Question 1

As a Gen AI engineer, you are preparing to deploy a large application that integrates multiple LLMs and external GenAI services. What is a critical step to ensure the deployment is successful and the application runs smoothly in production?

Options :

A : Prioritize optimizing each model-s performance before deployment to maximize efficiency.

B : Concentrate on reducing the overall application size to ensure faster deployment and lower resource usage.

C : Package the entire application together, including all models, endpoints, and necessary configurations, to ensure that all components function cohesively as part of a unified deployment pipeline.

D : Focus on deploying each LLM model separately to ensure flexibility and modular updates.

Answer: C

Question 2

As a Gen AI engineer working on improving a search engine’s ranking algorithm, you want to evaluate the effectiveness of your new ranking model. Which metric would you use to assess how well the model ranks the most relevant documents higher, taking into account both the position of the documents and their relevance?

Options :

A : Normalized Discounted Cumulative Gain (NDCG)

B : Mean Reciprocal Rank (MRR)

C : Precision at k

D : F1 Score

Answer: A

Question 3

As a Gen AI engineer utilizing MLflow for managing your model lifecycle, which feature of MLflow is most beneficial for handling both traditional machine learning and Gen AI workflows effectively?

Options :

A : MLflow provides a detailed user manual for each model flavor, which includes extensive theoretical explanations and guidelines for using different types of models.

B : MLflow’s unified platform allows you to manage end-to-end ML and GenAI workflows, including deployment, monitoring, and evaluation, from development to production.

C : MLflow exclusively supports GenAI-specific model flavors and evaluation metrics, without compatibility for traditional machine learning models.

D : MLflow focuses solely on managing model training and does not offer tools for model deployment or evaluation, requiring additional external tools for these tasks.

Answer: B

Question 4

As a Gen AI Engineer, you have a task to decide if you need a vector database for your Q&A chat bot. In which of the following scenarios you should use a vector database?

Options :

A : When you need to handle large-scale data, perform fast semantic searches, and require persistent storage.

B : When you need to quickly prototype a solution with a smaller dataset that can be handled in-memory.

C : When you need to store and retrieve large amounts of structured data based on exact matches.

D : When you need to perform keyword-based searches on a relational database.

Answer: A

Question 5

As a Gen AI engineer setting up LLM-as-a-Judge for evaluating your model outputs, which of the following best practices should you follow to ensure effective evaluation?

Options :

A : Employ a wide variety of examples, including only examples of good outputs, to guide the LLM’s evaluation process.

B : Use a broad rubric scale, such as 1-10, to capture nuanced differences in the quality of outputs.

C : Use a high-token LLM to provide more context and detail in the evaluation, improving the quality of feedback from the model.

D : Avoid using an additive scale, as it can complicate the evaluation process and introduce inconsistencies.

Answer: C

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