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

Topic	Details
Topic 1	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
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 the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
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 JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 7	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 8	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 9	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 10	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 (Q108-Q113):

NEW QUESTION # 108
A data scientist has computed updated feature values for all primary key values stored in the Feature Store table features. In addition, feature values for some new primary key values have also been computed. The updated feature values are stored in the DataFrame features_df. They want to replace all data in features with the newly computed data. Which of the following code blocks can they use to perform this task using the Feature Store Client fs?

Answer: D

NEW QUESTION # 109
A Machine Learning Engineer is setting up a cluster for a deep learning training run, but has a number of settings options to choose from. Their cluster will be reused by other engineers on their team and their datasets vary in size from hundreds of MBs to hundreds of GBs. They need to choose a configuration that allows for performant, stable deep learning training without excessive costs. Which configuration will do this?

A. Using Databricks Runtime, use a moderately sized CPU VM for the driver and a variable number of GPU enabled VMs for the workers with autoscale enabled
B. Using ML Runtime, use a moderate sized GPU VM for the driver and a variable number of memory optimized CPU VMs for the workers with autoscale enabled
C. Using ML Runtime, use a large single node, GPU-enabled VM with auto termination set to 20 minutes to reduce costs
D. Using ML Runtime, use a moderately sized CPU VM for the driver and a variable number of GPU enabled VMs for the workers with autoscale enabled

Answer: D

Explanation:
Using the ML Runtime ensures deep learning frameworks and GPU drivers are preconfigured and optimized. A moderately sized CPU driver is sufficient for coordination, while GPU-enabled worker nodes handle the computationally intensive training workload. Enabling autoscaling allows the cluster to efficiently adapt to datasets ranging from hundreds of megabytes to hundreds of gigabytes, providing strong performance without overprovisioning and controlling costs in a shared team environment.

NEW QUESTION # 110
A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable.
They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df. Which of the following code blocks can they use to create a function called predict that they can use to complete the task?

A.
B.
C. It is not possible to deploy a scikit-learn model on a Spark DataFrame.
D.
E.

Answer: E

NEW QUESTION # 111
Which of the following MLflow operations can be used to automatically calculate and log a Shapley feature importance plot?

A. client.log_artifact
B. None of these operations can accomplish the task.
C. mlflow.log_figure
D. mlflow.shap
E. mlflow.shap.log_explanation

Answer: D

NEW QUESTION # 112
What is the main purpose of the Databricks Feature Store?

A. Replacing Spark ML pipelines
B. Centralizing reusable ML features
C. Storing raw data
D. Storing trained models

Answer: B

Explanation:
Feature Store allows teams to:
share features
avoid training/serving skew
maintain feature lineage.

NEW QUESTION # 113
......

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