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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 the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
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 less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 5	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 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 that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
Topic 8	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 9	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 (Q94-Q99):

NEW QUESTION # 94
A machine learning engineer has deployed a model recommender using MLflow Model Serving.
They now want to query the version of that model that is in the Production stage of the MLflow Model Registry. Which of the following model URls can be used to query the described model version?

A. https:///model/recommender/stage-production/invocations
B. The version number of the model version in Production is necessary to complete this task.
C. https:///model-serving/recommender/Production/invocations
D. https:///model/recommender/Production/invocations

Answer: D

Explanation:
In MLflow Model Serving, the correct URL pattern to query a specific stage of a registered model is https:///model/<model_name>/<stage>/invocations Thus, to query the model named recommender in the Production stage, the proper endpoint is
https:///model/recommender/Production/invocations.
This allows direct access to the active production version without needing its version number.

NEW QUESTION # 95
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").load(path).drop("star_rating")
B. spark.read.format("delta").table(path).drop("star_rating")
C. spark.sql("SELECT * EXCEPT star_rating FROM path")
D. Delta tables cannot be modified
E. spark.read.table(path).drop("star_rating")

Answer: E

NEW QUESTION # 96
A data scientist has developed a scikit-learn model sklearn_model and they want to log the model using MLflow.
They write the following incomplete code block:

Which of the following lines of code can be used to fill in the blank so the code block can successfully complete the task?

A. mlflow.spark.track_model(sklearn_model, "model")
B. mlflow.spark.log_model(sklearn_model, "model")
C. mlflow.sklearn.log_model(sklearn_model, "model")
D. mlflow.sklearn.load_model("model")
E. mlflow.sklearn.track_model(sklearn_model, "model")

Answer: A

NEW QUESTION # 97
A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client.
Which of the following code blocks can they use to accomplish the task?

Answer: D

NEW QUESTION # 98
A data scientist wants to log outlier feature data from a CSV file at path outlier_path with an MLflow run for model model. Which code block will accomplish this task inside of an existing MLflow run block?

A.
B.
C. mlflow.log_data(outlier_path, "outlier-features.csv")
D. mlflow.log_artifact(outlier_path, "outlier-features.csv")

Answer: D

Explanation:
To log external files, such as a CSV containing outlier feature data, MLflow provides the mlflow.log_artifact() function. This function uploads the specified file or directory (outlier_path) as an artifact under the provided artifact path ("outlier-features.csv"). It is the correct way to associate data files with an MLflow run, whereas mlflow.log_model() is reserved for logging model objects, not arbitrary data.

NEW QUESTION # 99
......

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