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

Topic	Details
Topic 1	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 2	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 3	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 4	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 5	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 6	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
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
Topic 8	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
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 the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors

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Databricks Certified Machine Learning Professional Sample Questions (Q114-Q119):

NEW QUESTION # 114
After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set.
Which of the following SQL commands can be used to accomplish this task?

A. HISTORY
B. DESCRIBE HISTORY
C. TIMESTAMP
D. VERSION
E. DESCRIBE

Answer: B

NEW QUESTION # 115
Which of the following is an advantage of using the python_function(pyfunc) model flavor over the built-in library-specific model flavors?

A. python_function can be used to deploy models without worrying about whether they are deployed in batch, streaming, or real-time environments
B. python_function can be used to deploy models in a parallelizable fashion
C. python_function can be used to store models in an MLmodel file
D. python_function provides no benefits over the built-in library-specific model flavors
E. python_function can be used to deploy models without worrying about which library was used to create the model

Answer: B

NEW QUESTION # 116
A machine learning engineer is working on a fraud detection machine learning application. When a transaction is made with a credit card, the machine learning application will immediately process the data and make a prediction to determine whether or not to approve the transaction based on the probability that the transaction is fraudulent. Which deployment strategy can be used to meet these requirements?

A. Real-time
B. Edge/on-device
C. Batch
D. Streaming

Answer: A

Explanation:
Real-time deployment is required for applications that must provide immediate predictions, such as fraud detection during a credit card transaction. This ensures that the system can instantly assess risk and approve or deny the transaction without delay.

NEW QUESTION # 117
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. Retrain the model
B. Measure the latency of the prediction time
C. Compute the evaluation metric using the observed and predicted values
D. Obtain the observed values (actual) feature values
E. None of these should be completed as Step #3

Answer: E

NEW QUESTION # 118
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 rdd.mapPartitions() to iterate through each store and apply the model logic in Python.
B. Convert the DataFrame to a Pandas DataFrame using .toPandas() and loop through each store locally.
C. Use groupBy("store_id").applyInPandas() to train and apply the model per group using a Pandas UDF.
D. Use pyspark.pandas to call .groupby("store_id").apply() and train the model on each group.

Answer: C

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 # 119
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