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

Topic	Details
Topic 1	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 2	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
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 less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 5	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 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	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 8	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 9	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines

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Databricks-Machine-Learning-Professional Valid Exam Answers | Databricks-Machine-Learning-Professional Most Reliable Questions

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

NEW QUESTION # 21
A data scientist set up a machine learning pipeline to automatically log a data visualization with each run. They now want to view the visualizations in Databricks. Which location in Databricks will show these data visualizations?

A. The Figures section of the MLflow Run page
B. The MLflow Model Registry Model paqe
C. Logged data visualizations cannot be viewed in Databricks
D. The Artifacts section of the MLflow Run page
E. The Artifacts section of the MLflow Experiment page

Answer: A

NEW QUESTION # 22
A data scientist has developed a scikit-learn random forest model model, but they have not yet logged model with MLflow. They want to obtain the input schema and the output schema of the model so they can document what type of data is expected as input.
Which of the following MLflow operations can be used to perform this task?

A. mlflow.models.schema.infer_schema
B. mlflow.models.Model.signature
C. mlflow.models.Model.get_input_schema
D. There is no way to obtain the input schema and the output schema of an unlogged model.
E. mlflow.models.signature.infer_signature

Answer: A

NEW QUESTION # 23
What tool helps avoid feature mismatch between training and inference?

A. Model Registry
B. Spark SQL
C. Feature Store
D. MLflow Projects

Answer: C

Explanation:
Feature Store ensures:
same feature computation logic
consistency between training and serving.

NEW QUESTION # 24
In a continuous integration, continuous deployment (CI/CD) process for machine learning pipelines, which of the following events commonly triggers the execution of automated testing?

A. The launch of a new cost-efficient job cluster
B. The launch of a new cost-efficient SQL endpoint
C. CI/CD pipelines are not needed for machine learning pipelines
D. The arrival of a new feature table in the Feature Store
E. The arrival of a new model version in the MLflow Model Registry

Answer: A

NEW QUESTION # 25
A retail company wants to better forecast their sales of each SKU in every store in order to more accurately distribute their products. To achieve this, a Data Scientist proposes scaling their existing forecasting model to forecast individually for each combination of SKU and Store ID.
They have a cluster with 12 executors available in order to execute this. The current model is written using Pandas and the Prophet library for forecasting, with a Python function that receives a Pandas Data Frame with historic sales data as a parameter to train the forecasting model. For their next iteration, they want to improve the efficiency of this approach while using the least amount of effort and leveraging all available resources. Which approach will do this?

A. 1. Rewrite the function using the Generalized Linear Regression models available in Spark MLlib, which allows parameterizing to reflect a similar behavior to that of Prophet;
2. Perform a GROUP BY on the Spark DataFrame with the training data to group by Store and SKU;
3. Apply the Spark UDF to the grouped DataFrame with the apply function.
B. 1. Use a Spark query to identify all combinations of SKU and Store ID;
2. Run a loop through every combination calling the existing Python function using Spark repartition(12) function so it distributes across all available executors.
C. 1. Use Spark repartition(12) to scale across all executors;
2. Run to Pandas to convert the repartitioned DataFrame to Pandas so it can run on the original existing function;
3. Add the Store ID and SKU to the Prophet model as exogenous variables.
D. 1. Decorate the existing training function with the @pandas_udf;
2. On the Spark DataFrame with all training data perform a GROUP BY by Store and SKU;
3. Apply the decorated function to the grouped DataFrame with the apply function.

Answer: D

Explanation:
A grouped Pandas UDF lets you keep the existing Prophet-based pandas training function while distributing execution across the cluster. Grouping the Spark DataFrame by SKU and Store ID and applying the function runs one model per group in parallel on the executors, improving efficiency and leveraging all available resources with minimal refactoring.

NEW QUESTION # 26
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