Databricks-GenAI-Assoc Practice Exam 2 — 20 Questions

Question 1

Which TWO factors are most important when selecting a chunking strategy for text data prior to vectorization?

Accepted Answer

The maximum input token length of the target embedding model.. Selecting a chunking strategy requires balancing semantic context and technical limitations like context window size. If chunks are too small, they lack meaning; if too large, they exceed the limits of the embedding model and introduce irrelevant noise. Proper chunking is vital for maximizing the accuracy of RAG systems, as it determines the granularity of the information available for the retriever to present to the LLM.

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The file format of the source documents (e.g., PDF vs. Word).

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The total number of documents in the corpus.

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The storage cost of the resulting vector database.

Question 2

Which approach is recommended for managing secrets, such as API keys for external LLM providers, in Databricks?

Accepted Answer

Storing keys in Databricks Secrets.. Databricks Secrets provides a secure, encrypted storage mechanism for sensitive information, preventing credentials from being hardcoded in notebooks or scripts. This is a foundational security best practice. By using scope-based secret management, you ensure that only authorized users or service principals can access the credentials, keeping your RAG application secure and compliant with enterprise security standards.

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Hardcoding keys in a configuration file within a Repo.

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Passing keys as plain text arguments in a job parameter.

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Storing keys in a public S3 bucket with restricted access.

Question 3

Refer to the exhibit. A developer is deploying a model using the provided JSON configuration. What is the primary benefit of setting 'scale_to_zero_enabled' to true in this production RAG application?

Accepted Answer

It reduces operational costs by shutting down compute resources when the endpoint is not serving traffic.. Setting 'scale_to_zero_enabled' to true allows the Databricks Model Serving endpoint to automatically shut down compute resources when no requests are being processed. This is highly beneficial for cost optimization, as it eliminates idle runtime costs during periods of inactivity. When a new request arrives, the service automatically initializes the model, ensuring cost efficiency without requiring manual intervention to scale the underlying infrastructure up or down.

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It increases the throughput of the model by optimizing memory allocation during inference.

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It ensures that the model always stays warm to minimize latency for every user request.

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It enables high availability by automatically replicating the model across multiple availability zones.

Question 4

Refer to the exhibit. The user is a member of the 'finance_team'. Why might the user encounter an access error when executing this join query?

Accepted Answer

The user lacks the USAGE privilege on the 'sales' schema.. In Unity Catalog, users require the USAGE privilege on all parent objects—the catalog and the schema—to access a table. While the user has USAGE on the catalog and SELECT on the tables, they lack the USAGE privilege on the 'sales' schema. Without explicit USAGE permission on the schema, the user cannot traverse the hierarchy to reference the tables, causing a permission denied error.

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The user lacks the SELECT privilege on the schema object.

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The user requires the MODIFY privilege to perform joins.

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The user needs to be an owner of the tables to perform joins.

Question 5

Refer to the exhibit. Why did the analyst group lose access after the table was recreated?

Accepted Answer

The new table is a different object, so the GRANT statement must be repeated.. In Unity Catalog, privileges are bound to the specific object ID. When a table is dropped and re-created, it becomes a new object with a new unique identifier. The previous GRANT statements, which were associated with the original object ID, do not automatically apply to the new table. This mechanism protects against security drift by ensuring that every new object must have its permissions explicitly managed.

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The catalog owner needs to refresh the table metadata.

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The analyst group needs to be re-added to the schema permissions.

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The user who recreated the table is not the catalog owner.

Question 6

An AI engineer is developing a custom Databricks App using Mosaic AI Agent Framework and needs to deploy the application workspace securely. Which deployment artifact and configuration mechanism should the engineer use to define the app dependencies and entry point?

Accepted Answer

An app.yaml configuration file defining the command, environment variables, and source path alongside the application source code.. Deploying Databricks Apps requires an app.yaml configuration file and source code bundled together. The app.yaml file specifies the runtime, environment variables, and command-line entry points required to execute the application properly inside the Databricks environment. Defining these parameters correctly ensures the Mosaic AI Agent dependencies are initialized and served without manual intervention.

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A setup.py file placed in the root directory configured to automatically trigger a cluster restart upon deployment.

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A Databricks Asset Bundles deployment target pointing to an external Kubernetes cluster hosting the agent code.

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A standard requirements.txt file executed through a Jupyter notebook scheduled via a workflow job.

Question 7

A developer is deploying a custom LangChain agent application as a custom Python model to Databricks Model Serving. The application depends on a specific third-party library that is not included in the standard Databricks runtime environment. How should the developer ensure the dependency is installed when the model is loaded into the serving container?

Accepted Answer

Specify the required packages in the extra_pip_requirements argument when logging the MLflow model artifact.. When logging custom PyFunc models to MLflow, developers must explicitly specify Python dependencies using the extra_pip_requirements parameter or a conda.yaml file. This ensures that the Databricks Model Serving environment automatically provisions and installs all necessary external libraries during container builds, preventing runtime import errors and ensuring reliable model execution.

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Manually SSH into the serving container nodes after deployment and run pip install using terminal access.

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Include the external library installation command inside a standard print statement within the model scoring function.

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Rely on the serving endpoint to automatically discover and install any imported Python module dynamically from the public internet.

Question 8

Which TWO of the following are benefits of using Databricks Asset Bundles for deploying AI applications?

Accepted Answer

Enables automated testing through CI/CD pipeline integration. Databricks Asset Bundles standardize the deployment process, making it repeatable, version-controlled, and easier to integrate into CI/CD pipelines. This reduces the risk of configuration drift, where the production environment deviates from the development environment due to manual changes in the UI. By treating infrastructure as code, teams can maintain a clear history of changes and improve the reliability and auditability of their AI application releases.

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Provides a graphical drag-and-drop interface for deployment

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Automatically handles data labeling and cleaning tasks

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Allows users to bypass Unity Catalog governance

Question 9

Which of the following is a primary reason to prefer Databricks Asset Bundles (DABs) over manual workspace deployment?

Accepted Answer

DABs allow for version-controlled and automated deployments.. The primary advantage of DABs is the elimination of manual, error-prone configuration. By defining infrastructure as code, teams gain repeatability, version control, and automation. This leads to significantly fewer deployment failures and allows for consistent, auditable environments across development and production. It is a fundamental shift toward mature MLOps practices, where infrastructure is as controlled and tested as the application code itself.

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DABs provide better performance for Spark queries.

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DABs automatically convert your code to SQL.

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DABs require less memory to run than the standard workspace.

Question 10

When designing a production-ready Databricks notebook for model inference, which TWO practices improve maintainability and performance?

Accepted Answer

Use Databricks Widgets for parameterizing inputs like model paths.. Modular code design allows for easier testing and unit-level debugging, which is crucial for complex inference logic. Using parameterization enables the notebook to be reused across different environments and models without changing the core code. These practices reduce technical debt and simplify the lifecycle of ML models, making the transition from development to production much smoother and ensuring consistent performance across different stages of the CI/CD pipeline.

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Embed all logic including data preprocessing in a single, large notebook.

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Hardcode all file paths and configuration settings for consistency.

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Always run inference on the driver node to avoid network latency.

Question 11

A team stores raw documents in a Unity Catalog volume and needs to build a training set for fine-tuning a large language model. The raw files are in mixed formats including PDF, DOCX, and plain text. The team wants a single Delta table where every row is one document with its extracted text and source path, and wants the extraction to run in parallel across the cluster. Which approach should the team use?

Accepted Answer

Use binaryFile data source to read the volume, then apply a UDF that extracts text per file and writes results to a Delta table.. The binaryFile data source is designed to read arbitrary files as rows with path and content, enabling distributed extraction with a UDF. This yields one row per document with text and source path in a Delta table. Driver loops and plain text readers cannot handle mixed binary formats at scale or in parallel.

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Mount the volume as a local filesystem path and use a Python for-loop to read and extract each file on the driver.

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Use spark.read.text on the volume, which automatically parses PDF and DOCX content into text columns.

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Create an external table over the volume with a schema that includes columns for each document format's fields.

Question 12

What is the primary function of the 'Prompt' object in a Databricks RAG architecture?

Accepted Answer

To format instructions and retrieved context for LLM consumption.. The prompt acts as the interface between the data retrieved from the vector index and the LLM's generative capability. It organizes the context, instructions, and user request into a coherent message structure. Proper prompt design is essential to focus the LLM's attention, reduce extraneous output, and ensure that the generative output remains grounded in the provided evidence.

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To store the user's chat history for session persistence.

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To index documents for faster similarity search.

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To calculate the latency of the RAG pipeline.

Question 13

A developer is using MLflow to track experiments for a RAG application. They want to log the retrieval step's parameters, such as the number of documents retrieved (k) and the embedding model used. Which MLflow API should they use?

Accepted Answer

mlflow.log_param(). MLflow parameters are meant for logging configuration settings that are constant for a run. The number of documents retrieved and the embedding model are such settings. mlflow.log_param() records them as key-value pairs, enabling easy comparison across runs in the MLflow UI. Metrics, artifacts, and tags serve different purposes and are not suitable for this use case.

Answer

mlflow.log_artifact()

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mlflow.set_tag()

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mlflow.log_metric()

Question 14

A developer deploys a new model version as shown in the exhibit. What is the purpose of this configuration?

Accepted Answer

To conduct A/B testing or a canary rollout.. This configuration implements a canary deployment strategy by splitting traffic between two model versions. By routing 90% of requests to the stable version and 10% to the new version, the team can monitor performance and accuracy in a real-world scenario with minimal risk. This is a standard practice for safely rolling out model improvements, allowing teams to collect data on the new version's behavior before a full-scale transition.

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To increase compute capacity for the primary model.

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To load balance between two different cloud regions.

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To force all traffic to model-v1 when model-v2 errors.

Question 15

A GenAI engineer is building a Databricks RAG application where the retrieval step returns the top-5 chunks for each user question. The engineer wants to add a second LLM call that evaluates whether each retrieved chunk contains enough information to answer the question, and then filters out chunks that fail this evaluation before passing the remaining chunks to the final answer-generation prompt. Which design pattern is the engineer implementing?

Accepted Answer

Retrieval-augmented generation with relevance grading. The defining behavior in the scenario is a second LLM call that grades each retrieved chunk for answerability and drops the ones that fail. That is relevance grading layered on top of retrieval-augmented generation, which raises precision by keeping only context that can actually support the answer. The other techniques alter retrieval ranking, prompt reasoning, or embeddings but do not insert an evaluative filtering step between retrieval and generation.

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Chain-of-thought prompting

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Fine-tuning the embedding model on domain data

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Hybrid search combining vector similarity with BM25 keyword matching

Question 16

When developing a feature engineering pipeline using Feature Store, which practice ensures maximum code reusability across training and inference?

Accepted Answer

Use the Feature Store client to log feature definitions and retrieve them.. Defining features as code using the Databricks Feature Store ensures that the same logic is applied during both batch training and real-time inference. By encapsulating feature calculations in Feature Tables, developers avoid the 'training-serving skew' where features are calculated differently in production. This practice is critical in GenAI development to ensure model performance consistency and reduce technical debt caused by disjointed preprocessing pipelines across different stages of the ML lifecycle.

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Calculate features in the application code and pass them as raw inputs.

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Hardcode feature calculation logic inside the training notebook.

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Export features to a static CSV file after every training run.

Question 17

A team is preparing a Delta table of product descriptions for a RAG application. The table receives continuous upserts from a streaming pipeline, and the embedding job reads the table every hour. Engineers notice the embedding job reprocesses every row on each run even though only a few rows change. Which change should be made to the source table to let the embedding job process only new or updated rows?

Accepted Answer

Enable Change Data Feed on the Delta table and have the embedding job read the change feed since the last processed version.. Change Data Feed captures row-level changes with commit versions, allowing a downstream job to read only the inserts, updates, and deletes that occurred since its last checkpoint. This directly solves the problem of reprocessing unchanged rows. Partitioning, compaction, and timestamp filtering improve read characteristics but do not expose which rows changed, so they cannot deliver incremental processing.

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Partition the Delta table by the product category column so the embedding job reads fewer files.

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Convert the table to a view that filters rows by the current timestamp so only recent rows appear.

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Run OPTIMIZE with Z-ORDER on the product identifier column to compact small files before each embedding run.

Question 18

Which Databricks feature is specifically designed to facilitate the rapid development and deployment of LLM applications by providing a managed environment for hosting and testing prompts?

Accepted Answer

Databricks Mosaic AI Playground. Databricks Mosaic AI Playground provides a low-code interface for developers to experiment with different LLMs and system prompts. It allows for quick iteration and testing of model behavior before moving into full production deployment. This environment is crucial because it bridges the gap between experimentation and application development, ensuring that developers can validate prompt engineering strategies within the same governed environment where their data and production models reside.

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Databricks SQL Warehouse

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Unity Catalog Volumes

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Delta Live Tables

Question 19

A GenAI engineer has a RAG application whose retrieval step uses Databricks Vector Search. Users report that answers are sometimes irrelevant because the retriever pulls chunks from documents the user is not authorized to see. The engineer must enforce per-user document ACLs at query time without re-indexing the corpus. Which approach should the engineer take?

Accepted Answer

Add a metadata column that stores the document's ACL principals, then pass a filter string with the caller's identity to the Vector Search query.. Vector Search indexes can carry metadata columns that are queryable through the filters parameter, letting the application pass the caller's identity so only permitted chunks are returned. This satisfies per-user ACL enforcement at query time without rebuilding the index when permissions change, and it avoids leaking unauthorized content into the application tier. The other approaches either do not affect index results or are operationally impractical at scale.

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Post-filter the retrieved chunks in the application layer by querying Unity Catalog for the user's group memberships and dropping chunks that fail the check.

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Create a separate Vector Search index per user and have the application route each request to the user-specific index.

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Enable Delta Sharing on the source Delta table and rely on the workspace's existing table ACLs to filter Vector Search results.

Question 20

An application requires low-latency retrieval of RAG metadata stored in Databricks. Which storage approach balances performance and cost while ensuring seamless integration with Unity Catalog?

Accepted Answer

Deploy a Databricks Vector Search index on a Delta table with automatic synchronization.. Vector Search indexes are designed for low-latency similarity search, which is critical for RAG applications. By leveraging Databricks Vector Search, you avoid the overhead of custom search implementations. This approach ensures that embeddings are automatically synchronized with Delta tables, providing a scalable solution that integrates directly with Unity Catalog's security and governance framework, which is essential for maintaining consistent data access policies across enterprise environments.

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Store embeddings in a flat Parquet file on DBFS accessed via direct Spark reads.

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Utilize a third-party managed vector database outside of the Databricks environment.

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Implement a custom KNN algorithm using standard Python libraries on a single node cluster.