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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 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	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 4	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 5	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
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 the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 8	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 9	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
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

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Databricks Certified Machine Learning Professional Sample Questions (Q21-Q26):

NEW QUESTION # 21
A data scientist has developed a scikit-learn random forest model model, but they have not yet logged model with MLflow. They have created a model signature model_signature and a few example input records input_records. Which block of code can be used to log all of this information?

A.
B.
C. There is no way to log a model signature.
D. mlflow.sklearn.log_model(model, "model")
mlflow.log_signature(model_signature)
mlflow.log_input(input_records)

Answer: A

Explanation:
The code block correctly uses the mlflow.sklearn.log_model() function with both signature and input_example parameters.
signature=model_signature defines the expected model input and output schema, ensuring reproducibility and validation during inference.
input_example=input_records provides a sample input that helps users understand the model's expected input format.

NEW QUESTION # 22
A data scientist is using MLflow to track their machine learning experiment. As a part of each MLflow run, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values.
They are using the following code block:

The code block is not nesting the runs in MLflow as they expected.
Which of the following changes does the data scientist need to make to the above code block so that it successfully nests the child runs under the parent run in MLflow?

A. Provide the same name to the run name parameter for all three run blocks
B. Remove the nested=True argument from the child runs
C. Add the nested=True argument to the parent run and remove the nested=True arguments from the child runs
D. Indent the child run blocks within the parent run block
E. Add the nested=True argument to the parent run

Answer: C

NEW QUESTION # 23
A Machine Learning Engineer has a large dataset with a customer_region column and wants to train separate models for each region, then generate predictions. They need to parallelize this group-specific model training process using Databricks and the Pandas Function API. Which approach will implement this solution?

A. Use foreachBatch() to sequentially process each region's data.
B. Use groupBy("customer_region") and apply a training function with applyInPandas().
C. Use mapInPandas() to apply the training function across all partitions without grouping.
D. Use collect() to gather all data and process regions using standard pandas groupby.

Answer: B

Explanation:
The Pandas Function API supports parallel, group-specific processing by using groupBy on the grouping column and applyInPandas to execute a custom training function independently for each group. This enables separate models to be trained per region in parallel across the cluster, with each function invocation receiving only the data for its group.

NEW QUESTION # 24
A data scientist is building a model to predict which communication channel (Phone, SMS, Email, or Post) is most likely to be effective for a given customer. Which model type is suited to this task?

A. ARIMA
B. Logistic Regression
C. Linear Regression
D. Softmax Classifier

Answer: D

Explanation:
This task requires predicting one class from multiple mutually exclusive categories. A softmax classifier is designed for multiclass classification problems and outputs a probability distribution across all possible communication channels, allowing selection of the most likely effective channel.

NEW QUESTION # 25
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. VERSION
B. HISTORY
C. TIMESTAMP
D. DESCRIBE
E. DESCRIBE HISTORY

Answer: E

NEW QUESTION # 26
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