Databricks-Machine-Learning-Professional Prüfungsvorbereitung - Databricks-Machine-Learning-Professional Examengine

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Databricks Databricks-Machine-Learning-Professional Prüfungsplan:

Thema	Einzelheiten
Thema 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
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	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Thema 4	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 5	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Thema 6	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Thema 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

>> Databricks-Machine-Learning-Professional Prüfungsvorbereitung <<

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Databricks Certified Machine Learning Professional Databricks-Machine-Learning-Professional Prüfungsfragen mit Lösungen (Q24-Q29):

24. Frage
Which of the following statements about built-in library-specific MLflow Model flavors is true?

A. Built-in library-specific flavors allow models to be used with any library/span>
B. Built-in library-specific flavors are required for model signature use
C. Built-in library-specific flavors can only be used for logging models
D. Built-in library-specific flavors allow models to be exported as library objects

Antwort: D

Begründung:
Built-in library-specific MLflow model flavors (e.g., mlflow.sklearn, mlflow.xgboost) allow models to be exported and later loaded as native library objects, enabling seamless reuse with the original libraries for inference or further training.

25. Frage
A machine learning engineer is converting a Hyperopt-based hyperparameter tuning process from manual MLflow logging to MLflow Autologging. They notice that not all details and objects are automatically logged, and they will need to manually log some things. Which of the following will need to be manually logged when performing nested runs with Hyperopt and MLflow Autologging?

A. Evaluation metrics
B. Trial status
C. Hyperparameter values
D. Best trial evaluation metric
E. Trial models

Antwort: D

Begründung:
When using MLflow Autologging with Hyperopt and nested runs, the best trial evaluation metric is not automatically logged and must be logged manually. Autologging captures trial-level details like hyperparameters and evaluation metrics, but summarizing and logging the overall best trial's result is a manual responsibility of the engineer.

26. Frage
A Data Scientist is building a machine learning pipeline to classify raw text using a Logistic Regression model in Spark using Spark MLlib's Pipelines. This pipeline has three stages: the Tokenizer (to split the raw text in tokens), a HashingTF (to transform tokens into hashes) and the Logistic Regression itself (to perform the classification of texts). The Spark DataFrame with the training data is called trainingDF and the one with the test data is called testDF.
In order to do this, they use the following incomplete piece of code:

Which option correctly states:
(i) The complete command to run model training;
(ii) The complete command to execute the prediction on test data;
(iii) The object type of the model object returned by the model
training command.

Antwort: A

Begründung:
In Spark MLlib, a Pipeline is trained using the fit method, which applies all estimator stages to the training DataFrame and returns a PipelineModel. Predictions are generated by calling transform on the fitted PipelineModel, which applies the full pipeline (tokenization, feature hashing, and logistic regression) to the test DataFrame.

27. Frage
A Machine Learning Engineer has previously built a feature table for model training and inference using a batch mode approach:

They have been informed that they now require these features to be available in "real-time", with latency on the order of a minute. Their manager has informed them there is now a Kafka stream from which they can stream live data, and they need to have this ingested and available for low- latency feature lookups.
Which change to their existing code will achieve this?

A. Run a triggered workflow to ingest the Kafka data to a dataframe that they can use with their existing write_table() command.
B. Change the incoming_df to be a dataframe based on a readStream() from the Kafka source and publish the table as an online table with the streaming option set to True.
C. Change the incoming_df to be a dataframe based on a readStream() from the kafka source, the write_table() method will provide a low-latency lookup on this data.
D. Create a custom pyfunc MLflow model which processes results of the Kafka stream for on demand feature calculation.

Antwort: B

Begründung:
To achieve real-time availability with minute-level latency, the feature data must be continuously ingested from Kafka and published to an online table. Using a streaming DataFrame created with readStream from the Kafka source and enabling the online table with streaming allows incremental updates to be synchronized to the online store, supporting low-latency feature lookups for real-time inference.

28. Frage
Which of the following tools can assist in real-time deployments by packaging software with its own application, tools, and libraries?

A. Containers
B. REST APIs
C. Autoscaling clusters
D. None of these tools
E. Cloud-based compute

Antwort: E

29. Frage
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