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Generative AI Leader Practice Question: Use Google DeepMind's advances in protein…
A company wants to use Google DeepMind's advances in protein structure prediction to accelerate drug discovery. Which DeepMind achievement is most relevant to this goal?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
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AlphaFold
AlphaFold solves protein structure prediction, a key enabler for drug discovery. AlphaGo is for board games, AlphaCode for programming, and WaveNet for audio.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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AlphaFold
Why this is correct
AlphaFold predicts three-dimensional protein structures directly from amino acid sequences, solving a decades-old challenge in structural biology. This capability lets researchers model target proteins and potential binding sites computationally, dramatically accelerating lead identification and drug discovery timelines compared with slower experimental methods such as X-ray crystallography.
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WaveNet
Why it's wrong here
WaveNet generates raw audio waveforms for text-to-speech and music synthesis, so it cannot predict protein structures. Its appeal lies in DeepMind's generative modelling reputation, and it would be correct if the scenario involved speech synthesis, voice cloning or audio generation instead of drug discovery.
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AlphaCode
Why it's wrong here
AlphaCode generates competitive-programming source code, not three-dimensional protein structures. It tempts because it is a prominent DeepMind generative system, and it would be the right choice if the task were automated code generation or software engineering assistance rather than molecular biology.
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AlphaGo
Why it's wrong here
AlphaGo mastered the board game Go through reinforcement learning and tree search, offering no protein-folding capability. It is tempting because it demonstrated DeepMind's general game-playing strength, and it would be the right answer if the goal were game AI or sequential decision-making benchmarks rather than molecular structure prediction.
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Written by Johnson Ajibi, MSc IT Security
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This Generative AI Leader practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Generative AI Leader exam.