認證考試內容年年調整，2026 年的 Databricks-Machine-Learning-Professional 考試也不例外。KaoGuTi 持續追蹤 Databricks Certified Machine Learning Professional 的考綱變化，193 道題目隨之更新，購買後還享有 365 天免費更新。

Databricks Databricks-Machine-Learning-Professional 考試概覽:

認證廠商:	Databricks
考試名稱:	Databricks 認證機器學習專業人員考試
考試代碼:	Databricks-Machine-Learning-Professional
考試時間:	120 分鐘
證照有效期限:	2 年
考試費用:	200 美元
相關認證:	Databricks Certified Machine Learning Associate
實際考試題數:	59
及格分數:	未公開（約為 70%）
支援語言:	English
考試形式:	單選題, 多選題
推薦課程:	進階機器學習營運 大規模機器學習
考試報名:	Databricks 認證入口網站
範例考題:
考試方式:	線上監考或實體考場應試
必備條件:	無強制先修條件；建議具備 6 個月以上 Databricks ML、SparkML、MLflow 及 Python 的實務操作經驗
官方大綱網址:	https://www.databricks.com/learn/certification/machine-learning-professional

Databricks Databricks-Machine-Learning-Professional 考試大綱主題:

Databricks Certified Machine Learning Professional 考試常見問題解答

最新的 ML Data Scientist Databricks-Machine-Learning-Professional 免費考試真題：

問題 #1

A Machine Learning Engineer has deployed a fraud detection model in Databricks Model Serving to detect fraudulent transactions. The engineer wants to compare the model's predictions with the actual fraud classifications from the Fraud Ops team to monitor model performance. The Fraud Ops team uses a unique transaction_id to investigate fraudulent activity and persist their findings to a fraud_findings table. The engineer enabled inference tables on the endpoint, but they are not sure how to map the models' predictions to the Fraud Ops team's classifications. How can the engineer uniquely join the models' prediction to the fraud_findings table with the fewest code changes?

A. Modify the model to include an additional input: transaction_id. Log, register and deploy the new model. In the model serving request body, add transaction_id as an additional input feature. Join the inference table with the fraud_findings table using transaction_id as the join key.
B. Populate the client_request_id field with the transaction_id in the model serving request body.
Join the inference table with the fraud_findings table using client_request_id (which contains the transaction_id) as the join key.
C. Store databricks_request_id returned from each model serving request and persist it to the fraud_findings table. Join the inference table with the fraud_findings table using databricks_request_id as the join key.
D. Join the inference table with the fraud_findings table using timestamp_ms as the join key.

問題 #2

A Machine Learning Engineer wants to implement MLOps. They currently have three environments: dev, stage, and prod. They have many private PyPI packages that they use to train their models, and they are concerned that their environments will not be consistent between dev, stage, and prod. The engineer needs to follow enterprise scaling and CI/CD best practices to ensure that their environments are consistent. Which approach will do this?

A. Define dependencies for each task in their job(s) running the ML pipeline using Databricks Asset Bundles or Terraform.
B. Use the same DBR across environments.
C. Define an all purpose cluster with the private PyPI packages installed, and use the same cluster config across environments.
D. Use the same DBR for ML runtime across environments and %pip install any additional packages at the top of the notebook.

問題 #3

Which of the following describes the purpose of the context parameter in the predict method of Python models for MLflow?

A. The context parameter allows the user to specify which version of the registered MLflow Model should be used based on the given application's current scenario
B. The context parameter allows the user to provide the model with completely custom if-else logic for the given application's current scenario
C. The context parameter allows the user to include relevant details of the business case to allow downstream users to understand the purpose of the model
D. The context parameter allows the user to provide the model access to objects like preprocessing models or custom configuration files
E. The context parameter allows the user to document the performance of a model after it has been deployed

問題 #4

Which of the following lists all of the model stages are available in the MLflow Model Registry?

A. Development. Staging. Production. Archived
B. Staging. Production. Archived
C. Development. Staging. Production
D. None. Staging. Production
E. None. Staging. Production. Archived

問題 #5

A Data Scientist is training a complex gradient-boosted model for fraud detection. The model uses dynamic threshold tuning during training and generates custom visualizations of feature drift. To ensure reproducibility and collaboration, they need to programmatically track:
- Custom metrics (e g., adjusted_f1 for threshold variations)
- Hyperparameters from nested configuration files
- Drift visualization plots as PDFs
Which approach implements this tracking in MLflow?

A. Enable mlflow.autolog() at the beginning of the code and rely on it to automatically detect and log custom metrics, parameters, and visualization files.
B. Store hyperparameters in a YAML file, save metrics to a CSV, and use mlflow.log_artifact() to upload both to the tracking server as artifacts.
C. Use mlflow.log_metric("adjusted_f1", value),mlflow.log_params(nested_config), and mlflow.log_artifact("drift_plot.pdf") within an active MLflow run context, while enabling mlflow.autolog() to capture model-specific metadata automatically.
D. Write all custom metrics and parameters to stdout during training and configure MLflow's tracking server to scrape these outputs.

問題與答案：