Question 1

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 2

Which of the following parameters can affect the determinism and creativity of an LLM?

Options :

A : All of these options

B : "Top-k" value

C : "Top-p" value

D : "Temperature" value

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 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 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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