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Databricks-Generative-AI-Engineer-Associate Exam Dumps

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Description

Exam Name: Databricks Certified Generative AI Engineer Associate
Exam Code: Databricks-Generative-AI-Engineer-Associate
Related Certification(s): Databricks Generative AI Engineer Associate Certification
Certification Provider: Databricks
Actual Exam Duration: 90 Minutes
Number of Databricks-Generative-AI-Engineer-Associate practice questions in our database:
Expected Databricks-Generative-AI-Engineer-Associate Exam Topics, as suggested by Databricks :

  • Module 1: Design Applications: The topic focuses on designing a prompt that elicits a specifically formatted response. It also focuses on selecting model tasks to accomplish a given business requirement. Lastly, the topic covers chain components for a desired model input and output.
  • Module 2: Data Preparation: Generative AI Engineers covers a chunking strategy for a given document structure and model constraints. The topic also focuses on filter extraneous content in source documents. Lastly, Generative AI Engineers also learn about extracting document content from provided source data and format.
  • Module 3: Application Development: In this topic, Generative AI Engineers learn about tools needed to extract data, Langchain/similar tools, and assessing responses to identify common issues. Moreover, the topic includes questions about adjusting an LLM’s response, LLM guardrails, and the best LLM based on the attributes of the application.
  • Module 4: Assembling and Deploying Applications: In this topic, Generative AI Engineers get knowledge about coding a chain using a pyfunc mode, coding a simple chain using langchain, and coding a simple chain according to requirements. Additionally, the topic focuses on basic elements needed to create a RAG application. Lastly, the topic addresses sub-topics about registering the model to Unity Catalog using MLflow.
  • Module 5: Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal/licensing requirements in this topic.
  • Module 6: Evaluation and Monitoring: This topic is all about selecting an LLM choice and key metrics. Moreover, Generative AI Engineers learn about evaluating model performance. Lastly, the topic includes sub-topics about inference logging and usage of Databricks features.

Description

Exam Name: Databricks Certified Generative AI Engineer Associate
Exam Code: Databricks-Generative-AI-Engineer-Associate
Related Certification(s): Databricks Generative AI Engineer Associate Certification
Certification Provider: Databricks
Actual Exam Duration: 90 Minutes
Number of Databricks-Generative-AI-Engineer-Associate practice questions in our database:
Expected Databricks-Generative-AI-Engineer-Associate Exam Topics, as suggested by Databricks :

  • Module 1: Design Applications: The topic focuses on designing a prompt that elicits a specifically formatted response. It also focuses on selecting model tasks to accomplish a given business requirement. Lastly, the topic covers chain components for a desired model input and output.
  • Module 2: Data Preparation: Generative AI Engineers covers a chunking strategy for a given document structure and model constraints. The topic also focuses on filter extraneous content in source documents. Lastly, Generative AI Engineers also learn about extracting document content from provided source data and format.
  • Module 3: Application Development: In this topic, Generative AI Engineers learn about tools needed to extract data, Langchain/similar tools, and assessing responses to identify common issues. Moreover, the topic includes questions about adjusting an LLM’s response, LLM guardrails, and the best LLM based on the attributes of the application.
  • Module 4: Assembling and Deploying Applications: In this topic, Generative AI Engineers get knowledge about coding a chain using a pyfunc mode, coding a simple chain using langchain, and coding a simple chain according to requirements. Additionally, the topic focuses on basic elements needed to create a RAG application. Lastly, the topic addresses sub-topics about registering the model to Unity Catalog using MLflow.
  • Module 5: Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal/licensing requirements in this topic.
  • Module 6: Evaluation and Monitoring: This topic is all about selecting an LLM choice and key metrics. Moreover, Generative AI Engineers learn about evaluating model performance. Lastly, the topic includes sub-topics about inference logging and usage of Databricks features.

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Q1. 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.)

A.call_cust_history

B. maintenance_schedule

C. call_rep_history

D. call_detail

E. transcript Volume

Correct Answer: D, E

Q2. A Generative AI Engineer just deployed an LLM application at a digital marketing company that assists with answering customer service inquiries. Which metric should they monitor for their customer service LLM application in production?

A.Number of customer inquiries processed per unit of time

B. Energy usage per query

C. Final perplexity scores for the training of the model

D. HuggingFace Leaderboard values for the base LLM

Correct Answer: A

Q3. 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.)

A.call_cust_history

B. maintenance_schedule

C. call_rep_history

D. call_detail

E. transcript Volume

Correct Answer: D, E

Q4. A Generative AI Engineer just deployed an LLM application at a digital marketing company that assists with answering customer service inquiries. Which metric should they monitor for their customer service LLM application in production?

A.Number of customer inquiries processed per unit of time

B. Energy usage per query

C. Final perplexity scores for the training of the model

D. HuggingFace Leaderboard values for the base LLM

Correct Answer: A

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