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

Topic	Details
Topic 1	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 2	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 3	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 4	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 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	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 7	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 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 which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 10	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

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Databricks Certified Machine Learning Professional Sample Questions (Q56-Q61):

NEW QUESTION # 56
What tool helps avoid feature mismatch between training and inference?

A. Spark SQL
B. Model Registry
C. MLflow Projects
D. Feature Store

Answer: D

Explanation:
Feature Store ensures:
same feature computation logic
consistency between training and serving.

NEW QUESTION # 57
Which statement describes streaming with Spark as a model deployment strategy?

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

Answer: C

NEW QUESTION # 58
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.sklearn.log_model(sklearn_model, "model")
C. mlflow.sklearn.track_model(sklearn_model, "model")
D. mlflow.sklearn.load_model("model")
E. mlflow.spark.log_model(sklearn_model, "model")

Answer: A

NEW QUESTION # 59
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 "inference" with the path to the location of the Delta table
B. Replace spark.read with spark.readStream
C. Replace predict with a stream-friendly prediction function
D. Replace schema(schema) with option("maxFilesPerTriqqer", 1}
E. Replace formatfdelta") with format("stream")

Answer: D

NEW QUESTION # 60
A data scientist is utilizing MLflow to track their machine learning experiments. After completing a series of runs for the experiment with experiment ID exp_id, the data scientist wants to programmatically work with the experiment run data in a Spark DataFrame. They have an active MLflow Client client and an active Spark session spark.
Which of the following lines of code can be used to obtain run-level results for exp_id in a Spark DataFrame?

A. client.list_run_infos(exp_id)
B. There is no way to programmatically return row-level results from an MLflow Experiment.
C. spark.read.format("delta").load(exp_id)
D. spark.read.format("mlflow-experiment").load(exp_id)
E. mlflow.search_runs(exp_id)

Answer: C

NEW QUESTION # 61
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