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

Question # 1

A small and cost-conscious startup in the cancer research field wants to build a RAG application using Foundation Model APIs. Which strategy would allow the startup to build a good-quality RAG application while being cost-conscious and able to cater to customer needs?

A. Limit the number of relevant documents available for the RAG application to retrieve from

B. Pick a smaller LLM that is domain-specific

C. Limit the number of queries a customer can send per day

D. Use the largest LLM possible because that gives the best performance for any general queries

Answer

B. Pick a smaller LLM that is domain-specific

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Question # 2

A Generative AI Engineer received the following business requirements for an external chatbot. The chatbot needs to know what types of questions the user asks and routes to appropriate models to answer the questions. For example, the user might ask about upcoming event details. Another user might ask about purchasing tickets for a particular event. What is an ideal workflow for such a chatbot?

A. The chatbot should only look at previous event information

B. There should be two different chatbots handling different types of user queries.

C. The chatbot should be implemented as a multi-step LLM workflow. First, identify the type of question asked, then route the question to the appropriate model. If it’s an upcoming event question, send the query to a text-to-SQL model. If it’s about ticket purchasing, the customer should be redirected to a payment platform.

D. The chatbot should only process payments

Answer

C. The chatbot should be implemented as a multi-step LLM workflow. First, identify the type of question asked, then route the question to the appropriate model. If it’s an upcoming event question, send the query to a text-to-SQL model. If it’s about ticket purchasing, the customer should be redirected to a payment platform.

Explanation:

Problem Context: The chatbot must handle various types of queries and intelligently route them to the appropriate responses or systems.

Explanation of Options:

Option A: Limiting the chatbot to only previous event information restricts its utility and does not meet the broader business requirements.

Option B: Having two separate chatbots could unnecessarily complicate user interaction and increase maintenance overhead.

Option C: Implementing a multi-step workflow where the chatbot first identifies the type of question and then routes it accordingly is the most efficient and scalable solution. This approach allows the chatbot to handle a variety of queries dynamically, improving user experience and operational efficiency.

Option D: Focusing solely on payments would not satisfy all the specified user interaction needs, such as inquiring about event details.

Option Coffers a comprehensive workflow that maximizes the chatbot’s utility and responsiveness to different user needs, aligning perfectly with the business requirements.

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Question # 3

A Generative AI Engineer is tasked with deploying an application that takes advantage of a custom MLflow Pyfunc model to return some interim results. How should they configure the endpoint to pass the secrets and credentials?

A. Use spark.conf.set ()

B. Pass variables using the Databricks Feature Store API

C. Add credentials using environment variables

D. Pass the secrets in plain text

Answer

C. Add credentials using environment variables

Explanation:

 Context: Deploying an application that uses an MLflow Pyfunc model involves managing sensitive information such as secrets and credentials securely.
 Explanation of Options:

Option A: Use spark.conf.set(): While this method can pass configurations within Spark jobs, using it for secrets is not recommended because it may expose them in logs or Spark UI.

Option B: Pass variables using the Databricks Feature Store API: The Feature Store API is designed for managing features for machine learning, not for handling secrets or credentials.

Option C: Add credentials using environment variables: This is a common practice for managing credentials in a secure manner, as environment variables can be accessed securely by applications without exposing them in the codebase.

Option D: Pass the secrets in plain text: This is highly insecure and not recommended, as it exposes sensitive information directly in the code.

Therefore,Option Cis the best method for securely passing secrets and credentials to an application, protecting them from exposure.

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Question # 4

A Generative Al Engineer is tasked with improving the RAG quality by addressing its inflammatory outputs. Which action would be most effective in mitigating the problem of offensive text outputs?

A. Increase the frequency of upstream data updates

B. Inform the user of the expected RAG behavior

C. Restrict access to the data sources to a limited number of users

D. Curate upstream data properly that includes manual review before it is fed into the RAG system

Answer

D. Curate upstream data properly that includes manual review before it is fed into the RAG system

Explanation:

Addressing offensive or inflammatory outputs in a Retrieval-Augmented Generation (RAG) system is critical for improving user experience and ensuring ethical AI deployment. Here's whyDis the most effective approach:

Manual data curation: The root cause of offensive outputs often comes from the underlying data used to train the model or populate the retrieval system. By manually curating the upstream data and conducting thorough reviews before the data is fed into the RAG system, the engineer can filter out harmful, offensive, or inappropriate content.

Improving data quality: Curating data ensures the system retrieves and generates responses from a high-quality, well-vetted dataset. This directly impacts the relevance and appropriateness of the outputs from the RAG system, preventing inflammatory content from being included in responses.

Effectiveness: This strategy directly tackles the problem at its source (the data) rather than just mitigating the consequences (such as informing users or restricting access). It ensures that the system consistently provides non-offensive, relevant information.

Other options, such as increasing the frequency of data updates or informing users about behavior expectations, may not directly mitigate the generation of inflammatory outputs.

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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.

Answer

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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