Question: How do you evaluate the impact of parents joining Facebook on teenagers?

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This is a real interview question for meta facebook data engineer position. Let's deep dive on this question and see how to crack a meta facebook data engineer interview. The Analysis pattern/framework applies to all questions.

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Rationale

‍This question, while open-ended, helps assess how candidates structure problems and identify the data needed to address them. The focus is on their analytical framework and their ability to break down the analysis into separate, manageable steps.  Framing the question correctly is crucial, and this skill is being primarily evaluated.

Essentially, this question has two core components: (1) How can one accurately identify parent-teen relationships on Facebook? (2)  How does one evaluate the impact once relationships are identified? Candidates should address both aspects, and realizing their distinct nature is an encouraging sign.

Part 1: Identifying Relationships

Useful Input

‍Some Facebook users might explicitly state their relationships, but for others, the following can be leveraged: last names, age gaps, shared hometowns/current locations, devices or IP addresses used, second-degree connections (e.g., siblings' parents), and certain keywords ("mom," etc.)  used in posts or messages.

Candidates should understand that simply listing these input factors is insufficient.  The data points need to be translated into a confident decision about the relationship (yes/no). Many candidates might miss this crucial translation step.

There are a few ways to approach this.  Some might propose rule-based logic (e.g., the last name must match, age difference between 20-40 years). They could try validating these rules with known relationships to optimize the rule combinations.

More advanced candidates might frame this as a machine learning problem, training a classifier using the mentioned inputs. This is valid, but follow-up questions are beneficial: How exactly would they approach the ML task? Would they classify individuals or the connection between them? It's also worth probing whether they understand where to find suitable training data. Some might use self-identified relationships, but both positive and negative examples are needed.  Where would those negatives come from?

Finally, ask how they'd evaluate model performance. This is classic classifier evaluation, with options like human validation or surveys.  Standard metrics (false positives/negatives, precision/recall) apply.  The specific answer is less important than gauging their reaction to this crucial aspect.

Part 2: Evaluating Impact

How does one quantify impact?  This taps into traditional metrics: visits, engagement, communication,  etc. Let candidates settle on one.

Then, this could be framed as a pre/post analysis or a regression.

Candidates should recognize inherent bias. They need a "control" group, but what defines that group?

Most might pick random users without parents on Facebook.  Question how valid this comparison is, given that users whose parents join are a distinct subset.

Better candidates will aim for a demographically-matched comparison.  But is this enough?  Challenge: If those with parents joining show a 5% increase in time spent while the other group remains unchanged, what can be concluded? Note two confounding factors:  "parent" and "join". The 5% could stem from either or both.

By this point, strong candidates should realize the question focuses on the "parent" aspect, not simply "joining" (context is teens). They should adjust the benchmark to a demographically-matched group with non-parents joining.

If candidates get this far, it's excellent. Then, discuss how confidently this implies causality. Does parental presence cause changed engagement, or could a latent factor drive both joining and behavior shift (e.g., graduation)?

Conclusion

This question covers machine learning, evaluation, metrics, behavioral comparisons, causality vs. correlation, bias, etc. Ideal candidates will structure it independently and proactively address many of the above points.  Average candidates will need more guidance.  The perfect answer matters less than this analytical exercise, revealing a candidate's ability to break down a complex question.

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