🔒 Režim náhledu. Prvních patnáct lekcí Foundations je zdarma; tato je Pro. Spusťte 7denní trial pro odemčení editoru, AI nápověd a zbytku kurzu. Karta vyžadována, zrušte kdykoli v Dashboard.Spustit 7denní trial →
← Kurzy›Senior Deep-Dives›Module 4 · Memory + profiling · Recap›Zpracování chyb: výjimky vs typy výsledkůquiz86 / 161
+100 XP
Úkol🌐 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.
🎯 Kvíz
Otázka
📝 **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.