Data Science with Python Part 1 2025 — 30 Q&A Data
Manipulation NumPy Pandas Verified
Series:
CrashCourses Professional Study Series
Author:
Dr Z. Moomba, MBChB, MRCPsych | BethelWellness Ltd
Exam Target:
Data Science Python Part 1
Year:
2025/2026
Format:
30 Questions with Verified Answers and Rationales
>
Author's Note:
This document is an original work produced for the CrashCourses Professional Study Series.
Clinical questions and professional scenarios were composed by Dr Z. Moomba based on current
exam objectives, published guidelines, and evidence-based sources (2024–2025). All patient
names, ages, and case details are fictional. Any resemblance to existing published Q&A banks is
coincidental. For personal study use only — not for reproduction or redistribution.
SECTION A — FOUNDATIONS
Question 1
A data scientist at a regional health trust is tasked with calculating the heart rate variability for
100,000 continuous telemetry readings. They must choose between a standard Python list and a
NumPy dimensional array to store the data in memory. Which fundamental characteristic makes
the NumPy structure the superior choice for this mathematical calculation?
A) It allows storing mixed data types such as clinical strings and integers within the exact same
memory block.
B) It stores elements in contiguous memory locations, allowing for highly efficient vectorized
operations.
,C) It automatically handles missing clinical data points by default without explicit user instruction.
D) It relies on dynamic typing, making it significantly faster to append new real-time readings than
a standard list.
Answer: B
Rationale:
NumPy arrays are stored in continuous blocks of memory, which allows the processor to perform
calculations on entire arrays simultaneously through vectorization. The key discriminating feature
is this contiguous memory allocation, which eliminates the heavy overhead of type-checking each
individual element found in dynamically typed standard lists. The tempting option regarding
dynamic appending fails because NumPy arrays actually have fixed sizes upon creation, making
standard lists faster for dynamic appending but far slower for calculations; utilizing vectorization
can speed up clinical data processing by orders of magnitude [NumPy Performance Guidelines
2025].
Question 2
A clinical auditor is reviewing a one-dimensional NumPy array containing the baseline systolic
blood pressures of fifty patients on a cardiac ward. The auditor needs to subtract a calibration
error of five millimeters of mercury from every single reading simultaneously. Which NumPy
principle allows a single scalar value to be subtracted from an entire array without writing a loop?
A) Array casting
B) Memory mapping
C) Broadcasting
D) Concatenation
Answer: C
Rationale:
Broadcasting is the mechanism that describes how NumPy treats arrays with different shapes
during arithmetic operations, essentially stretching the smaller scalar value across the larger array
to perform element-wise calculations. This provides a highly readable and memory-efficient way
to adjust clinical datasets globally without manually creating a matching array of error values.
Memory mapping is a tempting wrong answer as it deals with memory efficiency, but it relates to
reading large files from a disk rather than arithmetic alignment; mastering broadcasting rules is
essential for writing clean, performant healthcare algorithms [Python Data Science Handbook
2024].
, Question 3
An epidemiologist has two separate one-dimensional NumPy arrays of equal length: one
containing patient heights in meters and the other containing patient weights in kilograms. They
need to calculate the Body Mass Index for the entire cohort. What is the most computationally
efficient method to achieve this in NumPy?
A) Iterate through both arrays using a standard for-loop and append the results to a new list.
B) Use the map function combined with a lambda expression across both arrays.
C) Perform a direct mathematical operation dividing the weight array by the square of the height
array.
D) Convert both arrays into a dictionary and use dictionary comprehension to pair the values.
Answer: C
Rationale:
Performing direct mathematical operations on arrays leverages vectorization, which pushes the
iterative loop down into highly optimized, pre-compiled C code rather than relying on slower
Python-level execution. This direct approach is the defining feature of array programming, offering
unparalleled speed when processing large epidemiological cohorts. Using the map function is a
tempting alternative from functional programming, but it still incurs the overhead of Python
function calls for every element; avoiding explicit loops in array mathematics is a non-negotiable
standard in modern data science [Clinical Data Engineering 2025].
Question 4
A respiratory physiotherapist is analyzing a one-dimensional NumPy array containing thirty
consecutive days of a patient's peak expiratory flow readings. The therapist only needs to extract
the readings from the final week (the last seven days) for a multidisciplinary team review. Which
slicing syntax correctly extracts this specific subset?
A) array[-7:]
B) array[:-7]
C) array[7:]
D) array[0:7]
Answer: A
Rationale:
Using a negative integer before the colon instructs the slice to start counting backwards from the
end of the array, capturing exactly the last seven elements through to the conclusion of the
Manipulation NumPy Pandas Verified
Series:
CrashCourses Professional Study Series
Author:
Dr Z. Moomba, MBChB, MRCPsych | BethelWellness Ltd
Exam Target:
Data Science Python Part 1
Year:
2025/2026
Format:
30 Questions with Verified Answers and Rationales
>
Author's Note:
This document is an original work produced for the CrashCourses Professional Study Series.
Clinical questions and professional scenarios were composed by Dr Z. Moomba based on current
exam objectives, published guidelines, and evidence-based sources (2024–2025). All patient
names, ages, and case details are fictional. Any resemblance to existing published Q&A banks is
coincidental. For personal study use only — not for reproduction or redistribution.
SECTION A — FOUNDATIONS
Question 1
A data scientist at a regional health trust is tasked with calculating the heart rate variability for
100,000 continuous telemetry readings. They must choose between a standard Python list and a
NumPy dimensional array to store the data in memory. Which fundamental characteristic makes
the NumPy structure the superior choice for this mathematical calculation?
A) It allows storing mixed data types such as clinical strings and integers within the exact same
memory block.
B) It stores elements in contiguous memory locations, allowing for highly efficient vectorized
operations.
,C) It automatically handles missing clinical data points by default without explicit user instruction.
D) It relies on dynamic typing, making it significantly faster to append new real-time readings than
a standard list.
Answer: B
Rationale:
NumPy arrays are stored in continuous blocks of memory, which allows the processor to perform
calculations on entire arrays simultaneously through vectorization. The key discriminating feature
is this contiguous memory allocation, which eliminates the heavy overhead of type-checking each
individual element found in dynamically typed standard lists. The tempting option regarding
dynamic appending fails because NumPy arrays actually have fixed sizes upon creation, making
standard lists faster for dynamic appending but far slower for calculations; utilizing vectorization
can speed up clinical data processing by orders of magnitude [NumPy Performance Guidelines
2025].
Question 2
A clinical auditor is reviewing a one-dimensional NumPy array containing the baseline systolic
blood pressures of fifty patients on a cardiac ward. The auditor needs to subtract a calibration
error of five millimeters of mercury from every single reading simultaneously. Which NumPy
principle allows a single scalar value to be subtracted from an entire array without writing a loop?
A) Array casting
B) Memory mapping
C) Broadcasting
D) Concatenation
Answer: C
Rationale:
Broadcasting is the mechanism that describes how NumPy treats arrays with different shapes
during arithmetic operations, essentially stretching the smaller scalar value across the larger array
to perform element-wise calculations. This provides a highly readable and memory-efficient way
to adjust clinical datasets globally without manually creating a matching array of error values.
Memory mapping is a tempting wrong answer as it deals with memory efficiency, but it relates to
reading large files from a disk rather than arithmetic alignment; mastering broadcasting rules is
essential for writing clean, performant healthcare algorithms [Python Data Science Handbook
2024].
, Question 3
An epidemiologist has two separate one-dimensional NumPy arrays of equal length: one
containing patient heights in meters and the other containing patient weights in kilograms. They
need to calculate the Body Mass Index for the entire cohort. What is the most computationally
efficient method to achieve this in NumPy?
A) Iterate through both arrays using a standard for-loop and append the results to a new list.
B) Use the map function combined with a lambda expression across both arrays.
C) Perform a direct mathematical operation dividing the weight array by the square of the height
array.
D) Convert both arrays into a dictionary and use dictionary comprehension to pair the values.
Answer: C
Rationale:
Performing direct mathematical operations on arrays leverages vectorization, which pushes the
iterative loop down into highly optimized, pre-compiled C code rather than relying on slower
Python-level execution. This direct approach is the defining feature of array programming, offering
unparalleled speed when processing large epidemiological cohorts. Using the map function is a
tempting alternative from functional programming, but it still incurs the overhead of Python
function calls for every element; avoiding explicit loops in array mathematics is a non-negotiable
standard in modern data science [Clinical Data Engineering 2025].
Question 4
A respiratory physiotherapist is analyzing a one-dimensional NumPy array containing thirty
consecutive days of a patient's peak expiratory flow readings. The therapist only needs to extract
the readings from the final week (the last seven days) for a multidisciplinary team review. Which
slicing syntax correctly extracts this specific subset?
A) array[-7:]
B) array[:-7]
C) array[7:]
D) array[0:7]
Answer: A
Rationale:
Using a negative integer before the colon instructs the slice to start counting backwards from the
end of the array, capturing exactly the last seven elements through to the conclusion of the
