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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	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 3	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 4	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 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	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 7	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 8	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 9	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 10	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 11	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow

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Databricks Certified Machine Learning Professional Sample Questions (Q182-Q187):

NEW QUESTION # 182
A machine learning engineer wants to log feature importance data from a CSV file at path importance_path with an MLflow run for model model.
Which of the following code blocks will accomplish this task inside of an existing MLflow run block?
A)

B)

C) mlflow.log_data(importance_path, "feature-importance.csv")
D) mlflow.log_artifact(importance_path, "feature-importance.csv")
E) None of these code blocks tan accomplish the task.

A. Option C
B. Option A
C. Option E
D. Option D
E. Option B

Answer: B

NEW QUESTION # 183
A data scientist would like to enable MLflow Autologging for all machine learning libraries used in a notebook. They want to ensure that MLflow Autologging is used no matter what version of the Databricks Runtime for Machine Learning is used to run the notebook and no matter what workspace-wide configurations are selected in the Admin Console. Which of the following lines of code can they use to accomplish this task?

A. mlflow.spark.autolog()
B. mlflow.sklearn.autolog()
C. It is not possible to automatically log MLflow runs.
D. spark.conf.set("autologging", True)
E. mlflow.autolog()

Answer: D

NEW QUESTION # 184
A Machine Learning Engineer needs a continuous deployment pipeline for their models hosted on Databricks Model Serving. The deployment automation should execute after a model is trained and registered using MLflow. The goal of the automation is to deploy the latest version of the model from the MLflow Model Registry only if the model can meet the company's strict latency requirements (P95 < 300ms) while serving production traffic. How can the engineer validate that new models meet their latency requirements when served in production?

A. Serve the latest model on Databricks Model Serving and use a load testing client to generate requests at the production request rate. Use the load testing client to calculate P95 latency and verify it is less than 300ms.
B. A/B test the latest model with Databricks Model Serving so that the latest model receives 5% of production traffic and the current model receives the rest. Use inference tables to calculate P95 latency and verify it is less than 300ms.
C. Use the MLflow Get Run API to retrieve the model metrics from MLflow Tracking and verify that the model_latency metric is less than 300ms.
D. Use the MLflow Get Run API to retrieve the model metrics from MLflow Tracking and verify that the inference_latency metric is less than 300ms.

Answer: B

Explanation:
Validating latency requirements must be done under real production serving conditions. Routing a small percentage of live production traffic to the new model using Databricks Model Serving allows accurate measurement of end-to-end serving latency. Inference tables capture request latency metrics, enabling calculation of the P95 latency and ensuring it meets the strict production threshold before full rollout.

NEW QUESTION # 185
Which of the following is an obstacle related to streaming machine learning applications?

A. None of these
B. End-to-end fault tolerance
C. Out-of-order data
D. All of these

Answer: D

Explanation:
Streaming machine learning applications face multiple challenges, including end-to-end fault tolerance (ensuring recovery from failures without data loss) and out-of-order data (handling events that arrive late or out of sequence). Both are common obstacles in building reliable real- time ML systems.

NEW QUESTION # 186
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.
D. It is not possible to deploy a scikit-learn model on a Spark DataFrame.
E.

Answer: E

NEW QUESTION # 187
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