Free Databricks Databricks-Certified-Generative-AI-Engineer-Associate Exam Questions
Absolute Free Databricks-Certified-Generative-AI-Engineer-Associate Exam Practice for Comprehensive Preparation
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Databricks Databricks-Certified-Generative-AI-Engineer-Associate Exam Questions
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Provided By: Databricks
- Exam: Databricks Certified Generative AI Engineer Associate
- Certification: Databricks Certified Associate
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Total Questions: 150
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Updated On: Jan 09, 2025
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Rated: 4.9 |
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Online Users: 300
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Question 1
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What is a key challenge when implementing reranking in a Retrieval-Augmented Generation (RAG) pipeline for a Gen AI application?
Answer: D
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What is a key challenge when implementing reranking in a Retrieval-Augmented Generation (RAG) pipeline for a Gen AI application?
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Question 2
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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.)
Answer: D,E
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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.)
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Question 3
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A Generative AI Engineer is creating an LLM-powered application that will need access to up-to-date
news articles and stock prices.
The design requires the use of stock prices which are stored in Delta tables and finding the latest
relevant news articles by searching the internet.
How should the Generative AI Engineer architect their LLM system?
Answer: D
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A Generative AI Engineer is creating an LLM-powered application that will need access to up-to-date
news articles and stock prices.
The design requires the use of stock prices which are stored in Delta tables and finding the latest
relevant news articles by searching the internet.
How should the Generative AI Engineer architect their LLM system?
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Question 4
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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.)
Answer: D,E
-
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.)
-
Question 5
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A Generative AI Engineer is creating an LLM-powered application that will need access to up-to-date
news articles and stock prices.
The design requires the use of stock prices which are stored in Delta tables and finding the latest
relevant news articles by searching the internet.
How should the Generative AI Engineer architect their LLM system?
Answer: D
-
A Generative AI Engineer is creating an LLM-powered application that will need access to up-to-date
news articles and stock prices.
The design requires the use of stock prices which are stored in Delta tables and finding the latest
relevant news articles by searching the internet.
How should the Generative AI Engineer architect their LLM system?