Databricks Databricks-Generative-AI-Engineer-Associate 問題集

質問 1：
A Generative Al Engineer is building a RAG application that answers questions about internal documents for the company SnoPen AI.
The source documents may contain a significant amount of irrelevant content, such as advertisements, sports news, or entertainment news, or content about other companies.
Which approach is advisable when building a RAG application to achieve this goal of filtering irrelevant information?
A. Include in the system prompt that the application is not supposed to answer any questions unrelated to SnoPen Al.
B. Include in the system prompt that any information it sees will be about SnoPenAI, even if no data filtering is performed.
C. Consolidate all SnoPen AI related documents into a single chunk in the vector database.
D. Keep all articles because the RAG application needs to understand non-company content to avoid answering questions about them.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 2：
A Generative Al Engineer is deciding between using LSH (Locality Sensitive Hashing) and HNSW (Hierarchical Navigable Small World) for indexing their vector database Their top priority is semantic accuracy Which approach should the Generative Al Engineer use to evaluate these two techniques?
A. Compare the cosine similarities of the embeddings of returned results against those of a representative sample of test inputs
B. Compare the Bilingual Evaluation Understudy (BLEU) scores of returned results for a representative sample of test inputs
C. Compare the Recall-Onented-Understudy for Gistmg Evaluation (ROUGE) scores of returned results for a representative sample of test inputs
D. Compare the Levenshtein distances of returned results against a representative sample of test inputs
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 3：
A Generative AI Engineer I using the code below to test setting up a vector store:

Assuming they intend to use Databricks managed embeddings with the default embedding model, what should be the next logical function call?
A. vsc.create_delta_sync_index()
B. vsc.create_direct_access_index()
C. vsc.similarity_search()
D. vsc.get_index()
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 4：
A Generative AI Engineer is designing a RAG application for answering user questions on technical regulations as they learn a new sport.
What are the steps needed to build this RAG application and deploy it?
A. User submits queries against an LLM -> Ingest documents from a source -> Index the documents and save to Vector Search -> LLM retrieves relevant documents -> LLM generates a response -> Evaluate model -> Deploy it using Model Serving
B. Ingest documents from a source -> Index the documents and saves to Vector Search -> User submits queries against an LLM -> LLM retrieves relevant documents -> Evaluate model -> LLM generates a response -> Deploy it using Model Serving
C. Ingest documents from a source -> Index the documents and save to Vector Search -> User submits queries against an LLM -> LLM retrieves relevant documents -> LLM generates a response -> Evaluate model -> Deploy it using Model Serving
D. Ingest documents from a source -> Index the documents and save to Vector Search -> Evaluate model -> Deploy it using Model Serving
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 5：
Which indicator should be considered to evaluate the safety of the LLM outputs when qualitatively assessing LLM responses for a translation use case?
A. The ability to generate responses in code
B. The accuracy and relevance of the responses
C. The latency of the response and the length of text generated
D. The similarity to the previous language
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 6：
A Generative AI Engineer at an automotive company would like to build a question-answering chatbot to help customers answer specific questions about their vehicles. They have:
A catalog with hundreds of thousands of cars manufactured since the 1960s Historical searches with user queries and successful matches Descriptions of their own cars in multiple languages They have already selected an open-source LLM and created a test set of user queries. They need to discard techniques that will not help them build the chatbot. Which do they discard?
A. Fine-tuning an embedding model on automotive terminology
B. Adding few-shot examples for response generation
C. Setting chunk size to match the model's context window to maximize coverage
D. Implementing metadata filtering based on car models and years
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 7：
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_detail
D. call_rep_history
E. transcript Volume
正解：C,E
解説: (Topexam メンバーにのみ表示されます)

質問 8：
A Generative Al Engineer wants their (inetuned LLMs in their prod Databncks workspace available for testing in their dev workspace as well. All of their workspaces are Unity Catalog enabled and they are currently logging their models into the Model Registry in MLflow.
What is the most cost-effective and secure option for the Generative Al Engineer to accomplish their gAi?
A. Setup a script to export the model from prod and import it to dev.
B. Use an external model registry which can be accessed from all workspaces
C. Setup a duplicate training pipeline in dev, so that an identical model is available in dev.
D. Use MLflow to log the model directly into Unity Catalog, and enable READ access in the dev workspace to the model.
正解：D
解説: (Topexam メンバーにのみ表示されます)