OSAT Computer Science (181)
OSAT Computer Science (181) is a trademark of its owner, which is not affiliated with and does not endorse Test Treasure Publication or TTP Prep.
OSAT Computer Science (181) is a trademark of its owner, which is not affiliated with and does not endorse Test Treasure Publication or TTP Prep.
10 sample questions for the OSAT Computer Science (181), with the correct answer and an explanation for each. No account needed. Try each one before you open the answer.
Sections covered on the exam: Computational Thinking and Algorithm Design, Programming and Program Development, Computer System Concepts, Learning Environments and Social Impacts of Computing, Pedagogy, Preconditions.
Question 1 · Computational Thinking and Algorithm Design
Answer: D. B6
Split the byte into 4-bit groups: 1011 = 11 = B and 0110 = 6, so the value is B6. Writing 6B reverses the two nibbles, and D6 comes from misreading 1011 as 1101.
Question 2 · Computational Thinking and Algorithm Design
Answer: B. Abstraction, by keeping only the details riders need to plan a route
Abstraction means filtering out details that do not matter for the purpose at hand; riders need connections and order of stations, not street names or exact track curves. Decomposition would involve breaking the problem into smaller subproblems, which is not what removing detail from the map does.
Question 3 · Computational Thinking and Algorithm Design
Answer: D. No such program can exist; Turing proved the halting problem is undecidable
Turing showed by contradiction that no algorithm can decide halting correctly for every program-input pair, so the problem is undecidable, not merely slow. A time limit fails because a program that has not stopped yet might still stop later, so the checker cannot distinguish slow programs from infinite loops.
Question 4 · Computational Thinking and Algorithm Design
Answer: D. An index-out-of-bounds error occurs when i reaches 4
The list has 4 elements with valid indexes 0 through 3, but i <= length(nums) lets i reach 4. Reading nums[4] goes past the end, causing an out-of-bounds runtime error before anything is printed. Using i < length(nums) would visit each element once and print 22.
Question 5 · Computational Thinking and Algorithm Design
Answer: D. Answer targeted questions about how specific lines work, such as tracing or annotating the loop
PRIMM stands for Predict, Run, Investigate, Modify, Make, so after running the code students investigate it through focused questions, tracing, and annotation. Changing the loop's range is the Modify stage, which comes after investigation, and writing an original program is the final Make stage.
Question 6 · Computer System Concepts
Answer: C. 229
Octal place values are 64, 8, and 1, so 3 × 64 + 4 × 8 + 5 × 1 = 192 + 32 + 5 = 229. The value 837 results from reading 345 as hexadecimal (3 × 256 + 4 × 16 + 5).
Question 7 · Computational Thinking and Algorithm Design
Answer: A. Pattern recognition, by identifying the shared structure across the scripts
Noticing that different problems share a common structure is pattern recognition; it often leads next to abstraction, such as a single drawPolygon(sides) procedure. Decomposition is tempting, but the student is comparing whole solutions for similarities, not breaking one problem into parts.
Question 8 · Computational Thinking and Algorithm Design
Answer: D. X is intractable and Y is undecidable
An intractable problem is solvable in principle but not in reasonable (polynomial) time for large inputs, while an undecidable problem, such as the halting problem, has no algorithm at all that is always correct. Reversing the labels confuses being too slow with being impossible.
Question 9 · Computational Thinking and Algorithm Design
Answer: A. 21
Each element becomes itself plus the already-updated element before it, so the list becomes [4, 11, 12, 21, 24]. nums[3] is 9 + 12 = 21. The answer 10 uses the original neighbor (9 + 1), forgetting that nums[2] was already changed to 12.
Question 10 · Learning Environments and Social Impacts of Computing
Answer: C. To model authentic debugging and show that errors are a normal, fixable part of programming
Live coding with deliberate mistakes makes an expert's debugging process visible, including reading output, forming hypotheses, and fixing off-by-one errors, and normalizes errors for novices. Catching inattentive students may be a side effect, but the technique's purpose is modeling the problem-solving process.
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