Databricks-GenAI-Assoc Application Development Practice Test 3 — 15 Questions

Question 1

Which approach is most effective for managing the dependencies of a custom ML model when deploying it to Mosaic AI Model Serving?

Accepted Answer

Include a 'requirements.txt' or 'conda.yaml' file in the model artifact.. Defining a custom environment using Conda or a 'requirements.txt' file ensures that the exact library versions required by the model are captured and recreated in the serving environment. This eliminates 'dependency hell' where a model works in development but fails in production due to library version mismatches. Ensuring consistent environments is a foundational step in robust ML engineering that guarantees reproducible performance in the serving infrastructure.

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Install all dependencies manually on the serving cluster after deployment.

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Assume the serving environment has all standard ML libraries pre-installed.

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Package the entire Python environment inside the model artifact as a zip.

Question 2

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.

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

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Store all API keys as plain text in notebook variables.

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

Question 3

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.

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The model version is incompatible with the CPU workload type.

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The model name is not registered in the Unity Catalog.

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

Question 4

An engineer is troubleshooting a Vector Search index that fails to update. The index relies on a Delta table that is frequently updated. What is the most likely cause for the index failing to reflect new data?

Accepted Answer

The synchronization process was not triggered after the table update.. Vector Search indexes are managed assets that require periodic synchronization with the source Delta table. If the synchronization process is not triggered automatically or via an API call, the index will remain stale. Understanding the lifecycle and sync mechanisms of Vector Search is essential for developers to ensure that RAG systems retrieve the most current information, as static indexes quickly lose relevance in dynamic data environments.

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The Delta table must be converted to a Parquet format.

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The embedding model version changed in the middle of a sync.

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Unity Catalog permissions do not allow read access to the source table.

Question 5

A developer is creating a custom model serving endpoint that requires an external API call for data enrichment. What is the recommended way to handle sensitive API keys within the Databricks environment?

Accepted Answer

Use the Databricks Secrets API to manage and retrieve keys.. Secrets management is the cornerstone of secure application development in Databricks. By using the Databricks Secrets API, developers can decouple sensitive credentials from source code. This practice prevents the accidental disclosure of keys in version control systems and ensures that credentials are injected into the runtime environment securely, which is mandatory for maintaining a secure and audit-compliant AI development lifecycle in enterprise cloud environments.

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Store keys as environment variables in the notebook code.

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Hardcode the keys directly into the model serving inference function.

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Use the DBFS root directory to store key-value text files.

Question 6

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.

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Unity Catalog.

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MLflow Tracking.

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Databricks SQL.

Question 7

A team is evaluating their RAG application using Mosaic AI Model Evaluation. Which TWO metrics are most relevant for assessing the quality of the generated responses?

Accepted Answer

Faithfulness.. Model evaluation requires quantitative metrics to measure both the accuracy of retrieved context and the quality of the final output. Faithfulness ensures the model sticks to the provided context, while answer relevance checks if the model actually addresses the user's intent. Using these metrics allows developers to iterate on their prompt engineering and retrieval strategy, ensuring the application remains accurate and useful for end-users while minimizing the risk of hallucinations.

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CPU utilization percentage.

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Network latency in milliseconds.

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Total storage cost of the index.

Question 8

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 9

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.

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Inside the prompt engineering layer.

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Within the final response generation phase.

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At the client-side browser application level.

Question 10

Which of the following is a primary benefit of using Unity Catalog to manage models for a RAG application?

Accepted Answer

It provides centralized lineage and access control.. Unity Catalog provides a unified governance framework that manages permissions, lineage, and discovery across all data and AI assets. In a RAG context, this ensures that the entire pipeline—from the source Delta tables to the vector indexes and the final models—is traceable and governed. This consistency is essential for enterprise compliance and simplifies the operational management of the AI lifecycle by providing a centralized point for auditing and security policy application.

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It automatically generates the optimal prompt for the model.

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It eliminates the need for vector embeddings.

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It forces all data to be stored in the cloud root.

Question 11

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.

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Databricks SQL Editor.

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Unity Catalog explorer.

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Cluster Management UI.

Question 12

A developer is building a RAG application and notices that the retrieval step often returns irrelevant context. Which step in the pipeline should be improved to address this?

Accepted Answer

Refine the chunking strategy and embedding model quality.. Improving retrieval accuracy often involves enhancing the quality of the embeddings or the text chunking strategy. By adjusting how data is partitioned into chunks (e.g., overlapping chunks, semantic chunking) or refining the embedding model, the vector search index can better capture the semantic meaning of the documents. This is a common and critical improvement path in RAG development, as the quality of the retrieval is fundamentally limited by the index's representational capability.

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Increase the number of API calls to the LLM.

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Enable auto-scaling on the serving endpoint.

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Switch to a larger, more expensive LLM.

Question 13

When designing an agentic workflow in Databricks, which TWO practices are essential to ensure the application remains observable and maintainable?

Accepted Answer

Enable MLflow tracing for all agent execution steps.. Observability and maintainability are critical for enterprise-grade AI agents. Using MLflow to log trace data provides visibility into the agent's decision-making process, which is necessary for debugging multi-step reasoning. Simultaneously, implementing robust evaluation suites ensures that agent updates do not introduce regressions in task performance. These two practices form the foundation of a reliable AI operations strategy, enabling teams to confidently deploy and iterate on agentic workflows in production environments.

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Avoid unit testing to speed up the development process.

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Store all agent logic in a single, massive notebook file.

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Disable logging to minimize storage consumption costs.

Question 14

An AI engineer is developing a real-time customer service chatbot application using Databricks Model Serving and needs to securely store API keys and database credentials without hardcoding them into the application code or notebook. Which approach should the engineer use?

Accepted Answer

Utilize Databricks Secrets to create a secret scope and retrieve values securely using dbutils.secrets.get during application execution.. Databricks Secrets provide a secure mechanism for managing credentials and sensitive configuration parameters. By leveraging secret scopes and the dbutils.secrets utility within notebooks or environment variables mapped to serving endpoints, developers ensure that sensitive tokens never leak into version control systems, adhering to strict enterprise security and compliance standards for production application deployments.

Answer

Store the credentials in a plain-text JSON file located in the workspace root directory and read it programmatically at runtime.

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Hardcode the credentials directly into the scoring script deployed to the Databricks Model Serving endpoint.

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Pass the credentials as plain-text arguments in the command-line interface when triggering the job cluster.

Question 15

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.

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An external third-party Hadoop cluster managed via SSH tunnels

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A manual cron job running on a local developer laptop

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A static CSV file stored on local driver disk storage