最新なDatabricks Databricks-Generative-AI-Engineer-Associate問題集（63題）、真実試験の問題を全部にカバー！

質問 1：
A Generative AI Engineer is designing an LLM-powered live sports commentary platform. The platform provides real-time updates and LLM-generated analyses for any users who would like to have live summaries, rather than reading a series of potentially outdated news articles.
Which tool below will give the platform access to real-time data for generating game analyses based on the latest game scores?
A. DatabrickslQ
B. Feature Serving
C. AutoML
D. Foundation Model APIs
正解：B
解説: (Pass4Test メンバーにのみ表示されます)

質問 2：
A Generative AI Engineer is creating an agent-based LLM system for their favorite monster truck team. The system can answer text based questions about the monster truck team, lookup event dates via an API call, or query tables on the team's latest standings.
How could the Generative AI Engineer best design these capabilities into their system?
A. Write a system prompt for the agent listing available tools and bundle it into an agent system that runs a number of calls to solve a query.
B. Ingest PDF documents about the monster truck team into a vector store and query it in a RAG architecture.
C. Instruct the LLM to respond with "RAG", "API", or "TABLE" depending on the query, then use text parsing and conditional statements to resolve the query.
D. Build a system prompt with all possible event dates and table information in the system prompt. Use a RAG architecture to lookup generic text questions and otherwise leverage the information in the system prompt.
正解：A
解説: (Pass4Test メンバーにのみ表示されます)

質問 3：
A Generative Al Engineer is tasked with developing a RAG application that will help a small internal group of experts at their company answer specific questions, augmented by an internal knowledge base. They want the best possible quality in the answers, and neither latency nor throughput is a huge concern given that the user group is small and they're willing to wait for the best answer. The topics are sensitive in nature and the data is highly confidential and so, due to regulatory requirements, none of the information is allowed to be transmitted to third parties.
Which model meets all the Generative Al Engineer's needs in this situation?
A. BGE-large
B. OpenAI GPT-4
C. Llama2-70B
D. Dolly 1.5B
正解：A
解説: (Pass4Test メンバーにのみ表示されます)

質問 4：
A Generative Al Engineer at an automotive company would like to build a question-answering chatbot for customers to inquire about their vehicles. They have a database containing various documents of different vehicle makes, their hardware parts, and common maintenance information.
Which of the following components will NOT be useful in building such a chatbot?
A. Embedding model
B. Response-generating LLM
C. Vector database
D. Invite users to submit long, rather than concise, questions
正解：D
解説: (Pass4Test メンバーにのみ表示されます)

質問 5：
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 planningwhich 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. transcript Volume
B. call_cust_history
C. call_detail
D. maintenance_schedule
E. call_rep_history
正解：A,C
解説: (Pass4Test メンバーにのみ表示されます)

質問 6：
A Generative AI Engineer developed an LLM application using the provisioned throughput Foundation Model API. Now that the application is ready to be deployed, they realize their volume of requests are not sufficiently high enough to create their own provisioned throughput endpoint. They want to choose a strategy that ensures the best cost-effectiveness for their application.
What strategy should the Generative AI Engineer use?
A. Deploy the model using pay-per-token throughput as it comes with cost guarantees
B. Throttle the incoming batch of requests manually to avoid rate limiting issues
C. Switch to using External Models instead
D. Change to a model with a fewer number of parameters in order to reduce hardware constraint issues
正解：A
解説: (Pass4Test メンバーにのみ表示されます)