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 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

Question 3

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 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

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

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