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Databricks Databricks-Machine-Learning-Professional Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Machine Learning Professional
Exam Number:	Databricks-Machine-Learning-Professional
Passing Score:	Not publicly disclosed
Exam Duration:	120 minutes
Related Certifications:	Databricks Certified Machine Learning Associate
Exam Format:	Multiple choice, Multiple select, Scenario-based questions
Exam Price:	$200 USD
Certificate Validity Period:	2 years
Available Languages:	English
Real Exam Qty:	Approximately 45–60
Recommended Training:	Databricks Academy Machine Learning Training
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:	Online proctored exam (typically delivered via Databricks certification partners such as Certiverse or Pearson VUE depending on region and current program structure)
Pre Condition:	Recommended experience with Databricks platform and machine learning workflows; Databricks Certified Machine Learning Associate certification is often recommended but not strictly required.
Official Syllabus URL:	https://www.databricks.com/learn/certification

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

Topic	Details
Topic 1	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
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	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 4	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 5	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 6	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 7	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 8	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 9	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 10	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 11	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook

Databricks Certified Machine Learning Professional Sample Questions (Q89-Q94):

NEW QUESTION # 89
A machine learning engineer is using the following code block as part of a batch deployment pipeline:

Which of the following changes needs to be made so this code block will work when the inference table is a stream source?

A. Replace predict with a stream-friendly prediction function
B. Replace formatfdelta") with format("stream")
C. Replace "inference" with the path to the location of the Delta table
D. Replace spark.read with spark.readStream
E. Replace schema(schema) with option("maxFilesPerTriqqer", 1}

Answer: E

NEW QUESTION # 90
A Machine Learning Engineer has a production AI model that predicts fraudulent transactions in real time. This model is integrated into critical business workflows and any downtime could result in significant financial loss and regulatory penalties. The engineer needs to deploy a new, improved version of the model to production to replace the existing model. The bank's fairness policy requires that all transactions be exposed to the same model at any given time. Additionally, the deployment must meet the following requirements:
- Zero downtime: The fraud detection service must remain continuously
available to users and downstream systems.
- Immediate rollback: If the new model causes issues, you must be able
to revert to the previous version instantly.
Which deployment strategy meets these requirements?

A. Shadow
B. Canary
C. Blue-Green
D. Rolling

Answer: C

Explanation:
Blue-green deployment maintains two fully operational production environments and switches all traffic from the old model to the new model at once. This guarantees zero downtime and ensures that all transactions are handled by the same model version at any given time. If issues arise, traffic can be immediately switched back to the previous environment, enabling instant rollback while meeting strict fairness and availability requirements.

NEW QUESTION # 91
A data scientist wants to remove the star_rating column from the Delta table at the location path.
To do this, they need to load in data and drop the star_rating column. Which of the following code blocks accomplishes this task?

A. spark.read.format("delta").table(path).drop("star_rating")
B. spark.read.format("delta").load(path).drop("star_rating")
C. spark.sql("SELECT * EXCEPT star_rating FROM path")
D. spark.read.table(path).drop("star_rating")
E. Delta tables cannot be modified

Answer: D

NEW QUESTION # 92
A machine learning engineering team wants to build a continuous pipeline for data preparation of a machine learning application. The team would like the data to be fully processed and made ready for inference in a series of equal-sized batches.
Which of the following tools can be used to provide this type of continuous processing?

A. [Structured Streaming
B. AutoML
C. MLflow
D Delta Lake
D. Spark UDFs

Answer: D

NEW QUESTION # 93
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. 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.
B. 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.
C. 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.
D. 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.

Answer: B

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 # 94
......

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