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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	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 3	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 4	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 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	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur

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Databricks Certified Machine Learning Professional Sample Questions (Q111-Q116):

NEW QUESTION # 111
A Machine Learning Engineer is working with a Spark DataFrame containing 100 million rows of retail transactions across thousands of stores. Each store requires its own demand forecasting model using the same scikit-learn pipeline. They want to train and apply these models in parallel for each store without collecting the data to the driver. Which approach will do this?

A. Use groupBy("store_id").applyInPandas() to train and apply the model per group using a Pandas UDF.
B. Convert the DataFrame to a Pandas DataFrame using .toPandas() and loop through each store locally.
C. Use pyspark.pandas to call .groupby("store_id").apply() and train the model on each group.
D. Use rdd.mapPartitions() to iterate through each store and apply the model logic in Python.

Answer: A

Explanation:
groupBy().applyInPandas() enables grouped Pandas UDFs that receive each store's data as a Pandas DataFrame on the executors. This allows training and applying a separate scikit-learn model per store in parallel without collecting data to the driver, making it the correct and scalable approach for large Spark DataFrames.

NEW QUESTION # 112
Which is a benefit of logging an input example with an MLflow model?

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

Answer: D

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 # 113
A machine learning engineer has registered a Spark ML model in the MLflow Model Registry using the Spark ML model flavor with UI model_uri. Which operation can be used to load the model as a Spark ML object for batch deployment?

A. mlflow.sklearn.load_model(model_uri)
B. mlflow.spark.read_model(model_uri)
C. mlflow.spark.load_model(model_uri)
D. mlflow.pyfunc.load_model(model_uri)

Answer: C

Explanation:
To load a model that was logged using the Spark ML flavor, the correct operation is mlflow.spark.load_model(model_uri). This restores the model as a Spark ML object, preserving its original structure and functionality for batch inference or further processing in Spark environments.

NEW QUESTION # 114
A data scientist has developed a model to predict whether or not it will rain using the expected temperature and expected cloud coverage. However, the proportion of days where it actually rains has increased dramatically from the proportion in the data on which the model was trained.
Which type of drift is present in the above scenario?

A. Label drift
B. Feature drift
C. Prediction drift
D. Concept drift

Answer: A

Explanation:
Label drift occurs when the distribution of the target variable (label) changes over time while the relationship between features and the label remains the same. In this scenario, the proportion of days when it rains (the label) has changed significantly compared to the training data, indicating label drift.

NEW QUESTION # 115
A data scientist has run the following line of code to create a Feature Store table:

Which statement correctly describes the data in the Feature Store table new_table?

A. The data in new_table is exactly the same as the data in df
B. The data in new_table is the customer-related features from df
C. The data in new_table is the values in column customer_id from df
D. There is no data in new_table

Answer: D

Explanation:
The fs.create_table() command defines metadata for a Feature Store table, such as its name, primary key, schema, and description - but it does not insert any data into the table. This command simply registers an empty table structure based on the provided schema (df.schema).
To populate the table with data, a separate fs.write_table() or equivalent write operation must be executed afterward.

NEW QUESTION # 116
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