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 GenAI engineer, how would you describe the role of MLOps in managing machine learning systems?

Options :

A : MLOps is exclusively concerned with developing and testing machine learning models before they are deployed into production.

B : MLOps is limited to managing code repositories and version control for machine learning projects.

C : MLOps primarily focuses on automating data collection processes and managing databases for business intelligence.

D : MLOps is automation for managing data, code and models to improve performance, stability and long-term efficiency of ML systems.

Answer: D

Question 3

As a Generative AI engineer, you are responsible for developing and deploying a machine learning model that provides real-time predictions for a financial services application. The model must be monitored for performance, and you need to track its training metrics, serve it in production, and evaluate its accuracy over time. Which combination of Databricks products would best address these requirements?

Options :

A : Use MLflow Tracking to record and manage the model-s training metrics, Model Serving for deploying the model in production, and Lakehouse Monitoring to monitor the model-s performance and operational metrics.

B : Use Lakehouse Monitoring to evaluate the model-s accuracy, MLflow Tracking to deploy the model, and Model Serving to monitor the model-s performance in production.

C : Use MLflow Evaluation to track the model-s training process, MLflow Tracking for evaluating the model-s accuracy, and Model Serving to deploy the model.

D : Use MLflow Tracking to monitor the model-s real-time performance, MLflow Evaluation for tracking training metrics, and Lakehouse Monitoring to serve the model in production.

Answer: A

Question 4

What is a key challenge when implementing reranking in a Retrieval-Augmented Generation (RAG) pipeline for a Gen AI application?

Options :

A : Reranking simplifies the RAG pipeline by reducing the overall complexity and streamlining the process.

B : Reranking requires minimal computational resources, making it an efficient process for large datasets.

C : Reranking eliminates the need for any further processing or adjustments to the retrieved documents before generating a response.

D : Reranking involves multiple calls to the LLM, which can significantly increase both the cost and latency of the RAG chain.

Answer: D

Question 5

Which of the following features is offered by Databricks Model Serving for AI models?

Options :

A : It does not offer any monitoring or payload logging features for external models like OpenAI or Anthropic.

B : It requires separate tools and interfaces for managing foundation models and external models.

C : It only supports models built with MLflow, limiting the integration of external models.

D : It provides a unified UI, API, and SDK for managing custom models, foundation models, and external models, ensuring seamless governance, monitoring, and logging.

Answer: D

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