Databricks Databricks-Machine-Learning-Professional Lerntipps - Databricks-Machine-Learning-Professional Exam Fragen

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

Section	Weight	Objectives
Model Deployment	12%	- Custom model serving - Deployment strategies - Model rollout and version management
Model Development	44%	- Scalable ML pipelines with SparkML - Feature Store and automated feature pipelines - Distributed training and hyperparameter tuning - Advanced MLflow usage
ML Ops	44%	- Model monitoring and drift detection with Lakehouse Monitoring - Testing and validation strategies - Environment management with Databricks Asset Bundles - Automated retraining workflows

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Databricks Certified Machine Learning Professional Databricks-Machine-Learning-Professional Prüfungsfragen mit Lösungen (Q63-Q68):

63. 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. Create a custom pyfunc MLflow model which processes results of the Kafka stream for on demand feature calculation.
B. Run a triggered workflow to ingest the Kafka data to a dataframe that they can use with their existing write_table() command.
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. 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.

Antwort: D

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.

64. Frage
Which tool can assist in real-time deployments by packaging software with its own application, tools, and libraries?

A. Flask
B. Structured Streaming
C. Docker
D. Click

Antwort: C

Begründung:
Docker is a containerization tool that packages software along with its dependencies, tools, and libraries into a single container. This ensures consistency across environments and is highly useful for real-time deployments, enabling scalable and portable model serving.

65. Frage
What does an Estimator do in Spark ML?

A. Evaluates model performance
B. Stores features
C. Trains a model using data
D. Transforms a dataset

Antwort: C

Begründung:
An Estimator learns from data and produces a Transformer.

66. Frage
A machine learning engineer has developed a model and registered it using the FeatureStoreClient fs. The model has model URI model_uri. The engineer now needs to perform batch inference on the training set logged with the model, but a few of the feature values in the column spend have since been updated and arc present in the customer-level Spark DataFrame spark_df. The customer_id column is the primary key of spark_df and the training set used when training and logging the model. Which code block can be used to compute predictions for the training set while overwriting its old spend values with the new spend values from spark_df?

A. df = fs.get_updated_features(spark_df)
fs.score_batch(model_uri, df)
B. fs.score_batch(model_uri, spark_df)
C. df = fs.get_updated_feature(spark_df, model=uri)
fs.score_batch(model_uri, df)
D. fs.score_model(model_uri, spark_df)

Antwort: A

Begründung:
To perform batch inference while incorporating updated feature values (like spend) from a DataFrame (spark_df), the correct approach is to use fs.get_updated_features(spark_df) to refresh the relevant features based on the primary key (customer_id), then score the model using fs.score_batch(...). This ensures predictions are made with the latest data.

67. Frage
A data scientist has developed a model model and computed the RMSE of the model on the test set. They have assigned this value to the variable rmse. They now want to manually store the RMSE value with the MLflow run.
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. There is no way to store values like this.
B. log_metric
C. log_param
D. log_model
E. log_artifact

Antwort: E

68. Frage
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