AI & Technical question

What are the algorithms used by Demand Side platforms in a programmatic advertising model?

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Technical understanding of programmatic advertising's algorithmic decision making on the buy side.

Describe real time bidding algorithms: predicting the value of an impression and setting an optimal bid within milliseconds.
Describe click through and conversion rate prediction models: machine learning models estimating the likelihood a user will engage.
Describe audience targeting algorithms: matching campaign criteria against user segments using behavioral and contextual signals.
Describe budget pacing algorithms: distributing spend evenly or strategically across a campaign's flight dates.
Describe frequency capping and fraud detection algorithms: limiting repeat exposure and filtering invalid traffic before bidding.
Tie these together: all feed into an optimization loop maximizing a target outcome, like conversions per dollar spent.

Names specific algorithm types, bid prediction, conversion prediction, and pacing, rather than a vague 'AI decides the ads'.
Explains the real time constraint explicitly, that bidding decisions happen within milliseconds per impression.
Mentions fraud detection as a distinct algorithmic function, showing awareness beyond just targeting and bidding.
Ties the algorithms together into a single optimization goal, like maximizing conversions per dollar, rather than listing them disconnected.

Describing DSP algorithms vaguely as 'machine learning' with no specific function named.
Ignoring the real time, millisecond-level constraint that shapes how these systems are actually built.

How would a DSP handle a new campaign with no historical performance data?
How would you evaluate if the bidding algorithm is actually improving campaign ROI?