A/B testing model versions in MLOps - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding A/B Testing Traffic Split

You want to run an A/B test for two model versions: v1 and v2. You configure the traffic split as 70% to v1 and 30% to v2. What will happen to incoming user requests?

A 70% of user requests will be served by model v1, and 30% by model v2.

B All user requests will be served by model v1 until 70 requests are served, then switch to v2.

C Only 30% of users will get any response, the rest will be dropped.

D User requests will be randomly served by either model without respecting the 70/30 ratio.

💡 Hint

Think about how traffic splitting works in A/B testing to distribute load.

In A/B testing, traffic split means the percentage of incoming requests routed to each model version. Here, 70% go to v1 and 30% to v2, so requests are distributed accordingly.

💻 Command Output

intermediate

Interpreting A/B Test Metrics Output

You run an A/B test comparing model v1 and v2. The output metrics show:

{"v1_accuracy": 0.85, "v2_accuracy": 0.88, "v1_requests": 700, "v2_requests": 300}

What does this output tell you?

A Model v1 has higher accuracy and more requests; traffic split is 50/50.

B Both models have the same accuracy; traffic split is incorrect.

C Model v2 has higher accuracy but fewer requests; traffic split matches 70/30 ratio.

D Model v2 has lower accuracy but more requests; traffic split is reversed.

💡 Hint

Look at accuracy values and request counts carefully.

The metrics show v2 has better accuracy (0.88 vs 0.85) and received 300 requests, which is 30% of total 1000 requests, matching the 70/30 traffic split.

❓ Configuration

advanced

Configuring Canary Deployment for Model Version

You want to deploy a new model version v3 as a canary with 10% traffic, while 90% remains on v2. Which YAML snippet correctly configures this traffic split in a Kubernetes service mesh?

A

apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
spec:
  http:
  - route:
    - destination:
        host: model-service
        subset: v3
      weight: 50
    - destination:
        host: model-service
        subset: v2
      weight: 50

B

apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
spec:
  http:
  - route:
    - destination:
        host: model-service
        subset: v3
      weight: 90
    - destination:
        host: model-service
        subset: v2
      weight: 10

C

apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
spec:
  http:
  - route:
    - destination:
        host: model-service
        subset: v2
      weight: 10
    - destination:
        host: model-service
        subset: v3
      weight: 90

D

apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
spec:
  http:
  - route:
    - destination:
        host: model-service
        subset: v2
      weight: 90
    - destination:
        host: model-service
        subset: v3
      weight: 10

💡 Hint

Remember weights must add up to 100 and match the desired traffic percentages.

The correct config routes 90% traffic to v2 and 10% to v3. Option D matches this exactly. Others reverse weights or split incorrectly.

❓ Troubleshoot

advanced

Diagnosing A/B Test Data Skew

After running an A/B test for 24 hours, you notice model v2 received 80% of traffic instead of the configured 50%. What is the most likely cause?

A The monitoring tool aggregated data incorrectly, actual traffic was 50/50.

B The traffic routing configuration was updated incorrectly, causing uneven weights.

C User sessions were sticky, causing repeated routing to v2 for returning users.

D Model v1 crashed, so all traffic was rerouted to v2 automatically.

💡 Hint

Consider what controls traffic distribution and what might cause imbalance.

Incorrect traffic routing weights or config changes can cause skew. Crashes or sticky sessions could cause issues but would not consistently cause 80% traffic to v2 unless config changed. Monitoring errors do not affect actual traffic.

🔀 Workflow

expert

Order of Steps in A/B Testing Model Deployment

Arrange the following steps in the correct order to perform a safe A/B test deployment of a new model version:

1. Analyze test results and decide winner
2. Deploy new model version alongside existing
3. Configure traffic split between old and new versions
4. Monitor performance and collect metrics

A 1,2,3,4

B 2,1,3,4

C 1,3,2,4

D 2,3,1,4

💡 Hint

Think about the logical flow from deployment to decision.

First deploy the new model alongside the old (1), then configure traffic split (2) to start sending requests to both. Next monitor and collect metrics (3), and finally analyze results to decide the winner (4).