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Databricks Certified Generative AI Engineer Associate Exam - Topic 4 Question 23 Discussion

Actual exam question for Databricks's Databricks Certified Generative AI Engineer Associate exam
Question #: 23
Topic #: 4
[All Databricks Certified Generative AI Engineer Associate Questions]

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.)

Show Suggested Answer Hide Answer
Suggested Answer: D, E

In the context of developing a chatbot for a company's internal HelpDesk Call Center, the key is to select data sources that provide the most contextual and detailed information about the issues being addressed. This includes identifying the root cause and suggesting resolutions. The two most appropriate sources from the list are:

Call Detail (Option D):

Contents: This Delta table includes a snapshot of all call details updated hourly, featuring essential fields like root_cause and resolution.

Relevance: The inclusion of root_cause and resolution fields makes this source particularly valuable, as it directly contains the information necessary to understand and resolve the issues discussed in the calls. Even if some records are incomplete, the data provided is crucial for a chatbot aimed at speeding up resolution identification.

Transcript Volume (Option E):

Contents: This Unity Catalog Volume contains recordings in .wav format and text transcripts in .txt files.

Relevance: The text transcripts of call recordings can provide in-depth context that the chatbot can analyze to understand the nuances of each issue. The chatbot can use natural language processing techniques to extract themes, identify problems, and suggest resolutions based on previous similar interactions documented in the transcripts.

Why Other Options Are Less Suitable:

A (Call Cust History): While it provides insights into customer interactions with the HelpDesk, it focuses more on the usage metrics rather than the content of the calls or the issues discussed.

B (Maintenance Schedule): This data is useful for understanding when services may not be available but does not contribute directly to resolving user issues or identifying root causes.

C (Call Rep History): Though it offers data on call durations and start times, which could help in assessing performance, it lacks direct information on the issues being resolved.

Therefore, Call Detail and Transcript Volume are the most relevant data sources for a chatbot designed to assist with identifying and resolving issues in a HelpDesk Call Center setting, as they provide direct and contextual information related to customer issues.


by Sheridan at Nov 23, 2025, 02:44 PM

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Romana
9 hours ago
call_rep_history could help too, but not as much as the others.
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Noe
6 days ago
Totally agree, call_detail has the root cause info we need!
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Ma
11 days ago
I think call_detail and transcript Volume are the best choices.
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Remona
16 days ago
Haha, I bet the maintenance schedule table is full of "user error" as the root cause! D and E are the way to go.
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Louvenia
21 days ago
D and E for sure. The call detail table and transcript volume are the most useful data sources to provide the necessary context for this application.
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Justine
26 days ago
I'd go with C and D. The call rep history and call detail tables have the information needed to determine the root cause and resolution for the tickets.
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Bo
1 month ago
I would lean towards call_detail and transcript Volume because they both seem to provide direct insights into the calls and their outcomes, but I could be wrong.
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Regenia
1 month ago
I feel like the maintenance_schedule could be relevant too, especially if outages affect ticket resolution, but I’m torn between that and call_rep_history.
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Albert
1 month ago
I remember practicing a similar question, and I think the transcript Volume might help provide context from the conversations, but I’m not completely confident.
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Mabelle
2 months ago
I think the call_detail table could be really useful since it has root_cause and resolution fields, but I'm not sure about the second source.
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Juan
2 months ago
I'm feeling pretty confident about this one. The call_detail and transcript Volume sources seem like the clear choices to provide the most relevant information for the chatbot application.
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Hailey
2 months ago
Okay, I think I have a strategy here. The call_detail table sounds like it could be really useful, since it includes the root_cause and resolution fields. And the transcript Volume could also add helpful context.
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Aleisha
2 months ago
I agree with D and E. The call detail table and transcript volume are the most relevant data sources to help identify the root cause and resolution for the tickets.
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Antione
2 months ago
I think D and E are the best choices. They provide context and details.
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Carole
2 months ago
D and E seem like the best options to me. The call detail table has the root cause and resolution fields, and the transcript volume could provide additional context to identify the ticket root cause.
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Renay
3 months ago
I'm a little unsure about the maintenance_schedule table. Could that also be relevant for understanding ticket root cause and resolution? I'll need to think that one through.
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Deeann
3 months ago
Wait, why would maintenance_schedule be useful here?
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Trina
3 months ago
Hmm, I'm a bit confused by the different data sources. I'll need to read through them carefully to understand which ones would be most helpful for the chatbot application.
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Myra
3 months ago
This seems like a tricky question. I'll need to carefully consider the data sources and how they could provide context for identifying ticket root cause and resolution.
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