Question 1

As a Gen AI engineer, you’re tasked with monitoring your AI system to ensure it performs effectively and cost-efficiently. Which of the following scenarios best represents an effective monitoring strategy for diagnosing potential issues in your AI model?

Options :

A : Monitoring the system-s user interface for aesthetic design improvements and user experience enhancements

B : Regularly reviewing input data, prompt queries and responses to identify any inconsistencies or anomalies.

C : Focusing solely on analyzing performance/cost details without reviewing mid-training checkpoints or human feedback.

D : Evaluating the model’s performance only during final deployment phases to ensure it meets all business requirements

Answer: B

Question 2

A Generative AI Engineer is creating an LLM-powered application that will need access to up-to-date news articles and stock prices. The design requires the use of stock prices which are stored in Delta tables and finding the latest relevant news articles by searching the internet. How should the Generative AI Engineer architect their LLM system?

Options :

A :

Use an LLM to summarize the latest news articles and lookup stock tickers from the summaries to find stock prices.

B :

Query the Delta table for volatile stock prices and use an LLM to generate a search query to investigate potential causes of the stock volatility.

C :

Download and store news articles and stock price information in a vector store. Use a RAG architecture to retrieve and generate at runtime

D :

Create an agent with tools for SQL querying of Delta tables and web searching, provide retrieved values to an LLM for generation of response.

Answer: D

Question 3

You are developing an AI application that needs to handle and generate diverse types of content for an interactive storytelling platform. The platform must integrate text and images seamlessly, allowing users to generate stories with accompanying visuals. Which of the following approaches best leverages Multi-Model Architectures for this scenario?

Options :

A : Embed text and images into the same vector space but do not use any advanced Multi-Modal LLMs, relying on basic retrieval systems to handle each modality individually.

B : Use one modality, such as text, as the primary focus and adapt images according to predefined templates or rules, without dynamically generating or integrating images with the text.

C : Use a Multi-Modal LLM like GPT-4V(ision) to generate both the narrative text and associated images in a unified manner, allowing the system to produce a story with integrated visuals based on user input.

D : Store text and images separately in distinct databases, and use separate models for text generation and image retrieval, without integrating them into a single system.

Answer: C

Question 4

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

Question 5

As a Gen AI engineer developing an intelligent system that integrates various external tools and APIs, which statement best describes the role of agents?

Options :

A : Agents only handle coding tasks and do not interact with other types of tools.

B : Agents rely solely on search tools, like web browsing and search engines, for all tasks.

C : Agents use pre-configured tools without considering the context of the task.

D : "Agents' reasoning skills decide which tools to use and when/how to use them.

Answer: D

Free Databricks-Certified-Generative-AI-Engineer-Associate Mock Exam – Practice Online Confidently

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