Practice Tests: Databricks Certified Generative AI Engineer
Master the Databricks Certified Generative AI Engineer Associate Exam with Realistic Practice Tests.
Practice Tests: Databricks Certified Generative AI Engineer free download
Master the Databricks Certified Generative AI Engineer Associate Exam with Realistic Practice Tests.
Practice Tests: Databricks Certified Generative AI Engineer Associate
Description: If you looking for practice tests for Databricks Certified Generative AI Engineer Associate exam, you have come to the right place! Two practice tests with detailed explanations are available to prepare you before appearing for the actual exam.
About the Exam:
1. Number of items: 45 multiple-choice or multiple-selection questions
2. Time Limit: 90 minutes
3. Registration fee: $200
4. Delivery method: Online Proctored
5. Validity: 2 years.
8. Recertication: Recertication is required every two years to maintain your certified status.
The practice tests cover the following exam topics with explanations:
Section 1: Design Applications
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
Dene and order tools that gather knowledge or take actions for multi-stage reasoning
Section 2: Data Preparation
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.
Dene 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
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 elds, 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
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 avor, embedding model, retriever, dependencies, input examples, model signature
Register the model to Unity Catalog using MLow
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
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
Select an LLM choice (size and architecture) based on a set of quantitative evaluation metrics
Select key metrics to monitor for a specic LLM deployment scenario
Evaluate model performance in a RAG application using MLow
Use inference logging to assess deployed RAG application performance
Use Databricks features to control LLM costs for RAG applications
Questions:
There are 45 multiple-choice questions on each practice exam. The questions will be distributed topic wise in the following way:
1. Design Applications – 14%
2. Data Preparation – 14%
3. Application Development – 30%
4. Assembling and Deploying Apps – 22%
5. Governance – 8%
6. Evaluation and Monitoring – 12%
By completing these practice tests, you will gain the confidence and knowledge needed to pass the Databricks Certified Generative AI Engineer Associate exam on your first attempt.
I wish you all the best in your exam!
