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

Section	Weight	Objectives
Topic 1: Model Development	44%	- Feature Store and automated feature pipelines - Distributed training and hyperparameter tuning - Scalable ML pipelines with SparkML - Advanced MLflow usage
Topic 2: ML Ops	44%	- Model monitoring and drift detection with Lakehouse Monitoring - Environment management with Databricks Asset Bundles - Automated retraining workflows - Testing and validation strategies
Topic 3: Model Deployment	12%	- Deployment strategies - Model rollout and version management - Custom model serving

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Databricks Certified Machine Learning Professional 認定 Databricks-Machine-Learning-Professional 試験問題 (Q22-Q27):

質問 # 22
A machine learning engineer is using the following code block as part of a batch deployment pipeline:

Which of the following changes needs to be made so this code block will work when the inference table is a stream source?

A. Replace formatfdelta") with format("stream")
B. Replace spark.read with spark.readStream
C. Replace schema(schema) with option("maxFilesPerTriqqer", 1}
D. Replace predict with a stream-friendly prediction function
E. Replace "inference" with the path to the location of the Delta table

正解：C

質問 # 23
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")

正解：A

解説：
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.

質問 # 24
A machine learning engineer wants to log feature importance data from a CSV file at path importance_path with an MLflow run for model model. Which code block will accomplish this task inside of an existing MLflow run block?

A.
B. mlflow.log_artifact(importance_path, "feature-importance.csv")
C. None of these code blocks tan accomplish the task.
D. mlflow.log_data(importance_path, "feature-importance.csv")
E.

正解：A

質問 # 25
A Data Scientist needs to analyze drift detection results from Databricks Lakehouse Monitoring.
The system has generated both profile metrics and drift metrics tables. The scientist needs to identify baseline drift in numerical features by comparing current data against a baseline from 6 months ago. Which combination of table columns and values indicates baseline drift in a numerical feature?

A. log_type = "BASELINE" in profile metrics table with population_stability_index > 0.2 in drift metrics table.
B. drift_type = "BASELINE", chi_squared_test.p_value < 0.05, and js_distance > 0.2.
C. drift_type = "BASELINE", ks_test.p_value < 0.05, and wasserstein_distance > 0.1.
D. window_cmp pointing to baseline time window and tv_distance > 0.5 with any drift_type value.

正解：C

解説：
Baseline drift is identified in the drift metrics table by setting drift_type to BASELINE, and for numerical features the drift statistics are the KS test (ks_test with a p-value indicating a statistically significant distribution change) and a numeric distance metric such as wasserstein_distance.

質問 # 26
A machine learning engineer wants to programmatically create a new Databricks Job whose schedule depends on the result of some automated tests in a machine learning pipeline. Which Databricks tool can be used to programmatically create the Job?

A. AutoML APIs
B. Jobs cannot be created programmatically
C. MLflow Client
D. Databricks REST APIs
E. MLflow APIs

正解：D

質問 # 27
......

この競争の激しい社会では、良い仕事をするためには、自分の能力を向上させ、可能性を常に探求し、関連するDatabricks-Machine-Learning-Professional認定を取得することが最善の方法です。しかし、私たちの専門的な能力は、試験を解読するのが難しいことであり、試験に関連するDatabricks-Machine-Learning-Professional準備質問が非常に多いため、試験に必要なすべてのキーポイントを体系化することは不可能です。

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