Question 1

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 2

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

Question 3

You are preparing a large-scale dataset for training a language model aimed at detecting hate speech in social media posts. The dataset is diverse, containing text in multiple languages and various social media platforms. You need to ensure high-quality labels and annotations for effective training. Which of the following approaches would most effectively ensure the quality and relevance of the dataset annotations?

Options :

A : Use automated keyword detection tools to label the dataset and skip manual review

B : Crowdsource the annotation task to non-experts for faster labeling

C : Label the dataset using a simple binary system (hate speech or not) without considering context or severity

D : Engage domain experts to manually review and annotate a portion of the dataset, and use these as a gold standard to train an automated labeling tool

Answer: D

Question 4

During the evaluation of a deployed LLM model, you notice that the model’s response times are inconsistent, with significant delays occurring sporadically. What is the most likely cause of these performance fluctuations?

Options :

A : Overfitting of the model

B : Random initialization of model weights

C : Insufficient model training

D : Network bandwidth limitations

Answer: D

Question 5

You are working on a project that involves developing a named entity recognition (NER) system using spaCy. The system must accurately identify and categorize entities in a large corpus of unstructured text data. Additionally, the data processing pipeline must be optimized for performance and scalability. Which two actions would best enhance the performance and accuracy of the NER system in this scenario? (Select two)

Options :

A : Fine-tune the spaCy model on a domain-specific dataset

B : Use NumPy to perform vectorized operations on large datasets

C : Deploy the spaCy model on a single-threaded CPU environment

D : Manually annotate a small subset of the data for training

E : Disable spaCy-s built-in tokenizer

Answer: A,B

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