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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	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 3	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
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 live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 6	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 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 JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 9	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 10	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 11	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur

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Databricks Certified Machine Learning Professional Sample Questions (Q11-Q16):

NEW QUESTION # 11
A machine learning engineer has developed a machine learning pipeline that produces a scikit- learn model model and computes the RMSE rmse. MAE mae, and R-squared r2 values for the test set. They now want to log these values with the MLflow run. These values are stored in the dictionary metrics.
They run the following code block:

The code block produces an error.
Which changes to the code block will successfully complete the task?

A. Replace metrics with rmse, mae, r2
B. Replace log_metric with log_metrics
C. Replace metrics with model
D. Replace mlflow.log_metric with mlflow.sklearn.log_metric

Answer: B

Explanation:
The method mlflow.log_metric() logs a single metric, while mlflow.log_metrics() is used to log multiple metrics at once from a dictionary. Since metrics is a dictionary containing rmse, mae, and r2, the correct function is mlflow.log_metrics(metrics).

NEW QUESTION # 12
Which of the following is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov-Smirnov (KS) test for numeric feature drift detection?

A. JS is not normalized or smoothed
B. JS does not require any manual threshold or cutoff determinations
C. JS is more robust when working with large datasets
D. All of these reasons
E. None of these reasons

Answer: C

NEW QUESTION # 13
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. The Job ID field in the Jobs page in the UI
B. The Job ID field in the Job details section of the specific Job page in the UI
C. The Job ID field returned in a query using the Jobs API
D. All of these approaches

Answer: D

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 # 14
Which of the following statements describes streaming with Spark as a model deployment strategy?

A. The inference of batch processed records as soon as a trigger is hit
B. The inference of all types of records in real-time
C. The inference of incrementally processed records as soon as a Spark job is run
D. The inference of batch processed records as soon as a Spark job is run
E. The inference of incrementally processed records as soon as trigger is hit

Answer: C

NEW QUESTION # 15
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 spark.read with spark.readStream
B. Replace "inference" with the path to the location of the Delta table
C. Replace schema(schema) with option("maxFilesPerTriqqer", 1}
D. Replace predict with a stream-friendly prediction function
E. Replace formatfdelta") with format("stream")

Answer: C

NEW QUESTION # 16
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