How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

What difficulty level is this interview question?

This is a medium difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at Google.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Google during technical interviews.

Analyze Call Drop Rates Pre- and Post-Update Implementation

Q: Analyze Call Drop Rates Pre- and Post-Update Implementation

This question evaluates a data scientist's competency in causal inference and quasi-experimental analysis for observational telemetry, emphasizing time-series reasoning and handling confounding in product metrics.

Evaluate a Non-Experimental Release: Google Meet Call Drops

Scenario

Engineers shipped a new Google Meet version intended to reduce call drops. A traditional A/B test was not possible.

Task

Describe analytical methods you would use to determine whether the new version is effective without a traditional experiment.

You may consider

Pre–post (interrupted time series)
Synthetic controls / Bayesian structural time series
Difference-in-differences (including staggered adoption, event studies)
Hold-out markets or geo-based quasi-experiments
Regression discontinuity in time

Assume access to call-level logs (drop outcome, timestamp), version/adoption timestamps, device/network/geo covariates, and that rollout timing varied slightly across users/regions due to normal app-store waves or enterprise policies.

Evaluate a Non-Experimental Release: Google Meet Call Drops

Scenario

Engineers shipped a new Google Meet version intended to reduce call drops. A traditional A/B test was not possible.

Task

Describe analytical methods you would use to determine whether the new version is effective without a traditional experiment.

You may consider

Pre–post (interrupted time series)
Synthetic controls / Bayesian structural time series
Difference-in-differences (including staggered adoption, event studies)
Hold-out markets or geo-based quasi-experiments
Regression discontinuity in time

Analyze Call Drop Rates Pre- and Post-Update Implementation

Quick Overview

Evaluate a Non-Experimental Release: Google Meet Call Drops

Scenario

Task

You may consider

Solution

Comments (0)

Analyze Call Drop Rates Pre- and Post-Update Implementation

Quick Overview

Evaluate a Non-Experimental Release: Google Meet Call Drops

Scenario

Task

You may consider

Solution

Comments (0)