Databricks-GenAI-Assoc Practice Questions

Question 1

A GenAI engineer is building a RAG application on Databricks. They have registered a foundation model endpoint in Unity Catalog as a model. The application needs to query the endpoint, and the engineer wants to ensure that only members of the group 'genai_team' can invoke it. Which Unity Catalog privilege must be granted on the model object to allow invocation?

Accepted Answer

EXECUTE. To invoke a model registered in Unity Catalog, a user must have the EXECUTE privilege on that model. Granting EXECUTE to the genai_team group ensures that only its members can call the model endpoint, aligning with the requirement to restrict access. Other privileges like SELECT, USE, or CREATE do not authorize model invocation.

Answer

USE

Answer

SELECT

Answer

CREATE

Question 2

An organization is deploying a GenAI application using Databricks Model Serving. Which TWO steps are required to ensure the deployment environment handles model governance and observability effectively?

Accepted Answer

Enable Unity Catalog for the registered model and its versions.. Effective model governance and observability require integrating Unity Catalog for centralized access control and MLflow for tracking inference payloads. These components allow organizations to monitor model performance, lineage, and bias over time. Mastering these tools ensures that production deployments remain compliant, transparent, and auditable, which is essential for regulated industries using generative AI to make data-driven decisions while minimizing operational risks associated with model drift and unauthorized access.

Answer

Hardcode API credentials within the model inference script.

Answer

Disable automatic scaling to maintain consistent latency.

Answer

Manually deploy the model via the standard cluster user interface.

Question 3

Refer to the exhibit. What is the most likely cause for this error in a production RAG application?

Accepted Answer

Network connectivity between Model Serving and the vector store is misconfigured.. The error indicates a network timeout when connecting to the vector store. This is a common connectivity issue between the serving endpoint and the data source. Investigating network security groups, firewall rules, or DNS resolution issues within the Databricks environment is necessary to resolve it. Ensuring robust, low-latency connectivity to the retrieval source is fundamental for reliable RAG performance and minimizing application downtime in production.

Answer

The model is too large for the GPU memory.

Answer

The model weights are corrupted.

Answer

The input prompt is too long for the model.

Question 4

Which Databricks feature is best suited for maintaining the lineage of data used during the preparation of training sets for Generative AI?

Accepted Answer

Unity Catalog. Unity Catalog provides centralized governance, including automated data lineage tracking. This allows engineers to trace the source of data used for model training, which is vital for reproducibility and regulatory compliance. Knowing the exact provenance of training data helps in troubleshooting model bias and ensures that sensitive data sources are correctly identified and managed throughout the lifecycle of the model training process.

Answer

Delta Lake Time Travel

Answer

Cluster Policies

Answer

MLflow Experiments

Question 5

Refer to the exhibit. What is the impact of min_instances: 0 on this deployment?

Accepted Answer

The endpoint will scale to zero when idle, reducing costs.. Setting min_instances to 0 in Databricks Model Serving enables 'scale-to-zero'. This feature is highly effective for cost optimization, as it automatically shuts down the GPU resources when no traffic is present. Upon receiving a new request, the endpoint will automatically scale up, though this may introduce a slight 'cold start' latency. This trade-off is often acceptable for non-critical or low-traffic services to drastically reduce infrastructure costs.

Answer

The endpoint will be permanently disabled.

Answer

The endpoint will have higher latency than a fixed instance.

Answer

The endpoint will be restricted to CPU usage only.

Question 6

A team needs to provide fine-grained access control to an LLM application that uses a vector index stored in a Delta table. Which Unity Catalog feature should they use to restrict access to specific rows based on user identity?

Accepted Answer

Row-level security (RLS). Row-level security (RLS) in Unity Catalog allows administrators to define policies that filter data returned from tables based on the user's identity. By applying these policies to the Delta table used as a knowledge base, the team ensures that the LLM only retrieves information the user is authorized to see. This is essential for compliance and security in multi-tenant or sensitive information application environments.

Answer

Column-level masking

Answer

Workspace-level permissions

Answer

Access Control Lists (ACLs)

Question 7

An enterprise LLM application on Databricks is experiencing high latency and inconsistent responses. Which approach best enables observability to identify the root cause of these performance bottlenecks within the LLM pipeline?

Accepted Answer

Use MLflow Tracing to record inputs, outputs, and execution duration of each step in the chain.. Integrating MLflow Tracing provides granular visibility into the execution flow, including model inputs, outputs, and latency per step. This is critical for diagnosing complex chains where intermediate calls may be stalling. By instrumenting the code, engineers can identify which specific retrieval step or prompt generation block is contributing to the high latency, allowing for targeted optimization of the RAG pipeline components.

Answer

Enable standard Spark UI logs to monitor cluster resource utilization during inference.

Answer

Review the Databricks SQL query history to identify slow retrieval operations.

Answer

Deploy an external monitoring agent to scan the model server network traffic.

Question 8

A Generative AI engineer is building a Delta Live Tables pipeline that ingests raw JSON event logs into a bronze table, then uses ai_query to classify each event's free-text field. The classification call is expensive, so the engineer wants to avoid re-running it on events that have already been processed in previous pipeline updates. The source table is append-only and new events arrive continuously. Which Delta Live Tables feature should the engineer configure on the bronze table to prevent reprocessing of previously ingested rows?

Accepted Answer

Configure the bronze table as a streaming table so it processes only new data on each pipeline update.. An append-only bronze ingestion table should be defined as a streaming table so each pipeline update consumes only new source records. This makes expensive operations like ai_query run once per new event rather than on the full history. Materialized views and batch-style definitions re-read all source data, and storage properties like Auto Optimize only affect file layout, not incremental read behavior.

Answer

Add a QUALIFY clause that filters out rows where the classification column is already populated.

Answer

Enable change data capture by setting pipelines.cdcEnabled to true on the bronze table.

Answer

Set the table property pipelines.autoOptimize.managed to true on the bronze table.

Question 9

A data team is developing a Mosaic AI Agent application. Which TWO of the following are mandatory for deploying this application using the Databricks Model Serving infrastructure?

Accepted Answer

A logged model signature defining input and output schemas. Deploying Mosaic AI Agents requires defining a signature for the model input/output and ensuring the agent code is packaged with its dependencies. These steps are crucial because the Serving environment needs to understand how to route requests and instantiate the environment. Properly defining the signature ensures type safety during inference, while dependency management prevents runtime errors, ensuring the agent operates as expected within the serverless environment.

Answer

A manual provisioning of a dedicated GPU worker pool

Answer

A hardcoded API key for external vector store access

Answer

A pre-configured Kubernetes manifest file

Question 10

What is the primary benefit of using MLflow Model Evaluation for generative AI applications compared to manual evaluation methods?

Accepted Answer

It enables consistent, repeatable evaluation metrics across different model versions.. MLflow Model Evaluation provides a standardized framework to calculate metrics like RAGAS and perform side-by-side comparisons of model versions. By automating this process, organizations can ensure consistency across releases, track performance history over time, and reduce human bias. This systematic approach allows teams to make data-driven decisions when deciding whether to promote a model to production, ensuring reliability and auditability in the AI lifecycle.

Answer

It automatically rewrites the prompt templates based on user feedback.

Answer

It removes the need for ground truth datasets during the testing phase.

Answer

It creates the vector index automatically from raw unstructured data.

Question 11

When evaluating LLM outputs, which TWO metrics are most appropriate for measuring the 'quality' of a response in a RAG system? (Choose two)

Accepted Answer

Faithfulness. Answering quality in RAG requires looking at two distinct dimensions: the correctness of the content (faithfulness) and the relevance of the answer to the user's intent. Faithfulness ensures the model remains grounded in the provided evidence, while answer relevance captures whether the model actually addressed the specific user question. These metrics provide a balanced view, ensuring the system is both accurate and useful, which is essential for end-user satisfaction.

Answer

Inference Latency

Answer

GPU Utilization

Answer

Training Iterations

Question 12

A developer needs to deploy a custom Python model that requires non-standard library dependencies. Which MLflow feature should the developer use to specify these environment requirements during model logging?

Accepted Answer

MLflow environment requirements (pip_requirements). When logging a custom model with MLflow, the developer should use the `pip_requirements` or `conda_env` parameter in the `mlflow.pyfunc.log_model` function. This ensures that the environment is correctly packaged and reproducible. During deployment, the Databricks Model Serving service reads these requirements to recreate the identical software environment, preventing runtime errors caused by missing dependencies when the model is loaded and served in the production environment.

Answer

Model Schema definition

Answer

Global workspace library settings

Answer

Custom model signatures

Question 13

Refer to the exhibit. An engineer wants to perform a canary deployment by routing 10% of traffic to a new version (version 6). How should the JSON traffic configuration be modified?

Accepted Answer

Add a new route for version 6 and set its traffic_percentage to 10, then set version 5 to 90.. Canary deployments involve splitting traffic between an established model version and a new candidate. To achieve this, the 'routes' list must include both versions, with the 'traffic_percentage' values summing to 100%. This controlled rollout allows teams to monitor the performance and accuracy of the new model on a small segment of production traffic before committing the full load to the new version.

Answer

Set the traffic_percentage of version 6 to 10 and keep version 5 at 100.

Answer

Remove version 5 and set version 6 to 10.

Answer

Create a new endpoint entirely for version 6 and switch the DNS record.

Question 14

A data scientist is using Databricks to fine-tune a large language model. The training data is stored in a Unity Catalog table that contains sensitive customer information. The data scientist needs to read the table but should not be able to see the raw values of certain columns. Which Unity Catalog feature should be used to dynamically mask the sensitive columns based on the user's group membership?

Accepted Answer

Dynamic column masking. Dynamic column masking in Unity Catalog enables the data scientist to see masked values for sensitive columns based on their group membership. By applying a column mask that checks group membership, the raw values are hidden from unauthorized users while still allowing the data scientist to read the table for fine-tuning.

Answer

Column-level encryption

Answer

Attribute-based access control

Answer

Row-level security

Question 15

When evaluating the performance of a RAG application, which metric is most useful for measuring the quality of the retrieved context?

Accepted Answer

Context Precision.. Context Precision measures how much of the retrieved information is actually relevant to the user query. This is a critical metric for RAG systems because high retrieval precision directly reduces the noise in the LLM input, leading to more accurate and focused answers. Measuring this allows engineers to iteratively improve the retrieval pipeline, which is essential for maximizing the utility of the application.

Answer

Total number of documents retrieved.

Answer

The total latency of the RAG pipeline.

Answer

The number of parameters in the LLM.

Databricks Certified Generative AI Engineer Associate Databricks-GenAI-Assoc practice test

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Databricks Certified Generative AI Engineer Associate practice questions