Google Professional Machine Learning Engineer Question 314
Select 3Google Cloud PlatformYour team has deployed a new version of a recommendation model on Google Cloud, and you want to use A/B testing to compare its performance against the current production model. Which steps should you take to ensure a fair and reliable A/B test in this scenario?
- A
Divide the user traffic randomly between the two models to avoid selection bias.
- B
Use a single evaluation metric to measure the performance of both models during the test period.
- C
Monitor key metrics such as latency and error rates for both models during the test.
- D
Ensure that the test period is long enough to capture variations in user behavior over time.
- E
Deploy the new model to all users to gather a larger dataset for evaluation.
Show answer and explanation
Correct answers: A, C, D
Explanation
A/B testing involves comparing two versions of a model by splitting user traffic and measuring their performance under identical conditions. It is important to randomly divide the traffic to avoid bias, monitor key metrics to ensure reliability and maintain user experience, and run the test over a long enough period to account for user behavior variations. Avoiding actions like full deployment ensures the integrity of the test and prevents invalid comparisons.
- A. Correct.
Correct. Randomly dividing user traffic ensures that the test results are not influenced by pre-existing biases in user characteristics, making the comparison fair.
- B. Incorrect.
Incorrect. Relying on a single evaluation metric may not provide a comprehensive understanding of the model's performance. Multiple metrics should be considered to evaluate trade-offs and overall effectiveness.
- C. Correct.
Correct. Monitoring operational metrics like latency and error rates helps ensure the new model is functioning as expected and does not negatively impact user experience.
- D. Correct.
Correct. A longer test period allows for capturing variations in user behavior, such as seasonal trends or changes in preferences, leading to more reliable results.
- E. Incorrect.
Incorrect. Deploying the new model to all users would eliminate the A/B testing setup and make it impossible to compare the performance of the two models side-by-side.