Question 1

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

Question 2

As a Gen AI engineer working on a project that involves analyzing large volumes of customer feedback stored in a Databricks SQL, which of the following approaches is most appropriate for efficiently generating insights using an AI model?

Options :

A : Implement a custom script to manually invoke the Foundation Models API for each customer feedback entry individually, avoiding batch processing to ensure immediate responses.

B : Utilize a real-time streaming API to continuously feed customer feedback data into the AI model, even though the feedback is stored in SQL and does not change frequently.

C : Use ai_query() to perform batch inference directly from the Databricks SQL, which automatically parses and processes completions from the Foundation Models API.

D : Manually export the customer feedback data from SQL into a CSV file, then process the file one entry at a time using a local AI model without batching.

Answer: C

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

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 setting up LLM-as-a-Judge for evaluating your model outputs, which of the following best practices should you follow to ensure effective evaluation?

Options :

A : Employ a wide variety of examples, including only examples of good outputs, to guide the LLM’s evaluation process.

B : Use a broad rubric scale, such as 1-10, to capture nuanced differences in the quality of outputs.

C : Use a high-token LLM to provide more context and detail in the evaluation, improving the quality of feedback from the model.

D : Avoid using an additive scale, as it can complicate the evaluation process and introduce inconsistencies.

Answer: C