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Databricks Databricks-Machine-Learning-Professional Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Machine Learning Professional
Exam Number:	Databricks-Machine-Learning-Professional
Passing Score:	Not publicly disclosed
Exam Format:	Multiple select, Multiple choice, Scenario-based questions
Real Exam Qty:	Approximately 45–60
Related Certifications:	Databricks Certified Machine Learning Associate
Certificate Validity Period:	2 years
Exam Price:	$200 USD
Available Languages:	English
Exam Duration:	120 minutes
Recommended Training:	Databricks Academy Machine Learning Training
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:	Online proctored exam (typically delivered via Databricks certification partners such as Certiverse or Pearson VUE depending on region and current program structure)
Pre Condition:	Recommended experience with Databricks platform and machine learning workflows; Databricks Certified Machine Learning Associate certification is often recommended but not strictly required.
Official Syllabus URL:	https://www.databricks.com/learn/certification

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

Topic	Details
Topic 1	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
Topic 2	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 3	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 4	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 5	Identify a use case for HTTP webhooks and where the Webhook URL needs to come Identify advantages of using Job clusters over all-purpose clusters
Topic 6	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 7	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 8	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 9	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 10	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook

Databricks Certified Machine Learning Professional Sample Questions (Q103-Q108):

NEW QUESTION # 103
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 of the following lines of code can they use to register the model to the MLflow Model Registry?

A. mlflow.register_model(model_uri, "best_model")
B. mlflow.register_model(model_uri, "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(run_id, "best_model")

Answer: B

NEW QUESTION # 104
A Data Scientist is building a propensity model for an e-commerce start-up. The company maintains 7GB of historical data and receives about 5MB of new transaction data daily. The goal is to generate daily purchase predictions for all users by 7:00 AM each morning. As the start-up is in its early stages, the data scientist must prioritize a highly cost-efficient approach. Which approach should the Data Scientist take?

A. Use a single-node memory compute to build a model with libraries like scikit-learn. The model can then be scheduled as a nightly batch job that runs on a single-node compute.
B. Use a single-node compute to build a model with libraries like scikit-learn. The model can then be integrated into an always-on streaming pipeline, to ensure immediate processing of incoming data in order to meet the SLA.
C. Use a single-node compute to build a model with libraries like scikit-learn. The model can then be deployed as an always-on REST API so that users can query the API to get the predictions whenever they want.
D. Use a multi-node compute and leverage distributed frameworks like SparkML to train the model.
The model can then be scheduled as a nightly batch job that runs on a multi-node compute.

Answer: A

Explanation:
With only 7GB of historical data and a small daily increment (about 5MB), a single-node memory- optimized cluster can comfortably train and score using scikit-learn without the overhead and cost of distributed compute. Scheduling a nightly batch job is the most cost-efficient way to meet a fixed daily SLA (7:00 AM) because the compute can be started only for the job run and then terminated, avoiding the expense of always-on serving or streaming infrastructure.

NEW QUESTION # 105
A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or computing predictions using that model. They decide to wrap this preprocessing in a custom model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as a pyfunc model.
Which of the following is a benefit of this approach when loading the logged pyfunc model for downstream deployment?

A. There is no longer a need for pipeline-like machine learning objects
B. The pvfunc model can be used to deploy models in a parallelizable fashion
C. This approach has no impact when loading the logged Pvfunc model for downstream deployment
D. The same preprocessing logic will automatically be applied when calling fit
E. The same preprocessing logic will automatically be applied when calling predict

Answer: A

NEW QUESTION # 106
A Data Scientist is preparing a Spark ML pipeline on a customer dataset with numeric features age, annual_income, and transaction_count, each varying widely. Because the chosen algorithm requires inputs normalized to the [0,1] range, they need to apply the appropriate Spark ML transformer to these features. Which Spark ML transformer should the Data Scientist use to scale all features to the [0,1] range?

A. RobustScaler
B. MinMaxScaler
C. StandardScaler
D. MaxAbsScaler

Answer: B

Explanation:
MinMaxScaler rescales each numeric feature to a fixed range, typically [0, 1], by subtracting the minimum value and dividing by the feature's range. This makes it the appropriate Spark ML transformer when an algorithm explicitly requires inputs normalized to the [0,1] interval.

NEW QUESTION # 107
A machine learning engineering manager has asked all of the engineers on their team to add text descriptions to each of the model projects in the MLflow Model Registry. They are starting with the model project "model" and they'd like to add the text in the model_description variable.
The team is using the following line of code:

Which of the following changes does the team need to make to the above code block to accomplish the task?

A. Add a Python model as an argument to update_registered_model
B. Replace description with artifact
C. There no changes necessary
D. Replace client.update_registered_model with mlflow
E. Replace update_registered_model with update_model_version

Answer: C

NEW QUESTION # 108
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