NumPy 2.x Interview
6
594
No official exam: interview preparation
Practice tests untimed; Exam Mode test 99 minutes
60% target score
Multiple choice, single answer
Array Creation and Data Types | Constructors, dtypes, NumPy 2 promotion rules (NEP 50), StringDType and removed aliases (8%) Indexing, Slicing, Copies and Views | Basic and fancy indexing, boolean masks, views versus copies and the NumPy 2 copy keyword (7%) Reshaping, Joining and Splitting Arrays | reshape, ravel, transpose and mT, moveaxis, stack, concatenate and split (12%) Broadcasting, Ufuncs and Vectorization | Broadcasting rules, ufunc methods such as reduce, outer and at, and vectorised code (13%) Mathematics, Statistics and Linear Algebra | Reductions, NaN-aware statistics, numpy.linalg, matmul, vecdot and einsum (10%) Random Number Generation | The Generator API with default_rng, seeding, distributions and sampling (4%) Structured Arrays, Masked Arrays and Iteration | Structured dtypes, numpy.ma and nditer (9%) Performance, Memory and Benchmarking | Memory layout, dtypes, in-place operations, profiling and timeit (11%) File I/O and Ecosystem Integration | .npy and .npz files, text loaders, memmap, pandas, SciPy, C and Fortran, GPUs and DLPack (14%) Testing, Debugging, Documentation and Community | numpy.testing, pytest, Python error handling, numpydoc and NEPs (12%)
Python developers preparing for NumPy-heavy technical interviews Data science and machine learning candidates Data engineers and analysts moving from NumPy 1.x to NumPy 2.x Students and bootcamp graduates validating their array-programming skills
Array Creation and Data Types
Under NumPy 2 promotion rules (NEP 50), what happens with np.uint8(1) + 300?
It returns np.int64(301)
It raises OverflowError, because 300 does not fit in uint8
It silently wraps round to 45
It returns the Python int 301
B
NEP 50 treats Python scalars as "weak": they take on the NumPy operand's dtype instead of upcasting it. A Python int that cannot be represented in that dtype raises OverflowError. NumPy 1.x would have upcast the result, which is why this catches out many candidates who learned NumPy before version 2.
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NumPy Interview Questions Practice Tests: 6 Practice Tests and 594 Questions

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About this course

These practice tests are for Python developers, data scientists and data engineers preparing for technical interviews that rely on NumPy. They are a quick way to find gaps before the real conversation.

You get 6 practice tests with 99 questions each, 594 questions in total, including a timed Exam Mode test. Every question explains why the right answer is right and why the others are wrong, and each one belongs to one of ten topic areas, from broadcasting and indexing to linear algebra, performance and testing. The series was rebuilt for 2026 against the current NumPy 2.x documentation. Every answer key was re-checked, duplicates were replaced with new questions, and the content now covers NumPy 2 changes such as NEP 50 promotion rules, the new copy semantics, StringDType, removed aliases and the Generator API.

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