Databricks-Machine-Learning-Professional Testking, Databricks-Machine-Learning-Professional Fragen&Antworten

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Databricks Databricks-Machine-Learning-Professional Prüfungsplan:

Thema	Einzelheiten
Thema 1	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Thema 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
Thema 3	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Thema 4	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Thema 5	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Thema 6	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
Thema 7	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Thema 8	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Thema 9	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Thema 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-Machine-Learning-Professional Fragen&Antworten, Databricks-Machine-Learning-Professional Probesfragen

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Databricks Certified Machine Learning Professional Databricks-Machine-Learning-Professional Prüfungsfragen mit Lösungen (Q133-Q138):

133. Frage
A Machine Learning Engineer is working with a Spark DataFrame containing 100 million rows of retail transactions across thousands of stores. Each store requires its own demand forecasting model using the same scikit-learn pipeline. They want to train and apply these models in parallel for each store without collecting the data to the driver. Which approach will do this?

A. Convert the DataFrame to a Pandas DataFrame using .toPandas() and loop through each store locally.
B. Use groupBy("store_id").applyInPandas() to train and apply the model per group using a Pandas UDF.
C. Use pyspark.pandas to call .groupby("store_id").apply() and train the model on each group.
D. Use rdd.mapPartitions() to iterate through each store and apply the model logic in Python.

Antwort: B

Begründung:
groupBy().applyInPandas() enables grouped Pandas UDFs that receive each store's data as a Pandas DataFrame on the executors. This allows training and applying a separate scikit-learn model per store in parallel without collecting data to the driver, making it the correct and scalable approach for large Spark DataFrames.

134. Frage
Why is Delta Lake time travel useful in ML pipelines?

A. Reproducible training datasets
B. Smaller datasets
C. Model tuning
D. Faster model inference

Antwort: A

Begründung:
Time travel allows training on exact historical datasets.

135. Frage
A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or computing predictions using that model. They decide to wrap this preprocessing in a custom model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as a pyfunc model. Which statement is a benefit of this approach when loading the logged pyfunc model for downstream deployment?

A. There is no longer a need for pipeline-like machine learning objects
B. This approach has no impact when loading the logged Pvfunc model for downstream deployment
C. The same preprocessing logic will automatically be applied when calling fit
D. The same preprocessing logic will automatically be applied when calling predict
E. The pvfunc model can be used to deploy models in a parallelizable fashion

Antwort: A

136. Frage
Which evaluator is used for binary classification?

A. MulticlassEvaluator
B. ClusteringEvaluator
C. RegressionEvaluator
D. BinaryClassificationEvaluator

Antwort: D

Begründung:
BinaryClassificationEvaluator computes metrics like:
AUC
areaUnderPR.

137. Frage
A machine learning engineer would like to compute predictions on inference data as it becomes available through the pipeline in microbatches. The predictions should be stored in a table for query later. Which deployment strategy can the engineer use?

A. Streaming
B. Batch
C. Real-time
D. Edge/on-device

Antwort: A

Begründung:
A streaming deployment strategy processes incoming data continuously or in microbatches, allowing predictions to be computed as new data arrives. The results can then be written to a table for later querying. This approach balances near-real-time inference with manageable resource use, making it ideal for scenarios where data flows steadily through a pipeline.

138. Frage
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