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 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 3

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 4

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 5

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

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