🔒 Mod previzualizare. Primele cincisprezece lecții Foundations sunt gratuite; aceasta este Pro. Pornește un trial de 7 zile pentru a debloca editorul, sugestiile AI și restul programului. Card necesar, anulează oricând în Dashboard.Pornește trial de 7 zile →
← Cursuri›Senior Deep-Dives›Module 4 · Memory + profiling · Recap›Gestionarea erorilor: excepții vs tipuri de rezultatequiz86 / 161
+100 XP
Sarcină🌐 shown in EN
📝 **Task:** Build a small \`memory_estimator(rows, bytes_per_row, overhead_pct)\` that returns total memory in MB (float, 2 decimals):
\`\`\`
total_bytes = rows * bytes_per_row * (1 + overhead_pct / 100)
mb = total_bytes / 1_000_000 # decimal MB — matches Prometheus/Grafana convention
return round(mb, 2)
\`\`\`
Test for 3 realistic scales: small in-memory cache, mid-size pandas DataFrame, large Redis dataset.
📋 Implement the function above. Tests run automatically.
💡 **Hint:** Re-read the theory if you get stuck.
🎯 Test
Întrebare
📝 **Task:** Build a small \`memory_estimator(rows, bytes_per_row, overhead_pct)\` that returns total memory in MB (float, 2 decimals):
\`\`\`
total_bytes = rows * bytes_per_row * (1 + overhead_pct / 100)
mb = total_bytes / 1_000_000 # decimal MB — matches Prometheus/Grafana convention
return round(mb, 2)
\`\`\`
Test for 3 realistic scales: small in-memory cache, mid-size pandas DataFrame, large Redis dataset.
📋 Implement the function above. Tests run automatically.
💡 **Hint:** Re-read the theory if you get stuck.