Separate changed tasks from claims the data cannot support.
Use before-and-after time logs, staffing evidence limits, new skill requirements and pilot rates. Calculate the stated changes without forecasting an occupation.
Four tasks each count one correct answer. Your count out of four describes these supplied cases, not a fixed collaboration ability, software certificate or employment prediction. Every worked answer and guide remains available.
What the four cases actually supply
The cases supply task durations, a description with no staffing figures, a training capability table and two small pilot counts. They ask for the total time, the limit on a headcount conclusion, the course covering both stated needs and the difference between an error count and rate. They do not ask you to guess which real careers will disappear.
Include new work in a time comparison
Suppose a fictional process changes from 20 minutes copying plus 10 checking to 5 copying, 15 checking and 5 logging. The total moves from 30 to 25 minutes, not to five. The tool reduces one task while other tasks remain or expand. This arithmetic alone cannot reveal staffing, customer demand or whether the new process is better in every respect.
Check denominators and missing evidence
For a missed rate item, keep the numerator and denominator visible: two errors among ten cases differs from two among twenty. For a staffing item, ask whether headcount and demand were supplied at all. A changed task or smaller rate is a supported observation; a definite job-loss count or causal claim needs evidence the exercise does not provide.
Explain one change without overclaiming
Build an invented before-and-after task list, include any added checking or exception work, and sum both sides. Then list what cannot be inferred. For a fictional training choice, compare required skills with the course features one by one. If using a real workplace example later, verify its actual data and rules rather than treating this illustrative task log as a benchmark.
Background is not a forecast or credential
Public career-exploration curriculum provides a background interest in technology’s workplace effects. It neither validates this bank nor supplies our fictional numbers. These original tasks check constrained reasoning, without population norms, a recruitment cutoff or a guaranteed training outcome. Repeated answers after studying the guide may reflect memory rather than a newly demonstrated work skill.