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Databricks Databricks-Generative-AI-Engineer-Associate Exam Sample Questions


Question # 1

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?

A. Use an LLM to summarize the latest news articles and lookup stock tickers from the summaries to find stock prices.
B. Query the Delta table for volatile stock prices and use an LLM to generate a search query to investigate potential causes of the stock volatility.
C. Download and store news articles and stock price information in a vector store. Use a RAG architecture to retrieve and generate at runtime.
D. Create an agent with tools for SQL querying of Delta tables and web searching, provide retrieved values to an LLM for generation of response.


D. Create an agent with tools for SQL querying of Delta tables and web searching, provide retrieved values to an LLM for generation of response.

Explanation:

To build an LLM-powered system that accesses up-to-date news articles and stock prices, the best approach is tocreate an agentthat has access to specific tools (option D).

Agent with SQL and Web Search Capabilities:By using an agent-based architecture, the LLM can interact with external tools. The agent can query Delta tables (for up-to-date stock prices) via SQL and perform web searches to retrieve the latest news articles. This modular approach ensures the system can access both structured (stock prices) and unstructured (news) data sources dynamically.

Why This Approach Works:

SQL Queries for Stock Prices: Delta tables store stock prices, which the agent can query directly for the latest data.

Web Search for News: For news articles, the agent can generate search queries and retrieve the most relevant and recent articles, then pass them to the LLM for processing.

Why Other Options Are Less Suitable:

A (Summarizing News for Stock Prices): This convoluted approach would not ensure accuracy when retrieving stock prices, which are already structured and stored in Delta tables.

B (Stock Price Volatility Queries): While this could retrieve relevant information, it doesn't address how to obtain the most up-to-date news articles.

C (Vector Store): Storing news articles and stock prices in a vector store might not capture the real-time nature of stock data and news updates, as it relies on pre-existing data rather than dynamic querying.

Thus, using an agent with access to both SQL for querying stock prices and web search for retrieving news articles is the best approach for ensuring up-to-date and accurate responses.





Question # 2

A Generative Al Engineer is responsible for developing a chat bot 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


D. call_detail
E. transcript Volume




Question # 3

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. Switch to using External Models instead
B. Deploy the model using pay-per-token throughput as it comes with cost guarantees
C. Change to a model with a fewer number of parameters in order to reduce hardware constraint issues
D. Throttle the incoming batch of requests manually to avoid rate limiting issues


B. Deploy the model using pay-per-token throughput as it comes with cost guarantees

Explanation:

Problem Context: The engineer needs a cost-effective deployment strategy for an LLM application with relatively low request volume.

Explanation of Options:

Option A: Switching to external models may not provide the required control or integration necessary for specific application needs.

Option B: Using a pay-per-token model is cost-effective, especially for applications with variable or low request volumes, as it aligns costs directly with usage.

Option C: Changing to a model with fewer parameters could reduce costs, but might also impact the performance and capabilities of the application.

Option D: Manually throttling requests is a less efficient and potentially error-prone strategy for managing costs.

OptionBis ideal, offering flexibility and cost control, aligning expenses directly with the application's usage patterns.




Question # 4

A Generative Al Engineer has developed an LLM application to answer questions about internal company policies. The Generative AI Engineer must ensure that the application doesn’t hallucinate or leak confidential data. Which approach should NOT be used to mitigate hallucination or confidential data leakage?

A. Add guardrails to filter outputs from the LLM before it is shown to the user
B. Fine-tune the model on your data, hoping it will learn what is appropriate and not
C. Limit the data available based on the user’s access level
D. Use a strong system prompt to ensure the model aligns with your needs.


B. Fine-tune the model on your data, hoping it will learn what is appropriate and not

Explanation:

When addressing concerns of hallucination and data leakage in an LLM application for internal company policies, fine-tuning the model on internal data with the hope it learns data boundaries can be problematic:

Risk of Data Leakage: Fine-tuning on sensitive or confidential data does not guarantee that the model will not inadvertently include or reference this data in its outputs. There’s a risk of overfitting to the specific data details, which might lead to unintended leakage.

Hallucination: Fine-tuning does not necessarily mitigate the model's tendency to hallucinate; in fact, it might exacerbate it if the training data is not comprehensive or representative of all potential queries.

Better Approaches:

A,C, andDinvolve setting up operational safeguards and constraints that directly address data leakage and ensure responses are aligned with specific user needs and security levels.

Fine-tuning lacks the targeted control needed for such sensitive applications and can introduce new risks, making it an unsuitable approach in this context.





Question # 5

A Generative Al Engineer interfaces with an LLM with prompt/response behavior that has been trained on customer calls inquiring about product availability. The LLM is designed to output “In Stock” if the product is available or only the term “Out of Stock” if not. Which prompt will work to allow the engineer to respond to call classification labels correctly?
A. Respond with “In Stock” if the customer asks for a product.
B. You will be given a customer call transcript where the customer asks about product availability. The outputs are either “In Stock” or “Out of Stock”. Format the output in JSON, for example: {“call_id”: “123”, “label”: “In Stock”}.
C. Respond with “Out of Stock” if the customer asks for a product.
D. You will be given a customer call transcript where the customer inquires about product availability. Respond with “In Stock” if the product is available or “Out of Stock” if not.


B. You will be given a customer call transcript where the customer asks about product availability. The outputs are either “In Stock” or “Out of Stock”. Format the output in JSON, for example: {“call_id”: “123”, “label”: “In Stock”}.



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