Free NVIDIA-Certified Associate: Generative AI with LLMs (NCA-GENL) Practice Questions

Sample NCA-GENL Practice Questions

Browse all 10 free NVIDIA-Certified Associate: Generative AI with LLMs practice questions below.

1. In a transformer model, what is the primary purpose of the self-attention mechanism?

   • To reduce the computational complexity of the model by replacing sequential recurrence with parallel token processing across the entire sequence
   • To allow each token in a sequence to attend to all other tokens and capture contextual relationships
   • To compress the input embedding dimension by projecting high-dimensional token representations into a lower-dimensional subspace for memory efficiency
   • To apply regularization during training by randomly masking tokens and forcing the network to reconstruct missing inputs from context

2. Which component of the transformer architecture is responsible for preserving the sequential order information of input tokens?

   • Positional encoding
   • The layer normalization component that stabilizes training by normalizing activations across the feature dimension for each sample independently
   • The feed-forward neural network layers that apply independent non-linear transformations to each position's representation after attention computation
   • The multi-head attention mechanism that computes weighted combinations of value vectors using query-key similarity scores across multiple subspaces

3. In multi-head attention, why does the transformer use multiple attention heads instead of a single attention mechanism?

   • To speed up inference time by distributing the attention computation across multiple GPUs, enabling hardware-level parallelism for each head
   • To reduce the total number of parameters in the model by sharing weight matrices across positions and decomposing large attention operations
   • To prevent gradient vanishing during backpropagation by creating multiple independent gradient pathways through the attention layers
   • To allow the model to attend to information from different representation subspaces at different positions

4. What is the key difference between the encoder and decoder components in an encoder-decoder transformer architecture?

   • The encoder exclusively uses self-attention while the decoder relies solely on cross-attention to encoder outputs for all token interactions
   • The decoder uses masked self-attention to prevent attending to future tokens during training, while the encoder uses bidirectional attention
   • The decoder uses larger embedding dimensions and wider feed-forward layers than the encoder to improve generation quality and fluency
   • The encoder typically has more layers than the decoder to better process and encode input representations before passing them to generation

5. In the attention mechanism, what mathematical operation is performed on the Query (Q) and Key (K) matrices to compute attention scores?

   • Element-wise multiplication of query and key vectors followed by summation across the feature dimension to produce position-wise similarity scores
   • A convolution operation applied across query and key matrices to capture local positional patterns and short-range dependencies between tokens
   • Concatenation of the query and key vectors for each position pair followed by a learned linear transformation to compute compatibility scores
   • Matrix multiplication (dot product) followed by scaling and softmax

6. What is the primary advantage of FlashAttention in modern transformer implementations?

   • It reduces memory usage by computing attention incrementally using tiling and recomputation techniques
   • It replaces multi-head attention with a single consolidated attention head, reducing the total number of learnable parameters across the model's layers significantly
   • It applies post-training quantization to compress attention weight matrices to 8-bit integer precision, reducing memory bandwidth requirements during computation
   • It eliminates the need for softmax normalization in the attention mechanism by using a linear approximation that scales more efficiently with sequence length

7. In decoder-only transformer architectures like GPT, what is the purpose of the KV (Key-Value) cache during inference?

   • To enable efficient batch processing of multiple input sequences simultaneously by sharing intermediate representations across parallel decoding streams
   • To compress the model's weight parameters during deployment by storing them in a lower-precision format that reduces GPU memory consumption
   • To store previously computed key and value vectors from past tokens, avoiding redundant computation during autoregressive generation
   • To implement causal attention masking by dynamically constructing triangular mask matrices that prevent each token from attending to future positions in the sequence

8. What is Multi-Query Attention (MQA) and how does it differ from standard multi-head attention? (Select TWO)

   • MQA uses a single set of key and value projection matrices shared across all attention heads, while maintaining separate query projections per head
   • MQA processes multiple user queries simultaneously in a single forward pass by parallelizing the attention computation across different input sequences in the batch
   • MQA eliminates the query projection matrices entirely and computes attention scores using only the key and value representations, reducing the total parameter count per layer
   • MQA increases the number of attention heads beyond the standard configuration to improve the model's representational capacity and pattern recognition ability
   • MQA significantly reduces the KV cache memory footprint during inference, enabling higher throughput and longer context lengths on the same hardware

9. Why do decoder-only architectures like GPT use causal (masked) self-attention?

   • To compress the KV cache memory footprint during inference by quantizing stored key-value pairs to lower precision formats like INT8 or FP8
   • To reduce the computational complexity of the self-attention mechanism by approximately half, since only the lower triangular portion of the attention matrix needs to be calculated
   • To enable bidirectional attention patterns where each token can attend to both preceding and following positions, improving contextual understanding of the full sequence
   • To prevent the model from attending to future tokens during training, ensuring each position only depends on previous positions

10. What happens during the token generation process when a decoder-only LLM produces an output?

    • The model generates all tokens in the output sequence simultaneously in a single forward pass using parallel decoding, producing the complete response at once rather than sequentially
    • The model outputs dense embedding vectors from its final transformer layer that are directly used as the text representation without any additional projection or sampling step
    • The model outputs logits (raw scores) for each vocabulary token, which are converted to probabilities via softmax and then sampled or selected
    • The model directly outputs text characters one at a time using a character-level mapping function, bypassing any intermediate probability computation or vocabulary-based token selection