Question 1

You are analyzing the performance metrics of a newly trained LLM model that has been optimized for sentiment analysis. The metrics include accuracy, precision, recall, and F1-score across different sentiment categories (positive, negative, neutral). You observe that the model performs well on the positive and negative categories but struggles with neutral sentiment, which is causing an imbalance in the overall performance metrics. The senior team member asks you to identify the root cause and suggest a solution. What is the most appropriate next step to address the performance imbalance?

Options :

A : Apply oversampling to the positive and negative sentiment categories.

B : Use a different evaluation metric that gives less importance to neutral sentiments.

C : Increase the weight of the neutral category in the loss function.

D : Increase the size of the training dataset by adding more neutral examples.

Answer: C

Question 2

You are assisting a senior team member in developing a generative AI model to generate product descriptions. The dataset includes hundreds of features, but you notice that many features are highly correlated with each other. What should be your approach to selecting features for the model?

Options :

A : Use Principal Component Analysis (PCA) to reduce dimensionality

B : Exclude all features that are correlated

C : Select features randomly to avoid bias

D : Include all features to capture maximum information

Answer: A

Question 3

You are deploying a generative AI model on an edge device with limited computational resources. The model must generate responses in near real-time while ensuring data privacy. Which strategy would be the most effective for balancing performance, accuracy, and privacy?

Options :

A : Perform all computations on the cloud

B : Use federated learning without model optimization

C : Use model quantization and secure enclave processing

D : Deploy the full model with no optimization

Answer: C

Question 4

When using cuDF and Dask cuDF to read large datasets directly into GPUs, which of the following best describes the advantage of using Dask cuDF over cuDF?

Options :

A : Dask cuDF allows for the distribution of data across multiple GPUs, enabling parallel processing

B : cuDF automatically scales the data processing across all available GPUs

C : Dask cuDF can only process structured data, while cuDF can handle both structured and unstructured data

D : Dask cuDF compresses the data before loading it into GPU memory

Answer: A

Question 5

You are assisting in deploying a machine learning model to a production environment. Your senior team member has asked you to evaluate the model's scalability and performance under varying loads. Which of the following steps should you take? (Select two)

Options :

A : Use load testing tools to simulate increasing numbers of requests to the model’s API.

B : Deploy the model on a single server without any redundancy to simplify testing.

C : Deploy the model without monitoring tools to reduce the system-s overhead.

D : Test the model only with the dataset it was trained on to ensure consistent performance.

E : Monitor CPU and memory usage while the model processes large batches of input data.

Answer: A,E

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