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, 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 3

A Gen AI engineer is tasked with creating an application named MatchAI to match team members to projects. Which of the following sequences accurately represents the correct workflow?

Options :

A : Store Team Member Profiles in Vector Store → Query Project Requirements → Retrieve Vector Representations → Similarity Search → Generate Matches → Validate

B : Store Project Requirements in Vector Store → Query Team Member Profiles → Retrieve Vector Representations → Similarity Search → Generate Matches → Validate

C : Query Project Requirements → Retrieve Vector Representations → Store Team Member Profiles in Vector Store → Similarity Search → Generate Matches → Validate

D : Query Project Requirements → Store Team Member Profiles in Vector Store → Retrieve Vector Representations → Similarity Search → Generate Matches → Validate

Answer: A

Question 4

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 5

Which benefits of Databricks Model Serving contribute to improving efficiency in managing machine learning models in production?

Options :

A : Reduce operational costs by leveraging serverless computing.

B : Streamline the ML lifecycle, enabling data science teams to focus on core tasks.

C : Support for a wide range of models, including custom, foundational, and external models.

D : Built-in A/B testing for model performance optimization.

E : All of these options.

Answer: E

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