Advanced Technical Product Manager Interview Questions Part 3, Cloud migration strategy, design an A/B testing experiment.
Advanced Technical Product Manager Interview Part 1
Advanced Technical Product Manager Interview Part 2
What security testing would you perform, and how would you respond to security vulnerabilities?
When it comes to keeping things secure, my focus is on being really proactive. That means we’ll definitely be running things like penetration tests – essentially trying to break in ourselves, just like a real attacker might – alongside vulnerability scanning to spot any known weaknesses. We’ll also do threat modeling early on in the process to think through all the potential ways someone could try to attack, and critically, we’ll have rigorous code reviews to catch vulnerabilities before they even make it into our systems. It’s about having multiple layers of defense and testing.
Now, if a security vulnerability is found – because sometimes they are, despite our best efforts – the absolute first step is figuring out how serious it is, right away. Then, it’s straight to working hand-in-hand with our engineering and security teams to build a fix. We’ll prioritize getting that fix out based on the potential impact; if it’s a critical issue, we won’t hesitate to push an urgent patch, and we’ll make sure to be completely transparent with affected users about what happened and what we’ve done. Once everything’s stable, we’ll always do a post-mortem – basically, a thorough review – to understand exactly how it happened and what we can change in our practices to prevent anything similar from occurring in the future.
Cloud migration strategy
Suppose you need to migrate a monolithic application to a cloud-native architecture. What migration strategy would you use?
Being a technical Product Manager, when confronted with the challenge of moving a monolithic app to a cloud-native setting, I would rather thoroughly contemplate the possibility of a phased migration approach to guarantee easy and successful migration.
To begin with, I would perform a thorough review of the current monolithic application, determining its major constituents, dependencies, and the system’s level of complexity overall. This would enable me to make the appropriate selection of migration technique and challenges encountered in the process.
One of the migration strategies that I would use is the “strangler pattern” where I would migrate the application’s functionality into a cloud-native architecture incrementally, one piece at a time. This would involve familiarizing myself with the loosely coupled modules of the monolith first, implementing cloud-native services to replace them, and then slowly routing traffic to the new services while keeping the overall system functional.
Another approach I could pursue is the “lift-and-shift” method where I would shift the monolithic application initially to the cloud in its existing form without having to go through a full transformation of the codebase. This would give an initial quick win by getting the application into the cloud, and thereafter I would reimplement and update the components in phases.
Regardless of which migration strategy is followed, I would make sure that the design of an overall migration plan, including exhaustive testing, phased deployment, and a well-defined rollback plan is accomplished initially. This will ensure that the end-users’ migration experience can be made as seamless as possible.
How would you assess the readiness of the application for cloud migration?
While assessing the cloud-readiness of an application, I would assess it multi-dimensionally. I would first closely inspect the architecture of the application. It is crucial to determine if it is monolithic or microservices architecture since cloud platforms live on the flexibility and scalability provided by microservices.
Next, I’d evaluate data dependencies and integration points. It’s important to know how the application integrates with databases and other external services. For instance, if we’re porting an application of a game, we must migrate player data, game state, and real-time interaction into the cloud with no decrease in performance.
In addition, I’d be assessing compliance and security mandates, particularly in an environment that is regulated, such as gaming. Being aware of the legal aspect of data processing and having strong security measures in place will be key to an uneventful migration.
Finally, I’d be performing performance testing and load analysis to project the application’s performance in the cloud. Talking with the development and operations teams so that they could share their perspectives can also uncover what might go wrong.
Overall, a detailed examination of architecture, data dependencies, compliance, and performance will give the overall picture of readiness of the application for cloud migration. Alignment across teams will play an important role in ensuring ease of transition, opening up doors for increased scalability and agility in the cloud.
What tools and services would you use to manage the migration, and how would you measure success?
Based on my experience of working at Humana handling gigantic-scale healthcare system migrations, I would have an enterprise-wide game system migration plan with negligible downtime and data integrity in mind. I would utilize AWS Database Migration Service for migration, supplemented with strong ETL tools such as Talend or Informatica to execute complex data mappings with referential integrity between player profiles and transaction history.
For code deployment and version control, I would use GitLab CI/CD pipelines with automated testing and phased deployments in development, staging, and production environments. I successfully used this method at Humana too where we attained 99.9% uptime for big system migrations. Docker using containerization and Kubernetes for orchestration would provide us with consistent deployment across environments but with the option to roll back when necessary.
As a metric for measuring success, I use key performance indicators monitored using monitoring tools such as Datadog or New Relic. Performance metrics will include system response time, error rates, data consistency checks, and user experience metrics. For instance, API response times prior to and after migration must be enhanced or at least remain on par with previous levels. Database query time and resource consumption would be monitored to meet optimal levels.
Among the important things is applying feature flags using tools like LaunchDarkly to facilitate incremental rollout and A/B testing of the migrated components. This would give quick rollback feature as well as valuable user behavior information. The measure of success would be in pre- and post-migration player interaction levels, transaction rate of success, and system stability metrics.
Can you design an A/B testing experiment to measure the impact of a new feature on user engagement?
An A/B test starts with specifying the experiment to a certain business goal—like growing daily active users or in-game spend. This is my process, refined at Humana while experimenting with the effect of engagement for a telehealth feature:
1. Define Hypothesis & Metrics: If Aristocrat wants to test a new “social leaderboard” feature, I’d hypothesize it increases session time by 15%. Track primary metrics (avg. playtime, leaderboard interactions) and guardrails (no drop in revenue or app crashes).
2. Split Audiences: Randomly divide the users into control (old UI) and variant (leaderboard) audiences with demographics/behavior parity (e.g., with Statsig). We stratified by age/risk profiles at Humana to avoid biased health outcomes.
3. Test the Run: Roll out to 10-20% of users to start with, listen for bugs, and scale. Use live dashboards with a tool like Optimizely or Firebase.
4. Statistical Significance: 95% confidence and min detectable effect (e.g., 10% lift). At Humana, we canceled one med reminder test as soon as it came back with negative engagement—resource conservation.
5. Iterate & Analyze: Test, then drill into cohorts (e.g., did part-timers beat pros?). If leaderboard wins, ship; if flat, A/B test adjustments (e.g., w/ rewards for top rank).
How would you select the control and treatment groups, and what metrics would you use to measure success?
To form control and treatment groups, I would make them statistically sound, demographically similar, and randomized to eliminate bias. The control group remains on the original experience, and the treatment group receives the new feature. For some user segments to target, I would make both groups have similar behavior and demographics to eliminate bias for comparison.
To determine success, I’d monitor primary and secondary metrics
Adoption Rate – Ratio of treatment group users utilizing the feature.
Engagement & Retention – Duration, number of uses, repeat usage.
Conversion Rate – If linked to revenue or activity (e.g., sign-ups, purchases).
Error Rates & Performance Metrics – No adverse effect on system performance.
Customer Satisfaction (NPS, CSAT) – Collect qualitative data.
What tools would you use to implement the experiment, and how would you analyze the results?
Being a technical Product Manager, I would make use of various tools and methods in conducting and quantifying experiments in such a way that it makes the whole process data-driven and effective.
To execute the experiment, I would probably use a mix of A/B testing tools, like Optimizely or Google Optimize, to configure and execute the experiment. These tools offer the capability needed to build and carry out the experiment, including setting the control and variant buckets, capturing users’ interactions, and aggregating the information pertinent to the experiment.
To compare the results, I would use a combination of data visualization and statistical analysis tools. A few examples of tools that can be used include Tableau or Power BI to develop interactive dashboards and reports, and statistical software such as R or Python to perform deeper data analysis. With these tools, I was able to spot and monitor the metrics of relevance like the rates of conversion, user engagement, and behavior, to ascertain the effects of the experiment.
Second, I would use qualitative data to supplement the quantitative data like user feedback and observations to interpret the results of the experiment in the best possible manner. This can be done by using tools such as Hotjar or UserTesting to record user sessions, surveys, and other information provided by the users.
During work, I would ensure that I have a clear and data-driven approach in that experiment design, data collection, and analysis meet the intended purpose of the product and organization’s best practices.
Can you describe a situation where you had to communicate complex technical information to non-technical stakeholders?
At Humana Inc., I had to describe implementing a new analytics platform to a non-technical group of stakeholders when they were part of the project’s critical review. I segmented the complicated data flow into terms people can comprehend using graphics and analogies—drawing a parallel with the operation of a familiar assembly line—to more clearly describe how the data flows and is converted into actionable insights. This method not only unbureaucratized the jargon but also allowed the team to grasp the short-term business implications. By tracking the essential points through documentation and the brief follow-up summary, I kept the team on track and steady on the project path. I look forward to applying this skill in bridging the technical and business views at Aristocrat Gaming in Austin, AR.