Databricks Certified Generative AI Engineer Associate Exam Dumps & Practice Test Questions

Question 1:

A Generative AI Engineer is working on a Retrieval-Augmented Generation (RAG) system that responds to queries about a fantasy novel series. The novels have been split into chunks and embedded into a vector store, with metadata like chapter and page numbers. Currently, the chunking setup is based on the engineer's intuition. To improve the system's accuracy and relevance, the engineer wants to refine the chunking method and its parameters. 

Which two of the following strategies should be applied for systematic optimization? (Select two.)

A. Test various embedding models and assess how they influence response quality.
B. Build a query classifier to identify the correct book for each query and use it to filter retrieval results.
C. Choose evaluation metrics (e.g., recall or NDCG) and experiment with chunking formats such as chapters or paragraphs, then optimize based on metric outcomes.
D. Ask the LLM to suggest a suitable token count for chunking using sample questions and answers, then average the suggested sizes.
E. Use an LLM-generated score to rate how well each chunk answers a query, and base chunking adjustments on this score.

Correct Answers: B, C

Explanation:

To enhance the chunking strategy in a RAG application, it is critical to adopt a structured and data-driven approach rather than relying on intuition. The two most effective strategies from the options provided are B and C.

Option B: Query Classifier for Book Selection
Incorporating a query classifier allows the system to intelligently filter which book(s) should be considered during retrieval. For instance, in a multi-book series, not every query applies to all books. A classifier trained to map user queries to a particular title or subset of titles significantly reduces noise in the retrieval process. This makes retrieval more efficient by eliminating irrelevant content early, leading to more accurate and contextually appropriate responses. Especially in large corpora, narrowing the scope of search leads to better latency and improved answer relevance.

Option C: Use Metrics to Guide Chunking Strategy
Using established metrics like Recall, which measures how many relevant items are retrieved, or NDCG (Normalized Discounted Cumulative Gain), which assesses the quality of ranked retrievals, offers a scientific way to evaluate different chunking strategies. For example, testing paragraph-based vs. chapter-based chunking provides insight into which structure best supports query-answering performance. By evaluating actual system outputs against expected behavior using these metrics, the engineer can make informed adjustments that improve overall accuracy and utility.

Why the Other Options Are Less Effective:

• Option A deals with experimenting on embedding models, which, while useful for general performance tuning, doesn’t directly help refine how the documents are chunked.

• Option D suggests relying on an LLM’s heuristic guess for chunk sizes. This is more speculative than scientific, offering little control over actual performance outcomes.

• Option E involves using the LLM to rate chunk relevance, which introduces subjectivity and inconsistency, as LLM evaluations can be biased or lack transparency compared to standard metrics.

Adopting B and C equips the engineer with analytical tools and targeted strategies that optimize both performance and user experience in the RAG pipeline.

Question 2:

An AI Engineer is creating a Retrieval-Augmented Generation (RAG) system to help users ask questions about rules and regulations of a sport they’re learning. 

What is the correct sequence of steps to build and deploy this RAG system?

A. Import documents → Save vectors → Accept user query → Use LLM to retrieve documents → Evaluate system → Generate response → Deploy the application.
B. Import documents → Index and store vectors → Accept user query → Retrieve documents via LLM → Generate response → Evaluate → Deploy with model serving.
C. Import documents → Store in vector database → Evaluate model → Deploy using model serving.
D. User submits query → Import documents → Store vectors → Retrieve documents → Generate response → Evaluate model → Deploy system.

Correct Answer: B

Explanation:

Building a RAG application involves a clear and logical progression of stages to ensure data is effectively processed, queries are accurately handled, and the model is ready for real-world deployment. The only option that captures the correct workflow is Option B, as it follows a coherent and complete data-to-deployment pipeline.

Step 1: Ingest Documents
First, source documents containing relevant regulatory or instructional content need to be collected. These could be rulebooks, manuals, or structured documentation relating to the sport in question.

Step 2: Index and Store in Vector Database
After ingestion, the documents are preprocessed and transformed into vector embeddings using an embedding model. These vectors, representing semantic meaning, are stored in a vector database (e.g., FAISS, Pinecone, or Elasticsearch with vector capabilities), enabling efficient semantic search later.

Step 3: Accept User Query
Users interact with the system by submitting natural language questions. These queries are then embedded and compared against stored vectors to find semantically similar chunks of content.

Step 4: Retrieve Relevant Documents
The RAG framework uses the vector index to fetch top-matching documents relevant to the query. These serve as contextual inputs for the LLM to understand and answer questions accurately.

Step 5: Generate a Response
The language model combines the user’s query with the retrieved documents and generates a context-aware response. This is the generative part of RAG.

Step 6: Model Evaluation
Before going live, it is crucial to evaluate the model using metrics such as precision, recall, or F1-score, ensuring the system performs well under various real-world scenarios.

Step 7: Deploy the Model
Once validated, the complete application is deployed using a model serving infrastructure. This can be done via APIs, cloud services, or frameworks like FastAPI, TensorFlow Serving, or MLflow.

Why Other Options Are Incorrect:

• Option A has a logical misordering—placing model evaluation before response generation.

• Option C skips important stages like query processing and response generation.

• Option D incorrectly places document ingestion after user queries, which contradicts the need for pre-indexed data.

Thus, Option B is the only option that outlines the RAG pipeline in the correct sequence from ingestion to live deployment.

Question 3:

A Generative AI Engineer has launched a customer service application that utilizes a Large Language Model (LLM) to respond to user inquiries. 

To ensure the application is functioning effectively in a production setting, which metric should be prioritized to assess its performance?

A. Number of customer inquiries processed per unit of time
B. Energy usage per query
C. Final perplexity scores from the model’s training phase
D. HuggingFace Leaderboard values for the base LLM

Correct Answer: A

Explanation:

In a production environment where a Large Language Model (LLM) is deployed to automate customer service interactions, it is essential to evaluate its real-time operational performance. The core concern here is ensuring that the system is capable of handling user inquiries promptly and efficiently.

Option A, "Number of customer inquiries processed per unit of time," is the most effective metric in this scenario. This value reflects the system’s throughput—how many customer requests it can process within a given period. High throughput indicates that the model is capable of responding to multiple users quickly, ensuring that service levels are maintained during periods of peak traffic. Monitoring this metric allows engineers to assess whether the infrastructure is capable of scaling and responding appropriately under different workloads. It also helps in identifying latency bottlenecks and system degradation over time.

Option B, "Energy usage per query," while relevant in contexts such as sustainability reporting or operational cost analysis, is not the most immediate concern when monitoring performance in a customer-facing production environment. Although optimizing energy usage is beneficial, it does not directly indicate whether customers are being served effectively.

Option C, "Final perplexity scores from the model’s training phase," is a measure of how well the model was trained, particularly how it predicts the next token in a sequence. While perplexity is a useful evaluation metric during model development, it becomes less relevant once the model is deployed. Production performance is more dependent on response speed, reliability, and usability rather than on training-phase metrics.

Option D, "HuggingFace Leaderboard values for the base LLM," pertains to the model’s general performance in standardized benchmark tests. While useful in choosing a model during initial development, these scores are static and do not reflect the model’s behavior or effectiveness once deployed with real-world data and tasks.

In summary, Option A is the most suitable and actionable metric to monitor when running an LLM-powered application in production. It provides a direct measurement of system efficiency and responsiveness, which are critical for customer service environments where delays or failures can impact user satisfaction and business outcomes.

Question 4:

A Generative AI Engineer is building a recommendation system to assign the most appropriate team member to newly defined projects. The match should be based on both the availability of the team member and how well their profile aligns with the unstructured text in the project scope. 

Which approach offers the best system design for this requirement?

A. Create a tool to check availability and embed all project scopes into a vector store. Use team member profiles to query and retrieve matching projects.
B. Develop a system that checks availability and uses an LLM to extract keywords from project scopes, then match those keywords against team member profiles.
C. Build a system that identifies available members, calculates a similarity score between profiles and project scopes, and ranks matches accordingly.
D. Design a solution that checks team member availability, embeds their profiles into a vector store, and retrieves matches using the project scope and filters.

Correct Answer: D

Explanation:

To effectively match team members with project opportunities, the system must satisfy two conditions: determine availability and assess relevance based on unstructured textual data in employee profiles and project scopes. Therefore, the design should incorporate both filtering for availability and semantic matching for relevance.

Option D offers the most comprehensive solution. This architecture involves embedding the team member profiles into a vector store, enabling advanced similarity search through vector-based retrieval. When a new project is initiated, its scope can be used as the query input against this vector store. By applying availability filters, the system ensures only currently unassigned team members are considered, optimizing the assignment process. This method takes advantage of semantic embeddings, allowing for nuanced and context-aware matching between the descriptive content of employee profiles and the nature of the project tasks.

Option A, while using embeddings correctly for the project scopes, reverses the query direction in a less efficient manner. Querying with profiles against embedded project scopes is not optimal in a many-to-one recommendation task, where a single project must find its best candidate.

Option B uses keyword extraction, which is a simplistic and often brittle technique. Keyword matching may miss context, synonyms, and deeper semantic understanding, making it less suitable for unstructured, complex data where nuanced skill alignment is needed.

Option C proposes calculating similarity scores, which is conceptually sound, but it does not specify how to efficiently store and retrieve the data. Without vector storage or semantic indexing, this solution could suffer in scalability and performance as the number of team members or projects grows.

In conclusion, Option D provides the most scalable, accurate, and efficient solution. By leveraging embeddings, a vector store, and filtering mechanisms, this approach supports intelligent matching while maintaining operational constraints like availability, ensuring team members are optimally assigned to projects that align with their capabilities and timing.

Question 5:

A Generative AI Engineer is designing a real-time sports commentary system powered by a large language model (LLM). The system needs to generate immediate, insightful summaries and analyses of ongoing games using the most current game data, such as player stats and live scores. 

What solution should the engineer use to ensure the LLM receives and utilizes fresh, low-latency data during inference?

A. DatabricksIQ
B. Foundation Model APIs
C. Feature Serving
D. AutoML

Correct Answer: C

Explanation:

For an application that delivers real-time commentary powered by LLMs, it’s essential that the model receives up-to-date data, such as the latest scores, key plays, or player performance metrics. This type of time-sensitive system must be designed to push relevant features to the model as close to the moment of inference as possible. That’s where Feature Serving plays a pivotal role.

Feature Serving refers to the process of delivering preprocessed features—data points that have been cleaned, transformed, or derived—to machine learning models in real time. This system ensures that relevant context (e.g., the current score, game time, or recent player stats) is available when the model generates output. In LLM-driven platforms like live sports commentary applications, where accuracy and timeliness are critical, Feature Serving acts as the bridge between raw, dynamic data and the model’s real-time generation capability.

By integrating Feature Serving, the commentary engine can enhance its outputs with the most recent game developments, providing users with high-value insights that reflect the current state of the match—not outdated snapshots. This makes it especially valuable for streaming applications, dashboards, and alerts, where each second of delay or outdated content can reduce user trust.

Let’s look at why the other options are not suitable:

• A. DatabricksIQ: This tool is more aligned with analytics, business intelligence, and AI experimentation—not real-time feature delivery.

• B. Foundation Model APIs: While these APIs allow access to LLMs, they don’t handle dynamic data feeding at inference time. They work well for static or semi-structured inputs, but not for rapid updates from a live event stream.

• D. AutoML: AutoML helps with automating the training and tuning of models, including feature selection, but it is not responsible for providing real-time data during inference.

To summarize, Feature Serving is the most suitable tool to support real-time use cases like live sports commentary, where streaming fresh contextual data into an LLM can dramatically improve the quality and relevance of its generated content.

Question 6:

An AI Engineer is managing a Retrieval-Augmented Generation (RAG) system deployed on a Databricks provisioned throughput serving endpoint. Currently, a custom-built microservice logs all requests sent to the model and the responses it returns. 

To reduce complexity, which built-in Databricks feature can replace this logging service and automatically track these interactions for observability and debugging?

A. Vector Search
B. Lakeview
C. DBSQL
D. Inference Tables

Correct Answer: D

Explanation:

In high-performing machine learning systems like Retrieval-Augmented Generation (RAG) applications, observability and transparency of model interactions are essential. Engineers need to log both incoming requests and generated responses to evaluate model behavior, identify errors, improve prompt strategies, and ensure compliance. Instead of relying on a custom-built logging microservice, Databricks offers a native solution: Inference Tables.

Inference Tables are a built-in feature provided by Databricks specifically for use with provisioned throughput model serving endpoints. When enabled, they automatically log detailed records of every model invocation—including input data, output results, metadata like timestamps, latency, status codes, and more. This structured logging is stored in Delta format, making it fully compatible with Databricks notebooks, dashboards, and SQL analytics tools.

With Inference Tables, organizations can:

• Audit model usage to ensure security and compliance.

• Monitor system performance (e.g., response latency, error rates).
  
  

• Analyze model inputs and outputs to fine-tune prompts or retrieval strategies in RAG pipelines.

• Eliminate the overhead of maintaining a separate logging service, thereby simplifying system architecture.

Now let’s evaluate the incorrect options:

• A. Vector Search: While this is useful for indexing and retrieving documents or embeddings, it doesn’t capture request/response data for model endpoints.

• B. Lakeview: This is Databricks’ tool for creating visual dashboards. Although helpful for displaying results, it doesn’t handle backend logging or request tracking.

• C. DBSQL: Databricks SQL is excellent for querying structured data in Delta tables but does not log or observe interactions between users and models.

In essence, Inference Tables are purpose-built to enhance the observability of AI applications on Databricks. They capture detailed telemetry without extra code or infrastructure, making them the best choice for maintaining transparency and performance in RAG systems.

Question 7:

A Generative AI Engineer is troubleshooting a Retrieval-Augmented Generation (RAG) system that has started producing harmful or offensive outputs. These inappropriate responses are damaging the trustworthiness and user experience of the system. 

To address the root cause and enhance the safety of generated responses, which strategy would be the most effective to implement?

A. Increase the frequency of upstream data updates
B. Inform users about how the RAG system behaves
C. Restrict access to the data sources to a limited number of users
D. Properly curate upstream data, including manual review, before feeding it into the RAG system

Correct Answer: D

Explanation:

When managing a Retrieval-Augmented Generation (RAG) system, the quality of generated content is deeply tied to the reliability and tone of the underlying retrieved context. Since the model's outputs are influenced by the data it retrieves from vector databases or document repositories, unfiltered or harmful data can lead to inappropriate and offensive generations. To address this, curating the upstream data through manual review and moderation before it enters the system becomes the most reliable defense against problematic content.

In RAG systems, documents are typically embedded into a vector database, and these embeddings are later retrieved based on user queries. If the documents being ingested contain biased, misleading, or offensive content, even a well-trained large language model (LLM) can inadvertently echo or reinforce that negativity. By curating this data manually—reviewing and filtering out inappropriate materials—you ensure that the LLM is influenced only by vetted, high-quality content.

Looking at the other options:

• Option A (Increase data update frequency) may improve content freshness but does nothing to filter or assess the quality of the input data. Offensive content would simply be refreshed more often, not resolved.

• Option B (Inform users about system behavior) emphasizes transparency but doesn't solve the core issue. Educating users might help set expectations, but it doesn’t eliminate the possibility of generating harmful outputs.

• Option C (Restrict data access) reduces who can view or use the data but still allows harmful data to exist in the system and be retrieved. It doesn’t mitigate the actual risk during generation.

In contrast, Option D addresses the foundational issue—preventing harmful information from being indexed or embedded at all. Curating content ensures that the system generates safer, more responsible outputs, directly impacting user trust and system reliability. In safety-critical applications like customer service, healthcare, or education, this proactive data hygiene is essential to maintaining ethical and functional AI systems.

Question 8:

An AI Engineer is developing a RAG-based application that uses a Large Language Model (LLM). The retriever splits documents into 512-token chunks. The engineer needs to select a model configuration that prioritizes minimal latency and low operational cost, rather than the highest possible output quality. 

Which configuration best fits these constraints?

A. Context length: 514 tokens; Model size: 0.44GB; Embedding dimension: 768
B. Context length: 2048 tokens; Model size: 11GB; Embedding dimension: 2560
C. Context length: 32,768 tokens; Model size: 14GB; Embedding dimension: 4096
D. Context length: 512 tokens; Model size: 0.13GB; Embedding dimension: 384

Correct Answer: D

Explanation:

In designing a cost-efficient and low-latency Retrieval-Augmented Generation (RAG) application, selecting the right model architecture is crucial—especially when token chunking and computational limitations are involved. Here, the documents have been pre-processed into chunks of 512 tokens, so there's no advantage to choosing a model that supports much longer context windows. In fact, longer context lengths can result in unnecessary resource consumption.

Option D—with a 512-token context length, a compact model size of 0.13GB, and an embedding dimension of 384—is the most suitable configuration. It perfectly aligns with the chunk size, ensuring the model can handle each chunk in a single pass. The small model footprint drastically reduces memory usage and improves inference speed, which is particularly advantageous in real-time applications or environments where hardware resources are limited.

While the embedding dimension of 384 may seem lower compared to other options, the engineer has explicitly deprioritized top-tier quality. Lower-dimensional embeddings are generally faster to compute and retrieve, providing adequate performance for many general-use cases.

In contrast:

• Option A uses a context length of 514, which is slightly over the chunk size, and its model size is significantly larger at 0.44GB. While still small, this option adds unnecessary overhead without performance gains in this specific scenario.

• Option B offers a 2048-token context and a much larger 11GB model with an embedding dimension of 2560. This results in higher cost and latency, making it unsuitable when performance and responsiveness matter more than rich outputs.

• Option C has an extremely large context window (32,768 tokens) and a massive 14GB model with a 4096-dimensional embedding vector. This is designed for complex use cases involving long documents or deep reasoning—not for compact, fast, and budget-conscious deployments.

Ultimately, Option D offers the most efficient balance of compatibility, speed, and cost-effectiveness. It meets the engineer's exact needs by optimizing resource usage and ensuring fast query processing without investing in more computational power than necessary.

Question 9:

A small startup specializing in cancer research wants to develop a Retrieval-Augmented Generation (RAG) application by leveraging Foundation Model APIs. Their priority is to keep costs low while delivering accurate and reliable answers to support both research efforts and customer inquiries. 

Given their limited budget, which approach best balances cost-efficiency with quality in this scenario?

A. Reduce the number of documents the RAG system can access for retrieval
B. Select a smaller, domain-specific large language model (LLM) tailored to their field
C. Limit the daily number of user queries to control expenses
D. Opt for the largest available general-purpose LLM to maximize performance

Correct Answer: B

Explanation:

When building a Retrieval-Augmented Generation (RAG) application, especially in a niche and complex domain such as cancer research, startups must carefully balance the trade-off between model performance and operational costs. This is particularly important for startups with limited financial and computational resources.

Choosing a smaller, domain-specific large language model (LLM) is the most practical and effective strategy here. Domain-specific models are fine-tuned on data directly related to the startup’s field, such as biomedical literature or cancer-related studies. This specialization allows the model to generate more relevant, accurate, and context-aware responses despite having fewer parameters compared to large, general-purpose LLMs. Because these models are smaller, they require less computational power and lower inference costs, making them ideal for startups mindful of their budgets.

Limiting the document pool (Option A) might save some processing time or costs, but it comes at the expense of reduced knowledge coverage and accuracy, weakening the overall quality of the generated answers. This undermines the core benefit of a RAG system, which relies on retrieving diverse and relevant documents.

Restricting the number of user queries (Option C) could reduce expenses but severely impacts user experience and accessibility—critical factors when the application is intended for research and customer support.

Using the largest general-purpose LLM (Option D) guarantees high raw performance but often results in exorbitant costs and latency that are unsustainable for a startup. Furthermore, the general model may not perform as well on highly specialized biomedical queries compared to a tailored model.

Therefore, choosing a smaller, domain-specific LLM enables the startup to maintain high-quality, relevant responses while optimizing costs and infrastructure use. This approach aligns perfectly with both technical needs and budget constraints, fostering a cost-effective, scalable solution.

Question 10:

Which two datasets will best help the chatbot provide accurate root cause identification and resolution suggestions?

A. call_cust_history
B. maintenance_schedule
C. call_rep_history
D. call_detail
E. transcript_volume

Correct Answers: D and E

Explanation:

For a chatbot designed to aid HelpDesk operations by diagnosing ticket root causes and proposing resolutions, the most valuable data sources are those that provide rich, relevant, and contextual information about past incidents and conversations.

The call_detail (Delta Table) is crucial because it contains structured information such as root causes and resolutions directly related to past support calls. Although some ongoing calls may have incomplete data, historical records offer a wealth of verified incident details that the chatbot can use to accurately ground its answers. This data provides a solid factual basis to help the AI system recommend proven solutions and diagnose recurring issues effectively.

On the other hand, the transcript_volume (Unity Catalog Volume) is indispensable for capturing the unstructured, conversational context of real calls. These transcripts and audio files allow the chatbot to learn from natural language interactions between support agents and customers. By embedding this unstructured data into a vector search index, the system can quickly retrieve relevant dialogues that mirror user inquiries, enabling human-like, context-aware responses. This is a key strength of Retrieval-Augmented Generation (RAG) systems, which combine structured data with unstructured textual knowledge to deliver accurate and fluent answers.

Other options are less suitable for root cause identification:

• call_cust_history focuses on usage frequency for billing, which is unrelated to issue diagnosis.

• maintenance_schedule provides valuable context on service disruptions but lacks granular resolution details.

• call_rep_history tracks agent performance metrics like call length but does not contain information about ticket resolutions.

In summary, the combination of structured problem-resolution data from call_detail and unstructured conversational insights from transcript_volume equips the chatbot with comprehensive information, enhancing its ability to diagnose and resolve HelpDesk tickets effectively.