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| Section | Weight | Objectives |
|---|---|---|
| Discrete Mathematics & Logic | 25% | - Boolean algebra and digital logic - Set theory, relations, functions - Propositional and predicate logic - Proof techniques and mathematical induction |
| Data Structures | 20% | - Trees, graphs, hash tables - Arrays, linked lists, stacks, queues - Data storage and retrieval principles - Primitive and composite data types |
| Software Engineering & Programming Basics | 15% | - Basic syntax and control structures - Software development lifecycle - Testing and debugging fundamentals - Programming paradigms |
| Computer Architecture & Organization | 15% | - Memory hierarchy and performance - Instruction sets and execution cycles - CPU, memory, I/O systems - Von Neumann architecture |
| Algorithms & Complexity | 25% | - Algorithm design and analysis - Big O notation, time and space complexity - Recursion and iterative structures - Sorting and searching algorithms |
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NEW QUESTION # 61
How does the data type of a variable get set in Python?
Answer: D
Explanation:
Python usesdynamic typing, a core concept emphasized in programming language textbooks. In dynamically typed languages, a variable name does not permanently "own" a type. Instead, theobjectcreated by an expression has a type, and the variable becomes a reference to that object. Therefore, the type associated with a variable at any moment is determined by the value assigned to it. For example, after x = 7, x refers to an integer object. After x = "seven", the same name now refers to a string object. The type changes because the binding changes, not because the variable's type declaration was edited.
Option A describesstatic typingsystems (common in languages like Java, C, or C++), where programmers declare types and compilers enforce them. Python does not require such declarations for ordinary variables.
Option B is incorrect because type assignment is deterministic, not random. Option C is incorrect because Python does not default variables to strings; it assigns whatever type results from the right-hand-side expression.
This model is closely tied to Python's runtime behavior: type checks occur during execution, and functions can accept values of different types as long as the operations used are valid (often discussed as
"duck typing"). This flexibility supports rapid development, but also motivates careful testing and, in larger systems, optional type hints for documentation and tool support.
NEW QUESTION # 62
What Python code would return the value 40 from np_2d, where np_2d = np.array([[1, 2, 3, 4], [10, 20, 30,
40]])?
Answer: D
Explanation:
In a 2D NumPy array, indexing is written as array[row_index, column_index] using zero-based indices. The array np_2d = np.array([[1, 2, 3, 4], [10, 20, 30, 40]]) has two rows (indices 0 and 1) and four columns (indices 0, 1, 2, 3). The value 40 is located in the second row and the fourth column. Using zero-based indexing, that corresponds to row index 1 and column index 3. Therefore, np_2d[1, 3] returns 40.
Option A attempts to access row 3, which does not exist and would raise an IndexError. Option C attempts to access column 4 in row 0, but valid column indices are only 0 through 3, so it would also error. Option D likewise refers to a non-existent row 4. Only option B uses valid indices and points to the correct location.
Textbooks emphasize multi-dimensional indexing because it underlies matrix operations, dataset manipulation, and feature extraction in data science. Correctly interpreting rows and columns is essential when rows represent observations (like people) and columns represent attributes (like age, weight, height). This question tests precise control over row/column addressing, which prevents subtle bugs in numerical analysis.
NEW QUESTION # 63
What happens if you try to create a NumPy array with different types?
Answer: A
Explanation:
When NumPy constructs an ndarray, it chooses a single data type called the dtype for the entire array. This is a defining feature of NumPy arrays: unlike Python lists, which can hold mixed object types freely, a NumPy array is designed for efficient numerical computation by storing values in a uniform, contiguous representation. Therefore, if you provide mixed types at creation time, NumPy will select a dtype that can represent all provided values and will convert elements as needed.
This process is commonly described as type promotion or coercion to a common type. For example, mixing integers and floats produces a float array because floats can represent integers without loss of generality.
Mixing numbers and strings often results in a string dtype (or, in some cases, an object dtype), because numbers can be converted to their string representations. Once the dtype is chosen, the array behaves consistently under vectorized operations appropriate for that dtype.
Option B correctly summarizes this textbook behavior: the array will contain a single type, converting all elements to that type. Option A is too absolute-many mixed-type arrays still support calculations depending on the resulting dtype. Option C is vague and misses the crucial fact that conversion occurs. Option D is not how NumPy works; it never automatically splits inputs into multiple arrays by type.
Understanding dtype coercion matters because it affects memory usage, performance, and whether numerical operations behave as expected.
NEW QUESTION # 64
Which process is designed to establish the identity of the user such as with a username and password?
Answer: A
Explanation:
Authenticationis the security process of proving or establishing a user's identity. In textbook terminology, authentication answers the question: "Who are you?" Common authentication factors include something you know (password, PIN), something you have (smart card, hardware token), and something you are (biometrics). Username and password is the classic "something you know" mechanism, where the username identifies the account and the password serves as a secret used to validate that the user is the rightful owner of that account.
Authentication is distinct fromauthorization, which determines what an authenticated user is allowed to do (permissions, roles). It is also distinct from registration, which is the administrative act of creating an account or enrolling a user in a system. "Verification" is a general term that can appear in many contexts, but in security frameworks the precise term for identity establishment is authentication. "Certification" usually refers to issuing or validating credentials such as digital certificates (PKI) or professional certifications, not the act of logging in with a password.
Textbooks emphasize that authentication should be strengthened with practices like hashing and salting passwords, multi-factor authentication (MFA), lockout policies, and secure transport (e.g., TLS) to prevent credential theft. The core concept remains: the process that establishes identity using credentials like a username and password is authentication.
NEW QUESTION # 65
Which type of data structure is the only focus of a binary search?
Answer: C
Explanation:
Binary search is designed for searching in asorted (ordered) sequence. Its efficiency comes from repeatedly comparing the target to the middle element and discarding half of the remaining search space. This halving logic only works when the data is ordered, because the algorithm relies on the guarantee that all elements on one side of the midpoint are smaller (or larger) than the midpoint. In textbooks, this requirement is stated explicitly: binary search assumes the collection is sorted according to the same ordering used for comparisons.
An "ordered list" is therefore the correct focus among the options. Binary search can be implemented on arrays or other random-access structures where you can quickly access the middle element by index. While you can conceptually perform binary search on a linked list, it becomes inefficient because finding the middle requires linear traversal, losing the O(log n) advantage. Stacks and queues are not appropriate because they restrict access to ends only (LIFO for stacks, FIFO for queues), preventing direct access to the midpoint and making the binary search strategy infeasible.
Thus, the central requirement for binary search is a sorted/ordered sequence, typically supporting efficient indexing, which is why the correct choice is an ordered list.
NEW QUESTION # 66
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