Handling Edge Cases and Failures | Dataford Interview Questions

Your question is Handling Edge Cases and Failures. Take a moment with it on the right.

Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).

Problem

Amperecloud's monitoring dashboard shows fleet-wide solar inverter output, computed from a batch of readings collected every few minutes. On-call has seen this function crash the whole batch job when a single inverter reports a malformed reading, taking the dashboard down for every site until the bad record is manually removed.

def compute_average_output(readings):
    total = 0
    for r in readings:
        total += r["power_kw"]
    average = total / len(readings)

    peak = readings[0]["power_kw"]
    for r in readings:
        if r["power_kw"] > peak:
            peak = r["power_kw"]

    efficiency = readings[-1]["power_kw"] / readings[-1]["rated_kw"]

    return {"average": average, "peak": peak, "efficiency": efficiency}

How does this code handle edge cases or potential failures? Go through each way it can break in production and what you would change.

Your question is Handling Edge Cases and Failures. Take a moment with it on the right.

Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).

def compute_average_output(readings):
    total = 0
    for r in readings:
        total += r["power_kw"]
    average = total / len(readings)

    peak = readings[0]["power_kw"]
    for r in readings:
        if r["power_kw"] > peak:
            peak = r["power_kw"]

    efficiency = readings[-1]["power_kw"] / readings[-1]["rated_kw"]

    return {"average": average, "peak": peak, "efficiency": efficiency}

How does this code handle edge cases or potential failures? Go through each way it can break in production and what you would change.