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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 the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 3	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 4	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 5	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
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	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	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift

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Databricks Certified Machine Learning Professional Sample Questions (Q139-Q144):

NEW QUESTION # 139
A machine learning engineer is monitoring label values for a production machine learning classification model. The engineer believes that the relative prevalence of the classes is becoming changing in more recent data. Which tool can the machine learning engineer use to assess their theory?

A. Two-way Chi-squared Test
B. One-way Chi-squared Test
C. Jenson-Shannon distance
D. Kolmogorov-Smirnov (KS) test

Answer: B

Explanation:
A One-way Chi-squared Test is used to compare observed class distributions with expected distributions to determine if there has been a significant shift. This makes it suitable for detecting label drift in classification tasks, where the relative prevalence of classes may be changing over time.

NEW QUESTION # 140
Which of the following is an obstacle related to streaming machine learning applications?

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

Answer: A

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 # 141
A machine learning engineer has developed a random forest model using scikit-learn and registered the model using MLflow. They now want to deploy that model in parallel. Which of the following operations can they use to create a function they can use to deploy the registered scikit- learn model in parallel?

A. mlflow.spark.pandas_udf
B. mlflow.pyfunk.spark.udf
C. It is not possible to deploy a scikit-learn model in parallel.
D. mlflow.sklearn.spark.udf

Answer: D

Explanation:
The correct operation is mlflow.sklearn.spark_udf, which creates a Spark UDF from a registered scikit-learn model. This allows the model to be applied in parallel across a Spark DataFrame, enabling scalable batch inference using Spark.

NEW QUESTION # 142
In order to connect an MLflow Model Registry Webhook to a Databricks Job, the Job ID must be provided to the code block used to create the webhook. Which approach can be used to obtain a Databricks Job ID?

A. All of these approaches
B. The Job ID field in the Jobs page in the UI
C. The Job ID field in the Job details section of the specific Job page in the UI
D. The Job ID field returned in a query using the Jobs API

Answer: A

Explanation:
A Databricks Job ID can be obtained in multiple ways - it is displayed directly in the Jobs page, in the Job details section of a specific Job, and can also be retrieved programmatically through the Databricks Jobs API. Any of these methods can be used to supply the Job ID when configuring an MLflow Model Registry Webhook.

NEW QUESTION # 143
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. 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.
D. 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.

Answer: C

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 # 144
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