Latency and cost benchmarking in Agentic AI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to measure the latency of a function call.

Agentic AI

import time
start = time.[1]()
result = my_function()
end = time.perf_counter()
latency = end - start
print(f"Latency: {latency} seconds")

Drag options to blanks, or click blank then click option'

Aperf_counter

Bsleep

Ctime

Dprocess_time

Attempts:

3 left

💡 Hint

Common Mistakes

Using time.sleep() instead of a timer function.

Using time.time() which has lower resolution.

We use time.perf_counter() because it provides the highest available resolution timer to measure short durations accurately.

2fill in blank

medium

Complete the code to calculate the average cost per API call given total cost and number of calls.

Agentic AI

total_cost = 50.0  # dollars
num_calls = 200
average_cost = total_cost [1] num_calls
print(f"Average cost per call: ${average_cost:.4f}")

Drag options to blanks, or click blank then click option'

A/

B*

C+

D-

Attempts:

3 left

💡 Hint

Common Mistakes

Multiplying total cost by number of calls.

Adding or subtracting instead of dividing.

To find the average cost per call, divide the total cost by the number of calls.

3fill in blank

hard

Fix the error in the code to correctly record the latency of multiple runs and compute the average latency.

Agentic AI

import time
latencies = []
for _ in range(5):
    start = time.perf_counter()
    run_task()
    end = time.[1]()
    latencies.append(end - start)
avg_latency = sum(latencies) / len(latencies)
print(f"Average latency: {avg_latency:.5f} seconds")

Drag options to blanks, or click blank then click option'

Asleep

Btime

Cprocess_time

Dperf_counter

Attempts:

3 left

💡 Hint

Common Mistakes

Using time.time() for end but perf_counter() for start.

Using sleep instead of a timer.

The end timer must match the start timer function perf_counter() to measure elapsed time correctly.

4fill in blank

hard

Fill both blanks to create a dictionary of API call latencies filtered by calls longer than 0.1 seconds.

Agentic AI

latency_data = {call_id: [1] for call_id, [2] in api_calls.items() if latency > 0.1}

Drag options to blanks, or click blank then click option'

Alatency

Bduration

Attempts:

3 left

💡 Hint

Common Mistakes

Using the same variable name for both blanks.

Using undefined variable names.

The dictionary comprehension uses latency as the value and duration as the variable for each call's time.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary of call costs where cost is above 0.05 dollars and keys are uppercase.

Agentic AI

filtered_costs = [1]([2]: cost for [3], cost in call_costs.items() if cost > 0.05)

Drag options to blanks, or click blank then click option'

Adict

Bk.upper()

Ck

Dv

Attempts:

3 left

💡 Hint

Common Mistakes

Not converting keys to uppercase.

Using wrong variable names in comprehension.

The dictionary is created with dict(), keys converted to uppercase with k.upper(), and iterating over keys k and values cost.