Databricks-Machine-Learning-Professional試験関連情報、Databricks-Machine-Learning-Professionalテスト内容

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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	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
トピック 2	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
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 that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
トピック 5	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
トピック 6	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
トピック 7	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur

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Databricks-Machine-Learning-Professionalテスト内容、Databricks-Machine-Learning-Professional資格復習テキスト

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Databricks Certified Machine Learning Professional 認定 Databricks-Machine-Learning-Professional 試験問題 (Q190-Q195):

質問 # 190
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, the write_table() method will provide a low-latency lookup on this data.
C. 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.
D. Create a custom pyfunc MLflow model which processes results of the Kafka stream for on demand feature calculation.

正解：C

解説：
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.

質問 # 191
A Machine Learning Engineer is tasked with building an automated daily pipeline that updates a customer_features table in Unity Catalog. They have implemented a function, compute_customer_features, that returns a DataFrame with a unique customer_id as the primary key and want to ensure the latest feature values are merged into the table each day. Which code snippet implements this requirement?

正解：B

解説：
Using write_table with mode set to merge updates existing rows and inserts new ones based on the table's primary key. This ensures that the latest feature values for each customer_id are merged into the existing feature table each day without overwriting the entire table, which is the correct and scalable approach for maintaining up-to-date customer features in Unity Catalog.

質問 # 192
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/Production/invocations
B. https://<databricks-instance>/model/recommender/stage-production/invocations
C. The version number of the model version in Production is necessary to complete this task.
D. https://<databricks-instance>/model-serving/recommender/stage-production/invocations
E. https://<databricks-instance>/model/recommender/Production/invocations

正解：C

質問 # 193
A Machine Learning Engineer is implementing integration tests for an ML pipeline in Databricks.
The current integration test runs the complete workflow but takes four hours to execute due to large dataset processing and extensive model training. They need to select an approach that will be the most effective for optimizing integration test execution while maintaining test reliability. The approach should also be based on MLOps best practices. Which approach will do this?

A. Use small subsets of production-like data and reduce training iterations while maintaining the same pipeline structure and validation checkpoints in a staging environment that closely matches production.
B. Replace integration tests with unit tests for each pipeline component to reduce execution time and focus on individual component validation in a staging environment that closely matches production.
C. Skip the model training phase entirely and only test data transformations and API endpoints using mock model predictions.
D. Run integration tests only in the production environment using full datasets to ensure complete accuracy of the testing process.

正解：A

解説：
Using smaller, production-like datasets and reduced training iterations preserves the full pipeline structure while significantly reducing execution time. This aligns with MLOps best practices by maintaining high-fidelity integration testing in a staging environment that mirrors production behavior, without the cost and delay of running full-scale training workloads.

質問 # 194
Which MLflow component is used to log parameters, metrics, and artifacts during model training?

A. MLflow Projects
B. MLflow Tracking
C. Model Registry
D. MLflow Models

正解：B

解説：
MLflow Tracking records experiment runs including:
parameters
metrics
artifacts (models, plots, etc.)

質問 # 195
......

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