Online Access Free NCA-GENL Exam Questions

Which metric is primarily used to evaluate the quality of the text generated by language models?

• A.Precision
• B.Perplexity
• C.Recall
• D.Accuracy

In the evaluation of Natural Language Processing (NLP) systems, what do 'validity' and 'reliability' imply regarding the selection of evaluation metrics?

• A.Validity refers to the speed of metric computation, whereas reliability pertains to the metric's performance in high-volume data processing.
• B.Validity is concerned with the metric's computational cost, while reliability is about its applicability across different NLP platforms.
• C.Validity ensures the metric accurately reflects the intended property to measure, while reliability ensures consistent results over repeated measurements.
• D.Validity involves the metric's ability to predict future trends in data, and reliability refers to its capacity to integrate with multiple data sources.

In the context of evaluating a fine-tuned LLM for a text classification task, which experimental design technique ensures robust performance estimation when dealing with imbalanced datasets?

• A.Bootstrapping with random sampling.
• B.Stratified k-fold cross-validation.
• C.Single hold-out validation with a fixed test set.
• D.Grid search for hyperparameter tuning.

When deploying an LLM using NVIDIA Triton Inference Server for a real-time chatbot application, which optimization technique is most effective for reducing latency while maintaining high throughput?

• A.Reducing the input sequence length to minimize token processing.
• B.Switching to a CPU-based inference engine for better scalability.
• C.Increasing the model's parameter count to improve response quality.
• D.Enabling dynamic batching to process multiple requests simultaneously.

In the Transformer architecture, which of the following statements about the Q (query), K (key), and V (value) matrices is correct?

• A.Q represents the query vector used to retrieve relevant information from the input sequence.
• B.V is used to calculate the positional embeddings for each token in the input sequence.
• C.Q, K, and V are randomly initialized weight matrices used for positional encoding.
• D.K is responsible for computing the attention scores between the query and key vectors.