DATABRICKS-GAIE Practice Questions: Assembling and Deploying Apps Domain

Published: September 29, 2025 | 20 min read

Test your DATABRICKS-GAIE knowledge with 10 practice questions from the Assembling and Deploying Apps domain. Includes detailed explanations and answers.

DATABRICKS-GAIE Practice Questions

Master the Assembling and Deploying Apps Domain

Test your knowledge in the Assembling and Deploying Apps domain with these 10 practice questions. Each question is designed to help you prepare for the DATABRICKS-GAIE certification exam with detailed explanations to reinforce your learning.

Question 1

You are tasked with deploying a machine learning model on Databricks to automatically classify customer feedback. The model needs to be updated weekly with new training data and should be accessible via an API endpoint. Which of the following approaches best ensures a reliable and automated deployment process?

A) Manually retrain the model every week and update the API endpoint with the new model.

B) Use MLflow to manage model versions and set up a Databricks Job to retrain and deploy the model weekly.

C) Deploy the model as a Databricks notebook and manually trigger it every week.

D) Create a REST API using Flask and deploy it on a separate server outside Databricks.

Show Answer & Explanation

Correct Answer: B

Explanation: Option B is correct because MLflow provides a streamlined way to manage model versions and automate the deployment process using Databricks Jobs. This ensures the model is retrained and deployed weekly without manual intervention. Option A is incorrect because it lacks automation. Option C is not ideal because it requires manual triggering. Option D involves unnecessary complexity and does not leverage Databricks' capabilities.

Question 2

After deploying your model on Databricks, you notice performance degradation. Which Databricks feature allows you to monitor the model's performance metrics in real-time and implement a feedback loop for continuous improvement?

A) Unity Catalog

B) Delta Lake

C) MLflow Model Registry

D) Databricks Jobs

Show Answer & Explanation

Correct Answer: C

Explanation: MLflow Model Registry allows you to track model performance metrics, version models, and manage the lifecycle of a model. It provides real-time monitoring and can be integrated with feedback loops for continuous improvement. Unity Catalog is for data governance, Delta Lake for data storage, and Databricks Jobs for scheduling tasks.

Question 3

In a Databricks environment, how can you ensure that your deployed model's API is secure from unauthorized access?

A) Enable public access to the API endpoint.

B) Use API keys and tokens for authentication.

C) Store all API keys in plaintext within the code.

D) Disable network security groups for unrestricted access.

Show Answer & Explanation

Correct Answer: B

Explanation: Using API keys and tokens for authentication is a standard practice to secure APIs from unauthorized access. Enabling public access, storing keys in plaintext, and disabling security groups increase the risk of unauthorized access.

Question 4

When deploying a model on Databricks, which of the following practices ensures that the model can handle unexpected spikes in demand?

A) Deploy the model on a single-node cluster.

B) Implement autoscaling for the cluster running the model.

C) Use a static configuration for resource allocation.

D) Deploy the model without any monitoring tools.

Show Answer & Explanation

Correct Answer: B

Explanation: Implementing autoscaling allows the cluster to automatically adjust its resources based on demand, ensuring that the model can handle unexpected spikes. Option A limits scalability. Option C does not allow for dynamic resource allocation. Option D is not advisable as monitoring is crucial for managing demand and performance.

Question 5

You are tasked with deploying a machine learning model on Databricks to predict customer churn. The model is trained and logged using MLflow. Which of the following steps should you take to ensure the model is deployed with proper version control and can be accessed via REST API?

A) Directly export the model as a pickle file and serve it using a Flask application.

B) Use MLflow's built-in REST API feature to deploy the model and manage versions.

C) Deploy the model using MLflow Model Registry and serve it with MLflow's REST API.

D) Containerize the model using Docker and deploy it on a Kubernetes cluster.

Show Answer & Explanation

Correct Answer: C

Explanation: The correct approach is to use MLflow Model Registry to handle model versioning and deployment. MLflow's REST API can then be used to serve the model. Option A lacks version control, option B is partially correct but lacks the registry step, and option D is more complex and not specific to Databricks.

Question 6

You are tasked with deploying a machine learning model on Databricks using MLflow. Which of the following steps is crucial to ensure that your model can be easily updated and monitored in production?

A) Deploy the model as a standalone application without using MLflow.

B) Use MLflow's model registry to manage different versions of the model.

C) Store the model files directly in a local directory on the Databricks cluster.

D) Deploy the model using a custom script without logging any metadata.

Show Answer & Explanation

Correct Answer: B

Explanation: Using MLflow's model registry allows you to manage different versions of your model, track changes, and facilitate easy updates and monitoring in production. Options A, C, and D do not provide the same level of management and monitoring capabilities.

Question 7

After deploying a model on Databricks, you want to implement a CI/CD pipeline for continuous integration and deployment. Which of the following tools would you integrate with Databricks to achieve this?

A) Jenkins

B) Apache Kafka

C) Unity Catalog

D) Delta Lake

Show Answer & Explanation

Correct Answer: A

Explanation: Jenkins is a widely used tool for implementing CI/CD pipelines and can be integrated with Databricks for continuous integration and deployment. Apache Kafka (Option B) is used for real-time data streaming, Unity Catalog (Option C) is for data governance, and Delta Lake (Option D) is for data storage.

Question 8

You have developed a machine learning model using Databricks and need to deploy it as a REST API to allow other applications to consume it. Which of the following steps is crucial to ensure the model is properly versioned and deployed using Databricks capabilities?

A) Export the model as a TensorFlow SavedModel and host it on a third-party service.

B) Use MLflow to register the model and deploy it through Databricks Model Serving.

C) Deploy the model using a custom Flask application on a separate VM.

D) Convert the model to ONNX format and deploy using Azure Functions.

Show Answer & Explanation

Correct Answer: B

Explanation: MLflow is integrated with Databricks and is used for tracking experiments, versioning models, and deploying them using Databricks Model Serving. This ensures that the model is properly versioned and can be easily deployed as a REST API. The other options involve external tools or services that do not leverage Databricks' built-in capabilities.

Question 9

How does containerizing a machine learning model in Databricks improve the deployment process?

A) It makes the model dependent on the underlying hardware.

B) It ensures consistent runtime environments across different deployment stages.

C) It requires manual dependency management for each deployment.

D) It restricts model deployment to on-premise servers only.

Show Answer & Explanation

Correct Answer: B

Explanation: Containerizing a model ensures that it runs in a consistent environment across different stages of deployment, reducing issues related to dependency conflicts. Option A is incorrect as containers are platform-independent. Option C is incorrect because containers manage dependencies automatically. Option D is incorrect as containers can be deployed on various platforms, including cloud environments.

Question 10

You are implementing a CI/CD pipeline for your Databricks ML model deployment. Which of the following tools would you use to automate the deployment process?

A) Apache Kafka

B) GitHub Actions

C) Unity Catalog

D) Spark Streaming

Show Answer & Explanation

Correct Answer: B

Explanation: GitHub Actions can be used to automate CI/CD pipelines, including those for deploying ML models on Databricks. It allows you to define workflows that automate the build, test, and deployment processes. Apache Kafka is for stream processing, Unity Catalog is for data governance, and Spark Streaming is for real-time data processing.

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About DATABRICKS-GAIE Certification

The DATABRICKS-GAIE certification validates your expertise in assembling and deploying apps and other critical domains. Our comprehensive practice questions are carefully crafted to mirror the actual exam experience and help you identify knowledge gaps before test day.