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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 Price:	$200 USD
Exam Duration:	120 minutes
Available Languages:	English
Real Exam Qty:	59
Certificate Validity Period:	2 years
Related Certifications:	Databricks Certified Machine Learning Associate
Exam Format:	Multiple choice, Multi-select
Passing Score:	Not publicly disclosed (approximately 70%)
Recommended Training:	Machine Learning at Scale Advanced Machine Learning Operations
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	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 2	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 3	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 4	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 5	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 6	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 7	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 8	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 9	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 10	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments

Databricks Certified Machine Learning Professional Sample Questions (Q45-Q50):

NEW QUESTION # 45
A Data Scientist is tasked with developing models to forecast product demand. The company offers 5000 different product types, and the Data Scientist must generate weekly forecasts for each type. They have access to two years of historical purchase data and are given ample project budget.
For their next project, they want to build 5000 separate Random Forest models, one for each product type. They aim to train all the models as quickly as possible with minimal setup.
Which approach meets these requirements?

A. Leverage the pandas function API (Grouped map) to group the data by product type and apply a custom model training function to each group.
B. Use the RandomForest method from MLlib. This will leverage Spark's parallel processing capability to train 5000 different models.
C. Create a Databricks Workflow with 5000 tasks. Each task is configured to accept a product ID as a parameter which will then train a model based on the specified product ID.
D. Use the DeepSpeed library to distribute the data by product across different nodes to enable the parallel training of multiple models.

Answer: A

Explanation:
The pandas function API with grouped map allows data to be grouped by product type and applies a custom training function independently to each group. This approach enables massive parallelism across the cluster with minimal orchestration or setup, making it well suited for rapidly training thousands of independent models in parallel.

NEW QUESTION # 46
A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable.
They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df. Which of the following code blocks can they use to create a function called predict that they can use to complete the task?

A.
B.
C. It is not possible to deploy a scikit-learn model on a Spark DataFrame.
D.
E.

Answer: E

NEW QUESTION # 47
A machine learning engineer has developed a model and registered it using the FeatureStoreClient fs. The model has model URI model_uri. The engineer now needs to perform batch inference on customer-level Spark DataFrame spark_df, but it is missing a few of the static features that were used when training the model. The customer_id column is the primary key of spark_df and the training set used when training and logging the model.
Which of the following code blocks can be used to compute predictions for spark_df when the missing feature values can be found in the Feature Store by searching for features by customer_id?

A. df = fs.get_missing_features(spark_df, model_uri)
fs.score_model(model_uri, df)
B. fs.score_batch(model_uri, spark_df)
C. df = fs.get_missing_features(spark_df)
fs.score_batch(model_uri, df)
D. df = fs.get_missing_features(spark_df, model_uri)
fs.score_batch(model_uri, df)
E. fs.score_model(model_uri, spark_df)

Answer: B

NEW QUESTION # 48
Which MLflow operation can be used to log a plot that was generated during an MLflow run?

A. mlflow.log_figure
B. None of these operations can accomplish the task.
C. mlflow.log_metric
D. mlflow.log_visualization

Answer: A

Explanation:
The mlflow.log_figure() operation is used to log plots or figures generated during an MLflow run. It takes a Matplotlib, Plotly, or similar figure object and saves it as an artifact in the MLflow tracking server, enabling visualization and later retrieval from the MLflow UI.

NEW QUESTION # 49
Which is a benefit of logging an input example with an MLflow model?

A. An example record is available to test serving applications
B. The model evaluation metrics are stored with the model
C. An example prediction is available to test post-inference pipelines
D. The environment requirements are packaged with the MLmodel file

Answer: A

Explanation:
Logging an input example with an MLflow model provides a concrete sample of the data format expected by the model. This helps serving applications and users understand the model's input structure and verify that inference requests are properly formatted when deploying or testing the model in production.

NEW QUESTION # 50
......

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