estimator into a single reusable workflow object?

Accepted Answer

scikit-learn

Answer

scikit-learn's `Pipeline` chains transformers and a final estimator so that `fit` and `predict` calls automatically apply all steps in sequence.

Question 4

When processing a large dataset in chunks to avoid memory overflow, which pandas parameter in `read_csv()` controls how many rows are loaded per iteration?

Accepted Answer

chunksize

Answer

Setting `chunksize=N` in `pd.read_csv()` returns a `TextFileReader` iterator where each iteration yields a DataFrame of N rows.

Question 5

Which SQLAlchemy function is used alongside pandas `read_sql()` to connect to a database and execute SQL queries into a DataFrame?

Accepted Answer

create_engine()

Answer

`create_engine('dialect+driver://user:pass@host/db')` from SQLAlchemy creates a connection engine that pandas `read_sql()` uses to execute queries and return results as a DataFrame.

(ACP) Anaconda Certified Professional Practice Test

(ACP) Anaconda Certified Professional Practice Test

ACP ACP Data Engineering & Workflow Automation