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Databricks Databricks-Machine-Learning-Professional 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
トピック 2	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
トピック 3	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
トピック 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
トピック 5	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
トピック 6	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

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Databricks Certified Machine Learning Professional 認定 Databricks-Machine-Learning-Professional 試験問題 (Q54-Q59):

質問 # 54
A machine learning engineer is converting a Hyperopt-based hyperparameter tuning process from manual MLflow logging to MLflow Autologging. They are trying to determine how to manage nested Hyperopt runs with MLflow Autologging.
Which of the following approaches will create a single parent run for the process and a child run for each unique combination of hyperparameter values when using Hyperopt and MLflow Autologging?

A. There is no way to accomplish nested runs with MLflow Autoloqqinq and Hyperopt
B. Starting a manual child run within the objective function
C. MLflow Autoloqqinq will automatically accomplish this task with Hyperopt
D. Ensuring that a built-in model flavor is used for the model logging
E. Startinq a manual parent run before calling fmin

正解：E

質問 # 55
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 customer-level Spark DataFrame spark_df, but it is missing a few of the static features that were used when training the model. The customer_id column is the primary key of spark_df and the training set used when training and logging the model.
Which of the following code blocks can be used to compute predictions for spark_df when the missing feature values can be found in the Feature Store by searching for features by customer_id?

A. df = fs.get_missing_features(spark_df)
fs.score_batch(model_uri, df)
B. fs.score_batch(model_uri, spark_df)
C. df = fs.get_missing_features(spark_df, model_uri)
fs.score_model(model_uri, df)
D. fs.score_model(model_uri, spark_df)
E. df = fs.get_missing_features(spark_df, model_uri)
fs.score_batch(model_uri, df)

正解：B

質問 # 56
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. Real-time
C. Edge/on-device
D. Batch

正解：A

解説：
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.

質問 # 57
A machine learning engineer has deployed a model recommender using MLflow Model Serving.
They now want to query the version of that model that is in the Production stage of the MLflow Model Registry. Which of the following model URIs can be used to query the described model version?

A. https://<databricks-instance>/model-serving/recommender/stage-production/invocations
B. The version number of the model version in Production is necessary to complete this task.
C. https://<databricks-instance>/model-serving/recommender/Production/invocations
D. https://<databricks-instance>/model/recommender/Production/invocations
E. https://<databricks-instance>/model/recommender/stage-production/invocations

正解：B

質問 # 58
Which of the following operations in Feature Store Client fs can be used to return a Spark DataFrame of a data set associated with a Feature Store table?

A. fs.create_table
B. There is no way to accomplish this task with fs
C. fs.write_table
D. fs.read_table
E. fs.get_table

正解：A

質問 # 59
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