15-minute session: read for 3 minutes, trace for 3, experiment for 7, and complete the check for 2. Spend longer whenever you need.
Use a monotonic high-resolution clock. Warm up separately, repeat a fixed workload, verify a checksum, and report median plus spread. Record units, data size, environment, and compiler/runtime version. The following measures Python sum on this CPU and includes interpreter/runtime costs; it is not a GPU or raw memory-bandwidth benchmark.
Practice and evidence: Predict the example or draw its state transitions. Change one assumption, test the boundary, and record the actual result. Explain what this small model does not establish about a production system.
import time,statistics,platform
data=list(range(100000))
expected=4999950000
assert sum(data)==expected
samples=[]
for _ in range(7):
start=time.perf_counter_ns()
result=sum(data)
samples.append((time.perf_counter_ns()-start)/1e6)
assert result==expected
print("CPU Python",platform.python_version(),platform.machine())
print("median_ms",round(statistics.median(samples),4))
print("min_ms",round(min(samples),4),"max_ms",round(max(samples),4))
Keep three notes: your prediction, the actual result, and why they match or differ. The completion check grades understanding; the practice task builds skill.