Question 1

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 2

As a Gen AI engineer tasked with selecting a deployment strategy for an email marketing system that generates customer segmentation reports daily based on accumulated data, which deployment strategy would be most appropriate given the need to process data on a regular schedule and write results to persistent storage?

Options :

A : Batch Deployment

B : Real-Time Deployment

C : Edge Deployment

D : Streaming Deployment

Answer: A

Question 3

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 4

As a Gen AI engineer managing the lifecycle of models in MLflow, which of the following URI patterns are used for referencing models in the Unity Catalog (MLflow) Model Registry? Choose two correct answers.

Options :

A : models:/{model_name}/latest

B : models:/{model_name}@{alias}

C : models:/{model_name}@{version}

D : models:/{model_name}/{model_version}

Answer: B,D

Question 5

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