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Practice Tests: Databricks Certified Generative AI Engineer AssociateDescription: 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 questions2. Time Limit: 90 minutes3. Registration fee: $2004. Delivery method: Online Proctored5. 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 ApplicationsDesign a prompt that elicits a specifically formatted responseSelect model tasks to accomplish a given business requirementSelect chain components for a desired model input and outputTranslate business use case goals into a description of the desired inputs and outputs for the AI pipelineDene and order tools that gather knowledge or take actions for multi-stage reasoningSection 2: Data PreparationApply a chunking strategy for a given document structure and model constraintsFilter extraneous content in source documents that degrades quality of a RAG applicationChoose 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 CatalogIdentify needed source documents that provide necessary knowledge and quality for a given RAG applicationIdentify prompt/response pairs that align with a given model taskUse tools and metrics to evaluate retrieval performanceSection 3: Application DevelopmentCreate tools needed to extract data for a given data retrieval needSelect Langchain/similar tools for use in a Generative AI application.Identify how prompt formats can change model outputs and resultsQualitatively assess responses to identify common issues such as quality and safetySelect chunking strategy based on model & retrieval evaluationAugment a prompt with additional context from a user's input based on key elds, terms, and intentsCreate a prompt that adjusts an LLM's response from a baseline to a desired outputImplement LLM guardrails to prevent negative outcomesWrite metaprompts that minimize hallucinations or leaking private dataBuild agent prompt templates exposing available functionsSelect the best LLM based on the attributes of the application to be developedSelect a embedding model context length based on source documents, expected queries, and optimization strategySelect a model for from a model hub or marketplace for a task based on model metadata/model cardsSelect the best model for a given task based on common metrics generated in experimentsSection 4: Assembling and Deploying ApplicationsCode a chain using a pyfunc model with pre- and post-processingControl access to resources from model serving endpointsCode a simple chain according to requirementsCode a simple chain using langchainChoose the basic elements needed to create a RAG application: model avor, embedding model, retriever, dependencies, input examples, model signatureRegister the model to Unity Catalog using MLowSequence the steps needed to deploy an endpoint for a basic RAG applicationCreate and query a Vector Search indexIdentify how to serve an LLM application that leverages Foundation Model APIsIdentify resources needed to serve features for a RAG applicationSection 5: GovernanceUse masking techniques as guard rails to meet a performance objectiveSelect 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 applicationUse legal/licensing requirements for data sources to avoid legal riskSection 6: Evaluation and MonitoringSelect an LLM choice (size and architecture) based on a set of quantitative evaluation metricsSelect key metrics to monitor for a specic LLM deployment scenarioEvaluate model performance in a RAG application using MLowUse inference logging to assess deployed RAG application performanceUse Databricks features to control LLM costs for RAG applicationsQuestions: 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!

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