Question 1

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 2

As a Gen AI engineer, you are preparing to evaluate your language model both before and after deployment. Which of the following approaches best describes the key focus of online evaluation compared to offline evaluation?

Options :

A : Offline evaluation focuses on analyzing real user behavior data collected from live deployments, while online evaluation involves curating benchmark data and using task-specific metrics.

B : Offline evaluation is concerned with collecting user feedback through live interactions, whereas online evaluation relies on predefined benchmark datasets and task-specific metrics.

C : Online evaluation requires extensive preparation of benchmark datasets before deployment, while offline evaluation is centered on real-time monitoring of user interactions with the model.

D : Online evaluation involves deploying the app and assessing the model based on real user interactions and feedback, whereas offline evaluation is conducted using curated benchmark data and task-specific metrics.

Answer: D

Question 3

In which of the following scenarios model learns using RAG?

Options :

A : Model Fine Tuning

B : None of these options

C : Model Pre-Training

D : Passing Contextual Information

Answer: D

Question 4

A Generative Al Engineer is responsible for developing a chatbot to enable their companys internal HelpDesk Call Center team to more quickly find related tickets and provide resolution. While creating the GenAI application work breakdown tasks for this project, they realize they need to start planning which data sources (either Unity Catalog volume or Delta table) they could choose for this application. They have collected several candidate data sources for consideration: call_rep_history: a Delta table with primary keys representative_id, call_id. This table is maintained to calculate representatives call resolution from fields call_duration and call start_time. transcript Volume: a Unity Catalog Volume of all recordings as a *.wav files, but also a text transcript as *.txt files. call_cust_history: a Delta table with primary keys customer_id, cal1_id. This table is maintained to calculate how much internal customers use the HelpDesk to make sure that the charge back model is consistent with actual service use. call_detail: a Delta table that includes a snapshot of all call details updated hourly. It includes root_cause and resolution fields, but those fields may be empty for calls that are still active. maintenance_schedule “ a Delta table that includes a listing of both HelpDesk application outages as well as planned upcoming maintenance downtimes. They need sources that could add context to best identify ticket root cause and resolution. Which TWO sources do that? (Choose two.)

Options :

A : call_cust_history

B : maintenance_schedule

C : call_rep_history

D : call_detail

E : transcript Volume

Answer: D,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

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