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Accenture Generative AIQuestions & Answers

Practice 200 verified Accenture Generative AI questions with detailed answers and explanations. Tap any question below to study the full solution — perfect for last-minute Accenture primer and dumps prep.

Generative AI primarily aims to:Which of these is NOT typically produced by generative models?Learning a data distribution p(x) allows a model to:Which statement best contrasts discriminative and generative models?Which is a common application of generative AI?Generative AI that helps artists by suggesting concepts is an example of:A model that learns to produce plausible human faces has learned approximations of:Which capability is NOT typical of generative models?Which of the following is a risk specifically mentioned for generative AI?Text generation, image generation and music generation are examples of:Why is learning a distribution more powerful than memorizing examples?Which of these is a direct benefit of synthetic data?A generative model that outputs new molecules would be used in:Which term best describes creating content that resembles training data but is not identical?Generative AI differs from classification because it focuses on:Gaussian Mixture Models (GMMs) are examples of:Hidden Markov Models (HMMs) are especially useful for:Which breakthrough enabled deep generative models to scale in the 2010s?The VAE paper was published by:GANs introduced the idea of:“Attention Is All You Need” introduced:Which year is commonly associated with the original GAN paper?Transformers replaced recurrence with:Which early model is probabilistic and explicitly models density?VAEs are celebrated for:Which model family is known as “implicit density”?The rise of LLMs was enabled by:Which contribution is attributed to Goodfellow et al.?CycleGAN is notable because it can:Which development made sampling from complex distributions more practical?Machine Learning systems typically start with:A perceptron computes:Which activation is most used to mitigate vanishing gradients?Backpropagation uses which calculus tool to compute gradients?Gradient descent updates weights to:Deep networks learn hierarchical features—early layers learn:Overfitting happens when the model:Which is NOT an optimizer for neural networks?Dropout is used to:Cross-entropy loss is most often used for:A bias term in a neuron is analogous to:Batch normalization primarily helps by:Which layer type is most common in image models?Transfer learning helps when:An epoch means:Explicit density models provide:Normalizing Flows are an example of:Which model family does a VAE belong to?Implicit models are characterized by:Which is a tractable explicit model?Which approach approximates likelihoods using ELBO?Sampling from an implicit model requires:Which model gives exact likelihoods (when tractable)?Which is an advantage of explicit density models?An example of implicit modeling is:Which family is well-suited to likelihood-based anomaly detection?ELBO stands for:Which is a limitation of implicit models?Tractable models are useful because they allow:VAEs, GANs and Flows are examples of:A standard autoencoder differs from a VAE because a VAE:The reparameterization trick allows:VAE loss includes reconstruction loss plus:Sampling z = μ + σ ⊙ ε moves randomness to:A common prior used in VAEs is:VAEs typically produce images that are:KL term in VAE encourages:Advantages of VAEs include:Which is a limitation of VAEs?In VAEs, the decoder maps from:ELBO maximization is equivalent to:Choosing a too-large KL weight will typically:VAEs are useful for:A well-structured latent space allows:Which is true about VAE encoder output?GANs train by:Mode collapse means the generator:If discriminator becomes too strong early, the generator may suffer from:DCGAN stands for a GAN variant optimized for:StyleGAN introduced:CycleGAN is primarily used for:The generator maps noise z to:Adversarial loss tries to make discriminator output for generated samples:A typical fix for mode collapse is:GANs are categorized as:Which is a common component of GAN training to stabilize it?Which GAN variant gives control over style at multiple scales?Discriminator\'s role is to:GAN training objective is best described as:A challenge when training GANs is:RNNs maintain memory via:Vanishing gradient makes it hard to learn:LSTM introduces which mechanism to control information?GRU differs from LSTM by:Sequence generation can be performed by training models to predict:Teacher forcing is a training technique where:Which is a limitation of RNNs compared to Transformers?RNN backpropagation through time requires:Applications of sequence models include:Beam search is used in generation to:Scheduled sampling mixes:An RNN cell output depends on:Which cell is computationally lighter?Sequence-to-sequence (seq2seq) models typically have:Teacher forcing can lead to:Self-attention allows tokens to:Positional encoding provides:Multi-head attention helps by:Transformers are more parallelizable than RNNs because:Decoder-only models like GPT are trained to:BERT is primarily used for:Transformer encoder blocks include:Masked self-attention prevents a token from attending to:Scaling transformers (more params + data) led to:A positional encoding can be:Which model is decoder-only?Attention scores are computed from queries, keys and values using:Transformer attention is typically multi-head to:Encoder-decoder transformers are commonly used for:Which is an advantage of Transformers over RNNs?Generative AI in healthcare can help by:In drug discovery generative models can:A major ethical risk of generative AI is:Which practice helps reduce model bias?Copyright concerns arise because models may:Responsible deployment includes:Which industry widely uses generative AI for creative media?Data augmentation via generative models mainly helps to:Regulation and policy are needed because:A practical mitigation for deepfakes is:Multi-modal generative models combine:Job displacement risk suggests:Which direction is important for future generative AI?Intellectual property questions involve:When deploying a generative model for production, you should:Which of the following is a real-world example of Generative AI?Which AI type primarily focuses on labeling data?What distinguishes Generative AI from Discriminative AI?Which of the following is a real-world example of GenerativeAI?If a model is better at distinguishing between classes rather than generating data, it is likely a _______. Generative model Which claim regarding generative models isn't true?Reparameterization trick is used to... Improve model accuracy Deal with the non-differentiability of sampling in VAEs The training process of GANs is often likened to which game?What does GAN stand for?What is a challenge faced during GAN training due to the minimax game concept?In GANs, which component is responsible for evaluating the authenticity of data?Which component of a GAN is responsible for generating new data samples?Progressive GANs are designed to address which challenge in traditional GANs?Which type of GAN allows for generating data based on specific categories?In the GAN architecture, what is the primary goal of the Discriminator?Which of the following is a real-world application where GANs have shown significant promise?What is mode collapse in the context of GANs?Which GAN variant focuses on gradually increasing the resolution of generated images?Which is NOT a real-world application of GANs?In GANs, if the discriminator becomes too powerful, what can happen?Which statement about GANs is true?Mode collapse is problematic because...What is a challenge in evaluating the performance of GANs?Which component of a GAN tries to produce fake data?The generator's objective in GANs is to...In the minimax game of GANs, what is the discriminator's goal?Which GAN variant can be conditioned on labels to generate specific outputs?How many images are there in each class of the CIFAR-10 dataset?What is used to refine the models during training?In the provided code, why is discriminator.trainable set to False when setting up the combined system?Which of the following is NOT a feedback given to the generator during training?Why might someone want to use GANs on the CIFAR-10 dataset?Which technique can help in dealing with training instability in GANs?Which of the following best describes the role of the generator in a GAN?Which challenge refers to the generator producing limited varieties or even the same sample every time?Which architecture can help address convergence issues in traditional GANs?In the generator code, what is the purpose of the Reshape layer?During training, what does the generator use to improve itself?What does the discriminator do in a GAN?In the discriminator's code, which layer helps in reducing the dimensions of the input image?Which activation function is used in the final layer of the generator model?RNNs are primarily used for which type of data?What is the key advantage of using LSTMs over basic RNNs in sequence generation tasks?Which problem in RNNs does LSTM help to address?When using RNNs for music generation, what does each neuron in the output layer typically represent?In NLP, what does RNNs help to predict?Which RNN architecture utilizes update and reset gates to manage memory?What does RNN stand for?During the training of RNNs for sequence generation, what is the common technique used to mitigate the vanishing gradient problem?Which of the following is NOT a type of RNN architecture?Which of the following is NOT a typical use case for RNNs?Which of the following is a common application of RNNs in NLP?Why might one use GRU over LSTM?In sequence generation tasks, what is the primary input to an RNN at each time step?Which RNN architecture uses a reset and update gate?How do RNNs handle variable-length sequences in NLP?Which problem arises when training RNNs on long sequences?What is the main advantage of LSTM over basic RNN?What is the role of the `<OOV>` token?Which layer in the RNN model represents words as detailed feature lists?Why is padding used in the preprocessing step?What advantage does LSTM have over traditional RNNs?What is the purpose of the Dropout layer in the LSTM with Dropout model?What might be a concern if the training accuracy is high but validation accuracy is significantly low?In which scenario might you prefer a simple RNN over an LSTM?

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