Databricks Certified Generative AI Engineer Associate - Certified Generative AI Engineer Associate Exam
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Total 80 questions
Question #6 (Topic: Exam A)
A Generative AI Engineer has a provisioned throughput model serving endpoint as part of a RAG application and would like to monitor the serving endpoint’s incoming requests and outgoing responses. The current approach is to include a micro-service in between the endpoint and the user interface to write logs to a remote server.
Which Databricks feature should they use instead which will perform the same task?
Which Databricks feature should they use instead which will perform the same task?
A. Vector Search
B. Lakeview
C. DBSQL
D. Inference Tables
Answer: D
Question #7 (Topic: Exam A)
A Generative Al Engineer is tasked with improving the RAG quality by addressing its inflammatory outputs.
Which action would be most effective in mitigating the problem of offensive text outputs?
Which action would be most effective in mitigating the problem of offensive text outputs?
A. Increase the frequency of upstream data updates
B. Inform the user of the expected RAG behavior
C. Restrict access to the data sources to a limited number of users
D. Curate upstream data properly that includes manual review before it is fed into the RAG system
Answer: D
Question #8 (Topic: Exam A)
A Generative Al Engineer is creating an LLM-based application. The documents for its retriever have been chunked to a maximum of 512 tokens each. The Generative Al Engineer knows that cost and latency are more important than quality for this application. They have several context length levels to choose from.
Which will fulfill their need?
Which will fulfill their need?
A. context length 514; smallest model is 0.44GB and embedding dimension 768
B. context length 2048: smallest model is 11GB and embedding dimension 2560
C. context length 32768: smallest model is 14GB and embedding dimension 4096
D. context length 512: smallest model is 0.13GB and embedding dimension 384
Answer: D
Question #9 (Topic: Exam A)
A small and cost-conscious startup in the cancer research field wants to build a RAG application using Foundation Model APIs.
Which strategy would allow the startup to build a good-quality RAG application while being cost-conscious and able to cater to customer needs?
Which strategy would allow the startup to build a good-quality RAG application while being cost-conscious and able to cater to customer needs?
A. Limit the number of relevant documents available for the RAG application to retrieve from
B. Pick a smaller LLM that is domain-specific
C. Limit the number of queries a customer can send per day
D. Use the largest LLM possible because that gives the best performance for any general queries
Answer: B
Question #10 (Topic: Exam A)
A Generative Al Engineer is responsible for developing a chatbot to enable their company’s 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.)
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.)
A. call_cust_history
B. maintenance_schedule
C. call_rep_history
D. call_detail
E. transcript Volume
Answer: DE
