Databricks-GenAI-Assoc Practice Test 11 — 15 Questions

Question 1

A developer is building a RAG application using Mosaic AI Model Serving. They need to ensure that the model endpoint logs inference requests and responses for audit purposes. Which configuration parameter should they enable?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Configure the 'inference_table_config' block in the endpoint request JSON.. Enabling 'inference_table_config' in the model serving endpoint configuration is the standard Databricks approach for capturing telemetry data. This feature automatically writes request and response payloads to a Delta table, enabling compliance auditing, model monitoring, and drift detection. Understanding this integration is critical for production-grade AI deployments where transparency, debugging, and regulatory logging are mandatory requirements for enterprise-scale machine learning operations.

Answer

Enable 'auto_capture_logs' in the endpoint environment variables.

Answer

Set the 'log_level' to 'DEBUG' in the Serving Endpoint settings.

Answer

Enable 'external_logging' in the Unity Catalog schema settings.

Question 2

When building a RAG application, a developer wants to ensure that the retrieved context is strictly limited to documents the user has access to. Where should this security logic be enforced?

Accepted Answer

At the Vector Search retrieval layer.. Access control must be enforced at the retrieval layer, ideally within the vector search engine or via a data-filtering mechanism that respects user identity. Relying on the model to enforce security is ineffective, as models cannot reliably manage authorization. Proper integration with Unity Catalog ensures that data retrieval is governed by the same permissions as the underlying tables, maintaining consistent security across the entire data-to-AI lifecycle.

Answer

Inside the prompt engineering layer.

Answer

Within the final response generation phase.

Answer

At the client-side browser application level.

Question 3

Which tool in the Databricks ecosystem is best suited for developers to experiment with prompt engineering and tool-calling logic iteratively?

Accepted Answer

Databricks Notebooks.. Databricks notebooks provide an interactive environment that is perfectly suited for iterative experimentation with prompts and chains. Developers can easily test different variations, observe the model's output in real-time, and integrate various tools, making it the ideal workbench for rapid prototyping in the early stages of the AI development lifecycle before committing to a final, production-ready implementation of their generative AI application.

Answer

Databricks SQL Editor.

Answer

Unity Catalog explorer.

Answer

Cluster Management UI.

Question 4

Which component in the Databricks GenAI stack is responsible for orchestrating the flow between data retrieval, prompt construction, and model invocation?

Accepted Answer

Mosaic AI Agent Framework.. Mosaic AI Agent Framework is designed to manage the complex orchestration required for RAG and agentic workflows. By providing a structured way to define tools, chains, and prompt strategies, it allows developers to build sophisticated applications that dynamically retrieve data and interact with LLMs. This orchestration layer is vital for building robust, maintainable AI applications where the logic needs to be clearly separated from the underlying model serving infrastructure.

Answer

Unity Catalog.

Answer

MLflow Tracking.

Answer

Databricks SQL.

Question 5

A developer is configuring a model serving endpoint as shown in the exhibit. They observe that the endpoint fails to respond quickly to the first request after a period of inactivity. What is the cause of this behavior?

Accepted Answer

The endpoint is entering a cold-start phase due to scale-to-zero.. The 'scale_to_zero_enabled' flag is set to true, which instructs the Databricks infrastructure to shut down the compute resources entirely when no traffic is detected to save costs. When a new request arrives, the infrastructure must perform a 'cold start,' initializing the containers and loading the model into memory. This introduces latency, which is expected behavior when optimizing for infrastructure cost over instant availability in a serverless environment.

Answer

The model version is incompatible with the CPU workload type.

Answer

The model name is not registered in the Unity Catalog.

Answer

The workload type 'CPU' is incorrectly specified as a string.

Question 6

An organization requires that all GenAI models deployed in Databricks be tracked and managed with a unified registry for compliance. Which feature should the developer use?

Accepted Answer

Unity Catalog Model Registry. Unity Catalog Model Registry serves as the centralized hub for governing, versioning, and deploying machine learning models across an organization. By using the Unity Catalog, developers ensure that every model has a documented lineage, access control, and deployment status. This is critical for regulatory compliance and enterprise security, as it prevents unvetted models from being deployed and provides a transparent audit trail of every model version currently in use.

Answer

Use Git branches to manage different model versions.

Answer

Store model artifacts as Pickle files in a shared workspace folder.

Answer

Create a custom Python class to track model metadata in a Delta table.

Question 7

A team is deploying a LLM-based application using Databricks Model Serving. They want to implement robust observability and monitoring for their endpoint. Which TWO features should they utilize to track performance and quality metrics? (Select TWO)

Accepted Answer

Model Inference Tables. To effectively monitor a LLM application, teams must integrate both system-level performance metrics and application-level quality telemetry. Inference tables automatically log input and output data for analysis, while the native 'Monitoring' tab in the Model Serving UI provides latency and throughput metrics. Combined, these features provide a comprehensive view of how the model is performing, identifying bottlenecks in latency or degradation in response quality over time.

Answer

Unity Catalog Lineage

Answer

Databricks Delta Sharing

Answer

Workspace-level Audit Logs

Question 8

Refer to the exhibit. A developer wants to enable monitoring for their deployed LLM endpoint. Given the current configuration, what must the developer change to ensure that request/response logs are captured for analysis?

Accepted Answer

Update 'auto_capture_request_payload' to true.. The current configuration has 'auto_capture_request_payload' set to false, which prevents the logging of inference traffic. By updating this flag to true, the system will start capturing payloads into a Delta table. This is critical for monitoring model performance, drift, and quality. Enabling this feature allows data teams to perform retrospective analysis, which is essential for iterating on model prompts and fine-tuning configurations based on real user interactions.

Answer

Change the task to 'llm/v1/completions'.

Answer

Add a new route with 0% traffic to the 'traffic_config'.

Answer

Increase the 'traffic_percentage' to 200%.

Question 9

Which Databricks asset is best suited for scheduling and orchestrating a multi-step GenAI pipeline that includes data ingestion, vector index updating, and model evaluation?

Accepted Answer

Databricks Workflows. Databricks Workflows is the unified tool for orchestrating multi-step pipelines. It allows developers to define dependencies between tasks, manage retries, and monitor job execution. For GenAI, this is crucial because pipelines often involve sequential dependencies—such as ensuring vector database updates complete before a model is evaluated. Leveraging Workflows ensures reliability and observability in automated production pipelines, which are essential for maintaining the integrity of data and models in an enterprise environment.

Answer

Databricks Feature Store

Answer

Databricks SQL Warehouse

Answer

Unity Catalog

Question 10

A team is designing an LLM application that requires strict data privacy. Which TWO approaches ensure that sensitive data is not leaked during the model inference process or training?

Accepted Answer

Redact PII from prompts using local libraries before sending data to the model.. Data privacy in GenAI necessitates both input sanitization and secure environment configuration. By using PII redaction libraries before sending tokens to the model and enforcing Unity Catalog access controls on training datasets, developers create a robust defense-in-depth posture. These techniques protect against prompt injection and unauthorized data exposure, which are critical security considerations when deploying large language models within enterprise-grade environments where compliance and data sovereignty are top priorities.

Answer

Enable public access on all input data buckets for faster retrieval.

Answer

Store all API keys as plain text in notebook variables.

Answer

Use the model provider's default logging for all user queries.

Question 11

An AI engineer is designing a scalable customer support application on Databricks that integrates custom vector search indexes with a fine-tuned LLM. Which TWO architectural components are essential for enabling efficient similarity search and low-latency retrieval within the Databricks ecosystem? (Choose TWO)

Accepted Answer

Databricks Vector Search. Databricks Vector Search provides serverless vector database capabilities to store and query embeddings efficiently without managing external infrastructure. Combined with Databricks Model Serving for hosting the LLM, these native services ensure high throughput, enterprise-grade security, and low-latency inference required for modern retrieval-augmented generation applications built on the Databricks platform.

Answer

An external third-party Hadoop cluster managed via SSH tunnels

Answer

A manual cron job running on a local developer laptop

Answer

A static CSV file stored on local driver disk storage

Question 12

A developer is building a retrieval-augmented generation (RAG) application on Databricks. They need to ensure that embeddings are updated automatically when the underlying Delta table changes. Which approach is the most efficient and scalable?

Accepted Answer

Implement a Delta Live Tables pipeline using streaming tables and a Python UDF to generate embeddings on arrival.. Delta Live Tables (DLT) with streaming tables allows for continuous data processing and incremental updates. By integrating embedding generation directly into the pipeline, the developer ensures that the vector database stays in sync with the source data without manual intervention or complex scheduling. This architecture minimizes latency and improves data consistency, which is a foundational requirement for production-grade RAG applications within the Databricks ecosystem.

Answer

Write a manual PySpark job that runs every hour to scan the entire Delta table and recompute all embeddings.

Answer

Use a Databricks Job with a notebook task triggered by a cron schedule to process changes using a watermark.

Answer

Deploy a Unity Catalog volume to store the embeddings and trigger an external API call from an event-driven function.

Question 13

When deploying a Generative AI application, why is it recommended to use a dedicated Serving Endpoint rather than a shared interactive cluster?

Accepted Answer

Serving endpoints provide better isolation and predictable performance.. Dedicated serving endpoints provide resource isolation, performance predictability, and standardized API interfaces. Shared clusters are prone to resource contention, inconsistent environment states, and lack the operational features required for reliable production inference. Separating the serving infrastructure from development clusters is a standard architectural pattern that ensures the stability and scalability of AI-driven features in user-facing applications.

Answer

Shared clusters are cheaper to run for production.

Answer

Shared clusters cannot be used for Python-based models.

Answer

Serving endpoints automatically train the model on new data.

Question 14

When deploying a model to Databricks Model Serving, which THREE of the following are best practices to ensure production reliability?

Accepted Answer

Configuring autoscaling policies based on traffic patterns.. Production reliability for model serving requires careful attention to environment configuration, scalability, and security. Best practices include using specific model versions to prevent unexpected updates, configuring autoscaling to handle variable traffic, and utilizing private connectivity to keep data transmission secure. These steps minimize downtime and ensure that the serving endpoint behaves predictably under various load conditions, which is critical for enterprise-grade generative AI applications.

Answer

Deploying the 'latest' alias for all production endpoints.

Answer

Using static credentials embedded directly in the deployment YAML.

Question 15

Refer to the exhibit. What is the current permission state for the 'analyst_group' after the execution of the REVOKE statement?

Accepted Answer

The group loses access to the table.. Unity Catalog employs a standard additive-subtractive permission model. When a user or group is explicitly denied or revoked a privilege, the revocation takes precedence. After the REVOKE command is executed, the SELECT privilege is removed from 'analyst_group'. Even if the group was previously granted access, the REVOKE command effectively clears that privilege, ensuring the group can no longer access the specified table.

Answer

The group retains access because they are still mentioned in the grant history.

Answer

The group retains access due to the order of operations in the metastore.

Answer

The group can still access the table if they are members of the manager_group.