Question 1

You are deploying a generative AI model that creates natural language responses in a customer service chatbot. During testing, the model occasionally generates incorrect but highly confident answers. Which of the following strategies is most appropriate to reduce this risk?

Options :

A : Increase the size of the training dataset

B : Add more layers to the model

C : Reduce the model-s temperature during inference

D : Implement an adversarial training regime

Answer: C

Question 2

What is the purpose of the NVIDIA NeMo Toolkit?

Options :

A : NeMo focuses on the morphology of a language by studying its words, and how they are formed.

B : NeMo helps researchers to develop models that trade-off size with minimum loss impact.

C : NeMo facilitates the creation of models for speech recognition and natural language understanding.

D : NeMo helps researchers develop state-of-the-art models for computer vision based on convolutions.

Answer: C

Question 3

When fine-tuning an LLM for a specific application, why is it essential to perform exploratory data analysis (EDA) on the new training dataset?

Options :

A : To uncover patterns and anomalies in the dataset

B : To select the appropriate learning rate for the model

C : To assess the computing resources required for fine-tuning

D : To determine the optimum number of layers in the neural network

Answer: A

Question 4

You are building a generative AI model for a medical chatbot that will provide diagnostic suggestions based on patient inputs. The dataset includes a large amount of unstructured text data from various medical records. The accuracy of the model is critical, but so is the model's ability to handle highly domain-specific terminology and nuances. Which of the following strategies would be most effective in ensuring the model understands and accurately responds to the medical context?

Options :

A : Preprocess the dataset to remove all complex medical terms to simplify the data

B : Use a generic pre-trained model and fine-tune on the medical dataset without additional preprocessing

C : Label all instances of medical jargon as generic terms in the dataset

D : Leverage a pre-trained medical LLM (like BioBERT) and fine-tune it on your specific dataset

Answer: D

Question 5

A generative AI model is deployed in a real-time application but is experiencing latency issues, causing delays in response time. Which of the following actions would most effectively address this performance problem?

Options :

A : Upgrade the model to a more complex version

B : Increase the batch size used during inference

C : Optimize the model-s inference process by reducing the number of layers

D : Adjust the learning rate used during model training

Answer: C

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