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Databricks Databricks-Machine-Learning-Professional Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Machine Learning Professional Exam
Exam Number:	Databricks-Machine-Learning-Professional
Exam Format:	Multi-select, Multiple choice
Passing Score:	Not publicly disclosed (approximately 70%)
Exam Duration:	120 minutes
Real Exam Qty:	59
Certificate Validity Period:	2 years
Related Certifications:	Databricks Certified Machine Learning Associate
Available Languages:	English
Exam Price:	$200 USD
Recommended Training:	Advanced Machine Learning Operations Machine Learning at Scale
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:	Online proctored or in-person test center
Pre Condition:	No mandatory prerequisites; recommended: 6+ months hands-on experience with Databricks ML, SparkML, MLflow, and Python
Official Syllabus URL:	https://www.databricks.com/learn/certification/machine-learning-professional

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

Topic	Details
Topic 1	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 2	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 3	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 4	Create, overwrite, merge, and read Feature Store tables in machine learning workflows View Delta table history and load a previous version of a Delta table
Topic 5	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
Topic 6	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 7	Test whether the updated model performs better on the more recent data Identify when retraining and deploying an updated model is a probable solution to drift

Databricks Certified Machine Learning Professional Sample Questions (Q157-Q162):

NEW QUESTION # 157
A Machine Learning Engineer needs to deploy a production ML workflow that includes an MLflow experiment for tracking model training runs, a registered model in Unity Catalog for version management, and a model serving endpoint for real-time inference. The team requires a unified configuration approach that ensures consistent deployment across development and production environments while adhering to infrastructure-as-code best practices. Which approach should the Machine Learning Engineer use to define all three components together?

A. Create separate REST API calls for each component and run them sequentially using a shell script.
B. Use Terraform to provision the infrastructure and then manually configure each ML component through the Databricks UI.
C. Define experiments, registered_models, and model_serving_endpoints resources in a Databricks Asset Bundle (DAB) configuration file.
D. Use MLflow's deployment tools to create individual deployment configurations for each component, then orchestrate them using Databricks Jobs.

Answer: C

Explanation:
Databricks Asset Bundles allow experiments, Unity Catalog-registered models, and model serving endpoints to be defined declaratively in a single configuration. This provides a unified, version-controlled, infrastructure-as-code approach that ensures consistent deployment across environments and aligns with MLOps best practices.

NEW QUESTION # 158
A machine learning engineer is in the process of implementing a concept drift monitoring solution. They are planning to use the following steps:
1. Deploy a model to production and compute predicted values
2. Obtain the observed (actual) label values
3. _____
4. Run a statistical test to determine if there are changes over time
Which of the following should be completed as Step #3?

A. Obtain the observed values (actual) feature values
B. Compute the evaluation metric using the observed and predicted values
C. None of these should be completed as Step #3
D. Measure the latency of the prediction time
E. Retrain the model

Answer: C

NEW QUESTION # 159
A data scientist has developed and logged a scikit-learn random forest model model, and then they ended their Spark session and terminated their cluster. After starting a new cluster, they want to review the feature_importances_ of the original model object.
Which of the following lines of code can be used to restore the model object so that feature_importances_ is available?

A. This can only be viewed in the MLflow Experiments UI
B. client.pyfunc.load_model(model_uri)
C. client.list_artifacts(run_id)["feature-importances.csv"]
D. mlflow.load_model(model_uri)
E. mlflow.sklearn.load_model(model_uri)

Answer: D

NEW QUESTION # 160
A machine learning engineer has implemented a numeric drift monitoring solution by examining trends in the summary statistics of input variables. However, the engineer's stakeholders would like a more robust monitoring solution. Which of the following can provide a more robust drift monitoring solution for numeric feature variables?

A. Oversampling
B. None of these can provide more robust drift monitoring than summary statistics
C. Correlations
D. Statistical tests

Answer: D

Explanation:
Statistical tests (such as the Kolmogorov-Smirnov test or Wasserstein distance) provide a more robust and quantitative method for detecting numeric feature drift compared to simple summary statistics. These tests compare the full distributions of features between datasets, making them more sensitive to subtle changes in data behavior over time.

NEW QUESTION # 161
A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. They have programmatically identified the best run from an MLflow Experiment and stored its URI in the model_uri variable and its Run ID in the run_id variable. They have also determined that the model was logged with the name "model". Now, the machine learning engineer wants to register that model in the MLflow Model Registry with the name "best_model".
Which line of code can they use to register the model to the MLflow Model Registry?

A. mlflow.register_model(run_id, "best_model")
B. mlflow.register_model(model_uri, "best_model")
C. mlflow.register_model(f"runs:/{run_id}/best_model", "model")
D. mlflow.register_model(f"runs:/{run_id}/model")
E. mlflow.register_model(model_uri, "model")

Answer: B

NEW QUESTION # 162
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