Databricks-Machine-Learning-Professional Zertifizierung, Databricks-Machine-Learning-Professional Buch

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Databricks Databricks-Machine-Learning-Professional Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Machine Learning Professional Exam
Exam Number:	Databricks-Machine-Learning-Professional
Real Exam Qty:	59
Exam Duration:	120 minutes
Passing Score:	Not publicly disclosed (approximately 70%)
Certificate Validity Period:	2 years
Exam Price:	$200 USD
Available Languages:	English
Related Certifications:	Databricks Certified Machine Learning Associate
Exam Format:	Multi-select, Multiple choice
Recommended Training:	Advanced Machine Learning Operations Machine Learning at Scale
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:	Online proctored or in-person test center
Pre Condition:	No mandatory prerequisites; recommended: 6+ months hands-on experience with Databricks ML, SparkML, MLflow, and Python
Official Syllabus URL:	https://www.databricks.com/learn/certification/machine-learning-professional

>> Databricks-Machine-Learning-Professional Zertifizierung <<

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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 the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Thema 3	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Thema 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
Thema 5	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
Thema 6	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Thema 7	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Thema 8	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Thema 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
Thema 10	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 11	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift

Databricks Certified Machine Learning Professional Databricks-Machine-Learning-Professional Prüfungsfragen mit Lösungen (Q43-Q48):

43. 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. The same preprocessing logic will automatically be applied when calling predict
C. This approach has no impact when loading the logged Pvfunc model for downstream deployment
D. The pvfunc model can be used to deploy models in a parallelizable fashion
E. The same preprocessing logic will automatically be applied when calling fit

Antwort: A

44. Frage
A machine learning engineering team has written predictions computed in a batch job to a Delta table for querying. However, the team has noticed that the querying is running slowly. The team has already tuned the size of the data files. Upon investigating, the team has concluded that the rows meeting the query condition are sparsely located throughout each of the data files.
Based on the scenario, which of the following optimization techniques could speed up the query by colocating similar records while considering values in multiple columns?

A. Data skipping
B. Tuning the file size
C. Bin-packing
D. Write as a Parquet file
E. Z-Ordering

Antwort: B

45. Frage
A machine learning engineer has developed a random forest model using scikit-learn, logged the model using MLflow as random_forest_model, and stored its run ID in the run_id Python variable.
They now want to deploy that model by performing batch inference on a Spark DataFrame spark_df. Which of the following code blocks can they use to create a function called predict that they can use to complete the task?

A. It is not possible to deploy a scikit-learn model on a Spark DataFrame.
B.
C.
D.
E.

Antwort: E

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

Antwort: A

Begründung:
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.

47. Frage
A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. The pipeline needs to automatically refresh its model each time it runs. The project is attached to the existing model model_name in the MLflow Model Registry.
They are using the following code block as part of their solution:

Which statement describes the impact of the registered_model_name=model_name parameter and argument given that model_name already exists in the MLflow Model Registry?

A. It registers the new version of the model_name model in the MLflow Model Registry.
B. It identifies the name of the logged model in the MLflow Experiment.
C. It avoids the need to specify the model name in the subsequent required call to mflow.register_model.
D. It registers a new model called model_name in the MLflow Model Registry.

Antwort: A

Begründung:
When using registered_model_name=model_name in mlflow.spark.log_model(), MLflow automatically registers the logged model under the specified model name. If that model name already exists in the MLflow Model Registry, MLflow creates a new version of that existing registered model rather than a new model entry. This enables automatic versioning and continuous model refresh with each pipeline run.

48. Frage
......

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