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

Topic	Details
Topic 1	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 2	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 3	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 4	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 5	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 6	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 7	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 8	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment

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Databricks Certified Machine Learning Professional Sample Questions (Q29-Q34):

NEW QUESTION # 29
A data scientist wants to remove the star_rating column from the Delta table at the location path.
To do this, they need to load in data and drop the star_rating column. Which of the following code blocks accomplishes this task?

A. Delta tables cannot be modified
B. spark.read.format("delta").load(path).drop("star_rating")
C. spark.read.format("delta").table(path).drop("star_rating")
D. spark.sql("SELECT * EXCEPT star_rating FROM path")
E. spark.read.table(path).drop("star_rating")

Answer: E

NEW QUESTION # 30
Which MLflow function registers a model to the registry?

A. mlflow.create_model()
B. mlflow.deploy_model()
C. mlflow.register_model()
D. mlflow.log_param()

Answer: C

Explanation:
mlflow.register_model() promotes a model into the Model Registry.

NEW QUESTION # 31
A data scientist wants to track the runs of their random forest model. The data scientist is changing the number of trees and the maximum depth of the trees in the forest across each run.
They write the following code block:

Which Python object type does params need to be an instance of?

A. PySpark DataFrarne
B. dict
C. array
D. list

Answer: B

Explanation:
The params variable must be a dictionary (dict) because mlflow.log_params() expects a dictionary where each key-value pair represents a parameter name and its corresponding value.
Additionally, the model instantiation RandomForestRegressor(**params) also requires params to be a dictionary to unpack the parameters correctly.

NEW QUESTION # 32
Which of the following MLflow Model Registry use cases requires the use of an HTTP Webhook?

A. Starting a testing job when a new model is registered
B. Sending an email alert when an automated testing Job fails
C. Updating data in a source table for a Databricks SQL dashboard when a model version transitions to the Production stage
D. None of these use cases require the use of an HTTP Webhook
E. Sending a message to a Slack channel when a model version transitions stages

Answer: C

NEW QUESTION # 33
A Data Scientist is using Spark ML to train a model for detecting fraudulent transactions (1:
fraudulent, 0: non-fraudulent). To maximize the model's overall AUC (ROC) score, the Data Scientist wants to perform hyperparameter tuning on a Random Forest classifier. Due to budget constraints, the tuning process must be completed efficiently to avoid excessive compute costs.
Which approach will fulfill their needs?

Answer: B

Explanation:
TrainValidationSplit evaluates each hyperparameter combination using a single train/validation split, which is significantly more compute-efficient than k-fold cross-validation. Using a BinaryClassificationEvaluator aligns directly with optimizing AUC (ROC) for a binary fraud detection task, making this approach well suited for maximizing model performance under budget and compute constraints.

NEW QUESTION # 34
......

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