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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:

Section	Objectives
Topic 1: Model Deployment and Serving	- Model deployment strategies 1. Databricks Model Serving 2. Batch inference vs real-time inference
Topic 2: MLOps, Monitoring, and Governance	- Governance and compliance 1. Feature Store usage and management 2. Model lifecycle governance - Model monitoring 1. Data drift detection 2. Performance tracking in production
Topic 3: MLflow and Experiment Tracking	- Model registry 1. Versioning and lifecycle management - Experiment management 1. Tracking runs and parameters 2. Model comparison and selection
Topic 4: Machine Learning Workflow on Databricks	- End-to-end ML pipelines 1. Reusable ML workflows 2. Pipeline construction and orchestration - Data preparation and feature engineering 1. Feature engineering techniques 2. Data ingestion and cleaning in Databricks
Topic 5: Machine Learning Models and Algorithms	- Unsupervised learning methods 1. Clustering techniques 2. Dimensionality reduction - Supervised learning methods 1. Classification and regression models 2. Model evaluation metrics

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100% Pass 2026 Databricks-Machine-Learning-Professional: Databricks Certified Machine Learning Professional –Trustable Examcollection Vce

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Databricks Certified Machine Learning Professional Sample Questions (Q154-Q159):

NEW QUESTION # 154
Which MLflow feature helps reproduce training runs?

A. Delta Sharing
B. MLflow Tracking
C. MLflow Projects
D. Model Serving

Answer: B

Explanation:
Tracking logs:
parameters
code version
metrics
artifacts
making runs reproducible.

NEW QUESTION # 155
A machine learning engineer has found drift in a production machine learning application. The engineer has determined that retraining and deploying a new model is necessary. Which statement must be true prior to deploying the new model?

A. All of these statements must be true prior to deploying the new model
B. The new model must perform better than the original model on the most recently available data
C. The now model must perform better than the original model on all of the available data
D. The new model must perform better than the original model on a random subset of the available data

Answer: B

Explanation:
When drift is detected, it indicates that the data distribution has changed. Therefore, the new model must perform better than the original model on the most recently available data, as that data reflects current conditions. This ensures the new model is well-adapted to the current environment and will deliver more reliable predictions.

NEW QUESTION # 156
A machine learning engineer needs to select a deployment strategy for a new machine learning application. The machine learning application requires central prediction computation and exceedingly fast results, but only a handful of predictions need to be computed at a time. Which deployment strategy can be used to meet these requirements?

A. Real-time
B. Edge/on-device
C. Streaming
D. Batch

Answer: A

Explanation:
Real-time deployment is the appropriate strategy when predictions need to be computed centrally with very low latency, even if only a small number of predictions are required at a time. This ensures fast responses for applications requiring immediate inference.

NEW QUESTION # 157
A Machine Learning Engineer has a real-time fraud detection model deployed that approves or blocks millions of transactions daily. They need to deploy a new version of the model with improved detection accuracy to this high-traffic, business-critical application. Because any model downtime could result in lost revenue or customer dissatisfaction, the engineer must ensure zero downtime and minimal disruption for end users. Leadership also requires that any rollback to the previous version be immediate if issues are detected with the new model in production. Which deployment strategy meets these requirements?

A. Deploy the new model in parallel with the old version, monitor both for a set period, and then notify all stakeholders to manually switch over to the new version at a coordinated time.
B. Use a canary deployment by initially routing a small percentage of user traffic to the new model version, monitoring results, and gradually increasing exposure until all traffic uses the new version if no problems are detected.
C. Replace the current production model with the new version during a scheduled maintenance window, notify affected users of potential brief disruptions, and prepare to reroute requests to a backup if failures occur after deployment.
D. Use a blue-green deployment for the new model, maintaining two separate production environments (one "blue," one "green") and switching user traffic to the new version only after confirming it is healthy, enabling instant rollback if needed.

Answer: D

Explanation:
A blue-green deployment maintains two fully operational production environments and shifts traffic between them instantly. This approach provides zero downtime during deployment and allows immediate rollback to the previous model version if issues arise, which is critical for high- traffic, business-critical real-time systems.

NEW QUESTION # 158
A Machine Learning Engineer developed a dynamic pricing model in MLflow that requires values from the company's cloud database to generate predictions. At inference time the PyFunc model uses the cloud provider's Python SDK to retrieve the latest values from the database. The inference code works well in a notebook, but when the engineer deploys the model to Databricks Model Serving, they receive 401 errors saying the user is not authenticated when trying to access the database. The engineer deploys the code via the REST API with the following payload:

Their Databricks administrators store cloud credentials under a Databricks secret scope called
"cloud_creds" with key "db_key". These credentials can be used to authenticate to the cloud provider's SDK.
Which change can the engineer make so the endpoint can authenticate to the remote database while avoiding storing the access tokens in plain text?

A. Log the model to MLflow and store {{secrets/cloud_creds/db_key}} as a MLflow secret in
/.mlflow/credentials. Register the model in Unity Catalog. When deployed in Databricks Model Serving, MLflow will automatically authenticate when the model server starts.
B. Retrieve the secret values using a notebook with dbutils.secrets.get(scope="cloud_creds", key="db_key"). Add "environment_vars":
{"DB_ACCESS_TOKEN": ""} to the served_entity. Change the PyFunc model code to read the environment variable and pass the secret to the cloud provider's SDK.
C. Retrieve the secret values using a notebook with dbutils.secrets.get(scope="cloud_creds", key="db_key"). Log the model to MLflow with the access token value as a custom MLflow artifact.
Register the model in Unity Catalog. Change the PyFunc model code to read the artifact and pass the secret to the cloud provider's SDK.
D. Add "environment_vars": {"DB_ACCESS_TOKEN": "{{secrets/cloud_creds/db_key}}"} to the served_entity. Change the PyFunc model code to read the environment variable and pass the secret to the cloud provider's SDK.

Answer: D

Explanation:
Databricks Model Serving supports secure secret injection by referencing Databricks secret scopes directly in the served entity configuration. By mapping the secret to an environment variable using the {{secrets/scope/key}} syntax, the model can securely access the credential at runtime without exposing it in plain text. The PyFunc model can then read the environment variable and authenticate to the cloud provider's SDK, resolving the 401 error while following security best practices.

NEW QUESTION # 159
......

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