If you're preparing for the Databricks Certified Generative AI Engineer Associate exam, you're likely looking for the best resources to ensure success. This certification is designed to validate your skills in applying generative AI models within the Databricks platform, making it a sought-after credential for AI professionals. One of the most effective ways to boost your chances of passing is to familiarize yourself with the latest exam questions. PassQuestion provides updated Databricks Generative AI Engineer Associate Exam Questions, offering you a valuable resource to practice and sharpen your knowledge, helping you ace the exam with confidence.

Databricks Generative AI Engineer Associate Certification

The Databricks Certified Generative AI Engineer Associate certification exam assesses an individual’s ability to design and implement LLM-enabled solutions using Databricks. This includes problem decomposition to break down complex requirements into manageable tasks as well as choosing appropriate models, tools and approaches from the current generative AI landscape for developing comprehensive solutions. It also assesses Databricks-specific tools such as Vector Search for semantic similarity searches, Model Serving for deploying models and solutions, MLflow for managing a solution lifecycle, and Unity Catalog for data governance. Individuals who pass this exam can be expected to build and deploy performant RAG applications and LLM chains that take full advantage of Databricks and its toolset.

Exam Information

Type: Proctored certification
Total number of questions: 45
Time limit: 90 minutes
Registration fee: $200
Question types: Multiple choice
Languages: English, Japanese, Portugues BR, Korea
Delivery method: Online proctored
Recommended experience: 6+ months of hands-on experience performing the generative AI solutions tasks outlined in the exam guide
Validity period: 2 years

Databricks Generative AI Engineer Associate Exam Sections

Section 1: Design Applications – 14%

• Design a prompt that elicits a specifically formatted response
• Select model tasks to accomplish a given business requirement
• Select chain components for a desired model input and output
• Translate business use case goals into a description of the desired inputs and outputs for the AI pipeline
• Define and order tools that gather knowledge or take actions for multi-stage reasoning

Section 2: Data Preparation – 14%

• Apply a chunking strategy for a given document structure and model constraints
• Filter extraneous content in source documents that degrades quality of a RAG application
• Choose the appropriate Python package to extract document content from provided source data and format.
• Define operations and sequence to write given chunked text into Delta Lake tables in Unity Catalog
• Identify needed source documents that provide necessary knowledge and quality for a given RAG application
• Identify prompt/response pairs that align with a given model task
• Use tools and metrics to evaluate retrieval performance

Section 3: Application Development – 30%

• Create tools needed to extract data for a given data retrieval need
• Select Langchain/similar tools for use in a Generative AI application.
• Identify how prompt formats can change model outputs and results
• Qualitatively assess responses to identify common issues such as quality and safety
• Select chunking strategy based on model & retrieval evaluation
• Augment a prompt with additional context from a user's input based on key fields, terms, and intents
• Create a prompt that adjusts an LLM's response from a baseline to a desired output
• Implement LLM guardrails to prevent negative outcomes
• Write metaprompts that minimize hallucinations or leaking private data
• Build agent prompt templates exposing available functions
• Select the best LLM based on the attributes of the application to be developed
• Select a embedding model context length based on source documents, expected queries, and optimization strategy
• Select a model for from a model hub or marketplace for a task based on model metadata/model cards
• Select the best model for a given task based on common metrics generated in experiments

Section 4: Assembling and Deploying Applications – 22%

• Code a chain using a pyfunc model with pre- and post-processing
• Control access to resources from model serving endpoints
• Code a simple chain according to requirements
• Code a simple chain using langchain
• Choose the basic elements needed to create a RAG application: model flavor, embedding model, retriever, dependencies, input examples, model signature
• Register the model to Unity Catalog using MLflow
• Sequence the steps needed to deploy an endpoint for a basic RAG application
• Create and query a Vector Search index
• Identify how to serve an LLM application that leverages Foundation Model APIs
• Identify resources needed to serve features for a RAG application

Section 5: Governance – 8%

• Use masking techniques as guard rails to meet a performance objective
• Select guardrail techniques to protect against malicious user inputs to a Gen AI application
• Recommend an alternative for problematic text mitigation in a data source feeding a RAG application
• Use legal/licensing requirements for data sources to avoid legal risk

Section 6: Evaluation and Monitoring – 12%

• Select an LLM choice (size and architecture) based on a set of quantitative evaluation metrics
• Select key metrics to monitor for a specific LLM deployment scenario
• Evaluate model performance in a RAG application using MLflow
• Use inference logging to assess deployed RAG application performance
• Use Databricks features to control LLM costs for RAG applications

Share Databricks Certified Generative AI Engineer Associate Free Dumps

1. A Generative AI Engineer just deployed an LLM application at a digital marketing company that assists with answering customer service inquiries.
Which metric should they monitor for their customer service LLM application in production?
A. Number of customer inquiries processed per unit of time
B. Energy usage per query
C. Final perplexity scores for the training of the model
D. HuggingFace Leaderboard values for the base LLM
Answer: A

2. A Generative AI Engineer is designing an LLM-powered live sports commentary platform. The platform provides real-time updates and LLM-generated analyses for any users who would like to have live summaries, rather than reading a series of potentially outdated news articles.
Which tool below will give the platform access to real-time data for generating game analyses based on the latest game scores?
A. DatabrickslQ
B. Foundation Model APIs
C. Feature Serving
D. AutoML
Answer: C

3. A Generative AI Engineer has a provisioned throughput model serving endpoint as part of a RAG
application and would like to monitor the serving endpoint’s incoming requests and outgoing responses. The current approach is to include a micro-service in between the endpoint and the user interface to write logs to a remote server.
Which Databricks feature should they use instead which will perform the same task?
A. Vector Search
B. Lakeview
C. DBSQL
D. Inference Tables
Answer: D

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

5. 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.
Answer: B

6. Generative AI Engineer at an electronics company just deployed a RAG application for customers to ask questions about products that the company carries. However, they received feedback that the RAG response often returns information about an irrelevant product.
What can the engineer do to improve the relevance of the RAG’s response?
A. Assess the quality of the retrieved context
B. Implement caching for frequently asked questions
C. Use a different LLM to improve the generated response
D. Use a different semantic similarity search algorithm
Answer: A

7. 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
Answer: B

8. A Generative AI Engineer is designing a chatbot for a gaming company that aims to engage users on its platform while its users play online video games.
Which metric would help them increase user engagement and retention for their platform?
A. Randomness
B. Diversity of responses
C. Lack of relevance
D. Repetition of responses
Answer: B

9. A Generative AI Engineer is building an LLM to generate article summaries in the form of a type of poem, such as a haiku, given the article content. However, the initial output from the LLM does not match the desired tone or style.
Which approach will NOT improve the LLM’s response to achieve the desired response?
A. Provide the LLM with a prompt that explicitly instructs it to generate text in the desired tone and style
B. Use a neutralizer to normalize the tone and style of the underlying documents
C. Include few-shot examples in the prompt to the LLM
D. Fine-tune the LLM on a dataset of desired tone and style
Answer: B

10. 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 similarity to the previous language
C. The latency of the response and the length of text generated
D. The accuracy and relevance of the responses
Answer: D