Module 7 Quiz – Analytics Engineering

1. A Jupyter Notebook is best described as an environment that lets you combine: *

A) Only SQL queries

B) Code, results, and Markdown in one place

C) Web dashboards only

D) Only terminal commands

2. A key reason analytics engineers use notebooks is: *

A) Production orchestration only

B) Rapid prototyping and exploratory data analysis

C) GPU model training only

D) Schema migration only

3. Why is Python popular for analytics engineering? *

A) Small community, low support

B) Limited libraries for data

C) Large ecosystem (pandas, NumPy, scikit-learn) and strong community

D) Only works on Windows

4. A recommended way to install Python & Jupyter is: *

A) Copying binaries by hand

B) Using Conda/Anaconda or pip with virtual environments

C) Editing the system registry

D) Browser plug-ins

5. Python blocks are defined primarily by: *

A) Curly braces

B) Semicolons

C) Indentation (typically 4 spaces)

D) Tabs only

6. The Python feature for embedding variables into strings is: *

A) strcat

B) f-strings

C) format_map only

D) Byte strings

7. Which data structure is ordered and mutable? *

A) List

B) Tuple

C) Set

D) Dictionary keys

8. A tuple is ideal when you need: *

A) A mutable sequence

B) An immutable, fixed collection (e.g., coordinates)

C) Automatic uniqueness

D) Key-value storage

9. A dictionary is best described as: *

A) Ordered and immutable

B) Unordered with unique-only values

C) Key-value pairs with common methods like items(), keys(), values()

D) A 2D table for analytics

10. A set is useful when you need: *

A) Duplicate-friendly ordered lists

B) Unordered collection of unique elements

C) Immutable key-value pairs

D) Strict indexing by position

11. In pandas, the primary data structures are: *

A) RDD and DataSet

B) Series (1D) and DataFrame (2D)

C) Table and View

D) Graph and Node

12. Pandas can read/write which formats out of the box? *

A) Only CSV

B) CSV, Excel, SQL, JSON (among others)

C) Images only

D) Audio files only

13. To quickly inspect a DataFrame you would use: *

A) head(), tail(), info(), describe()

B) compile(), explain(), optimize()

C) render(), plot3D()

D) unique(), ndim() only

14. Filtering rows in pandas commonly uses: *

A) Boolean indexing (e.g., df[df["col"] > 0])

B) SQL cursors

C) XML selectors

D) Shell pipelines

15. Typical data-cleaning methods in pandas include: *

A) dropna(), fillna(), rename(), astype(), and string .str methods

B) clear(), reset() only

C) compress(), encrypt()

D) mkdir(), chmod()

16. To summarize data by groups you would use: *

A) groupby() with agg() (e.g., sum, mean, count)

B) over() window only

C) collect_set()

D) explode() exclusively

17. To create pivoted summaries in pandas you would use: *

A) pivot_table()

B) reshape()

C) melt_only()

D) transpose_only()

18. For orchestration and reliability in real workflows, the module suggests: *

A) Manual daily runs only

B) Scheduling with tools like Airflow, plus automated quality checks and alerts

C) Emailing CSVs by hand

D) Running notebooks once per year