Question 1

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 2

You are working with a senior team member to deploy a large language model (LLM) across a multi-cloud environment. The senior team member asks you to assist in evaluating the model's scalability. The model is expected to handle a significant increase in traffic as the user base grows, and you need to identify any potential bottlenecks. What is the most appropriate first step in evaluating the model's scalability in this scenario?

Options :

A : Analyze the cost of running the model across different cloud providers.

B : Evaluate the model-s accuracy with a larger dataset.

C : Optimize the model’s inference time before deployment.

D : Conduct load testing to simulate high traffic conditions.

Answer: D

Question 3

You are part of a team developing a summarization tool designed to condense lengthy legal documents into brief, actionable summaries. The tool is intended to assist lawyers by quickly highlighting the most critical information. The senior engineer has asked you to help determine which aspects of the tool’s design are most critical for producing high-quality summaries. What is the most crucial factor to consider when building the summarization tool for legal documents?

Options :

A : Train the model using a generic dataset of various document types.

B : Optimize the model to generate summaries as quickly as possible.

C : Focus on reducing the model-s computational resource requirements.

D : Ensure the model captures the context and nuances of legal terminology

Answer: D

Question 4

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

Question 5

You are tasked with performing a system analysis to integrate a generative LLM into an existing customer relationship management (CRM) system. The goal is to automate customer response generation while ensuring the system meets all performance and security specifications. During the analysis, you identify potential bottlenecks in the system that could affect the LLM's response time. Which strategy should you prioritize to ensure the LLM integration meets the performance specifications without compromising security?

Options :

A : Focus on enhancing the LLM’s model to handle larger datasets without adjusting the system.

B : Optimize the CRM system’s infrastructure to reduce latency and ensure faster LLM response times.

C : Limit the LLM-s capabilities to reduce its computational load and improve performance.

D : Disable security features temporarily to improve performance during peak times.

Answer: B

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