Databricks Certified Generative AI Engineer Associate
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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 Details
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
Exam Outline
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
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1. 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
2. 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
3. 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
4. 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
5. 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
6. 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
7. What is an effective method to preprocess prompts using custom code before sending them to an LLM?
A.Directly modify the LLM’s internal architecture to include preprocessing steps
B.It is better not to introduce custom code to preprocess prompts as the LLM has not been trained with examples of the preprocessed prompts
C.Rather than preprocessing prompts, it’s more effective to postprocess the LLM outputs to align the outputs to desired outcomes
D.Write a MLflow PyFunc model that has a separate function to process the prompts
Answer: D
8. A Generative Al Engineer is creating an LLM system that will retrieve news articles from the year 1918 and related to a user's query and summarize them. The engineer has noticed that the summaries are generated well but often also include an explanation of how the summary was generated, which is undesirable.
Which change could the Generative Al Engineer perform to mitigate this issue?
A.Split the LLM output by newline characters to truncate away the summarization explanation.
B.Tune the chunk size of news articles or experiment with different embedding models.
C.Revisit their document ingestion logic, ensuring that the news articles are being ingested properly.
D.Provide few shot examples of desired output format to the system and/or user prompt.
Answer: D
9. 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
10. A Generative Al Engineer is tasked with developing an application that is based on an open source large language model (LLM). They need a foundation LLM with a large context window.
Which model fits this need?
A.DistilBERT
B.MPT-30B
C.Llama2-70B
D.DBRX
Answer: C
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