Master Databricks-Generative-AI-Engineer-Associate Exam with Reliable Practice Questions

Correct : D

To design an LLM-based application that can answer employee HR questions using HR PDF documentation, the most effective approach is option D. Here's why:

Chunking and Vector Store Embedding: HR documentation tends to be lengthy, so splitting it into smaller, manageable chunks helps optimize retrieval. These chunks are then embedded into a vector store (a database that stores vector representations of text). Each chunk of text is transformed into an embedding using a transformer-based model, which allows for efficient similarity-based retrieval.

Using Vector Search for Retrieval: When an employee asks a question, the system converts their query into an embedding as well. This embedding is then compared with the embeddings of the document chunks in the vector store. The most semantically similar chunks are retrieved, which ensures that the answer is based on the most relevant parts of the documentation.

LLM to Generate a Response: Once the relevant chunks are retrieved, these chunks are passed into the LLM, which uses them as context to generate a coherent and accurate response to the employee's question.

Why Other Options Are Less Suitable:

A (Calculate Averaged Embeddings): Averaging embeddings might dilute important information. It doesn't provide enough granularity to focus on specific sections of documents.

B (Summarize HR Documentation): Summarization loses the detail necessary for HR-related queries, which are often specific. It would likely miss the mark for more detailed inquiries.

C (Interaction Matrix and ALS): This approach is better suited for recommendation systems and not for HR queries, as it's focused on collaborative filtering rather than text-based retrieval.

Thus, option D is the most effective solution for providing precise and contextual answers based on HR documentation.

Correct : A

In a Retrieval-Augmented Generation (RAG) system, the key to providing relevant responses lies in the quality of the retrieved context. Here's why option A is the most appropriate solution:

Context Relevance: The RAG model generates answers based on retrieved documents or context. If the retrieved information is about an irrelevant product, it suggests that the retrieval step is failing to select the right context. The Generative AI Engineer must first assess the quality of what is being retrieved and ensure it is pertinent to the query.

Vector Search and Embedding Similarity: RAG typically uses vector search for retrieval, where embeddings of the query are matched against embeddings of product descriptions. Assessing the semantic similarity search process ensures that the closest matches are actually relevant to the query.

Fine-tuning the Retrieval Process: By improving the retrieval quality, such as tuning the embeddings or adjusting the retrieval strategy, the system can return more accurate and relevant product information.

Why Other Options Are Less Suitable:

B (Caching FAQs): Caching can speed up responses for frequently asked questions but won't improve the relevance of the retrieved content for less frequent or new queries.

C (Use a Different LLM): Changing the LLM only affects the generation step, not the retrieval process, which is the core issue here.

D (Different Semantic Search Algorithm): This could help, but the first step is to evaluate the current retrieval context before replacing the search algorithm.

Therefore, improving and assessing the quality of the retrieved context (option A) is the first step to fixing the issue of irrelevant product information.

Correct : A

In this scenario, the chatbot must avoid answering political questions and instead provide a standard message for such inquiries. Implementing a Safety Guardrail is the appropriate solution for this:

What is a Safety Guardrail? Safety guardrails are mechanisms implemented in Generative AI systems to ensure the model behaves within specific bounds. In this case, it ensures the chatbot does not answer politically sensitive or irrelevant questions, which aligns with the business rules.

Preventing Responses to Political Questions: The Safety Guardrail is programmed to detect specific types of inquiries (like political questions) and prevent the model from generating responses outside its intended domain. When such queries are detected, the guardrail intervenes and provides a pre-defined response: ''Sorry, I cannot answer that. I am a chatbot that can only answer questions around insurance.''

How It Works in Practice: The LLM system can include a classification layer or trigger rules based on specific keywords related to politics. When such terms are detected, the Safety Guardrail blocks the normal generation flow and responds with the fixed message.

Why Other Options Are Less Suitable:

B (Security Guardrail): This is more focused on protecting the system from security vulnerabilities or data breaches, not controlling the conversational focus.

C (Contextual Guardrail): While context guardrails can limit responses based on context, safety guardrails are specifically about ensuring the chatbot stays within a safe conversational scope.

D (Compliance Guardrail): Compliance guardrails are often related to legal and regulatory adherence, which is not directly relevant here.

Therefore, a Safety Guardrail is the right framework to ensure the chatbot only answers insurance-related queries and avoids political discussions.

Correct : B

Context: The Generative AI Engineer is setting up a vector store using Databricks' VectorSearchClient. This is typically done to enable fast and efficient retrieval of vectorized data for tasks like similarity searches.

Explanation of Options:

Option A: vsc.get_index(): This function would be used to retrieve an existing index, not create one, so it would not be the logical next step immediately after creating an endpoint.

Option B: vsc.create_delta_sync_index(): After setting up a vector store endpoint, creating an index is necessary to start populating and organizing the data. The create_delta_sync_index() function specifically creates an index that synchronizes with a Delta table, allowing automatic updates as the data changes. This is likely the most appropriate choice if the engineer plans to use dynamic data that is updated over time.

Option C: vsc.create_direct_access_index(): This function would create an index that directly accesses the data without synchronization. While also a valid approach, it's less likely to be the next logical step if the default setup (typically accommodating changes) is intended.

Option D: vsc.similarity_search(): This function would be used to perform searches on an existing index; however, an index needs to be created and populated with data before any search can be conducted.

Given the typical workflow in setting up a vector store, the next step after creating an endpoint is to establish an index, particularly one that synchronizes with ongoing data updates, hence Option B.

Correct : C

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 C is the best method for securely passing secrets and credentials to an application, protecting them from exposure.